Abdelhak M. Zoubir

Abdelhak M. Zoubir

Technische Universität Darmstadt

H-index: 50

Europe-Germany

Abdelhak M. Zoubir Information

University

Technische Universität Darmstadt

Position

Professor of Signal Processing

Citations(all)

10797

Citations(since 2020)

4311

Cited By

8099

hIndex(all)

50

hIndex(since 2020)

34

i10Index(all)

230

i10Index(since 2020)

99

Email

University Profile Page

Technische Universität Darmstadt

Abdelhak M. Zoubir Skills & Research Interests

Signal Processing

Top articles of Abdelhak M. Zoubir

Joint design of transmit precoding and antenna selection for multi-user multi-target MIMO DFRC

Dual-function radar and communications (DFRC) systems based on multiple-input multiple-output (MIMO) arrays have received considerable attention in recent years due to their excellent ability to alleviate spectrum congestion. The MIMO DFRC systems enable high-resolution detection of multiple targets while communicating with multiple users simultaneously. However, MIMO arrays require numerous radio frequency (RF) units and suffer a strong mutual coupling among antennas, resulting in significant system overhead and performance degradation, respectively. In light of this, this paper investigates the joint optimization of transmit precoding and antenna selection for MIMO DFRC systems, aiming to improve the angular ambiguity function with a guaranteed communication quality of service (QoS) using a small number of antennas. To address the resultant non-convex optimization problem, both the indirect and …

Authors

Jing Xu,Xiangrong Wang,Xuan Zhang,Xianghua Wang,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2024/5/1

Corrections to “Semiparametric CRB and Slepian-Bangs Formulas for Complex Elliptically Symmetric Distributions”

Errors in [1] are corrected below. 1. In Eq. (17), should be . Specifically, the correct version of Eq. (17) is: \begin{align*} \mathbf{s}_{\boldsymbol{\phi}_{0}}\triangleq\nabla_{\boldsymbol{\phi}}\ln p_{Z}(\mathbf{z};\boldsymbol{\phi}_{0},h_{0})=[\mathbf{s}^{T}_{\boldsymbol{\mu}_{0}},\mathbf{s}^{T}_{\boldsymbol{\mu}^{*}_ {0}},\mathbf{s}^{T}_{\mathrm{vec}(\boldsymbol{\Sigma}_{0})}]^{T}.\tag{17} \end{align*} 2. In the first line after Eq. (18), should be . 3. A minus “-” is missing in front of the right-hand side of Eq. (25). The correct equation is: \begin{align*} &\bar{\mathbf{s}}_{\mathrm{vec}(\boldsymbol{\Sigma}_{0})}= _{d}-\mathcal{Q}\psi_{0}(\mathcal{Q})\times \\ &\quad \times(\boldsymbol{\Sigma}_{0}^{-*/2}\otimes\boldsymbol{\Sigma}_{0}^{-1/2} \mathrm{vec}(\mathbf{u}\mathbf{u}^{H})-N^{-1}\mathrm{vec}(\boldsymbol{\Sigma}_{0}^{-1})) . \tag{25} \end{align*} 4. A minus “-” is missing in front of in …

Authors

Stefano Fortunati,Fulvio Gini,Maria S Greco,Abdelhak M Zoubir,Muralidhar Rangaswamy

Journal

IEEE Transactions on Signal Processing

Published Date

2024/1/31

Deep Unrolling Network for SAR Image Despeckling

Synthetic aperture radar (SAR) images are inherently affected by speckle noise. Deep learning-based methods have shown good potential in image denoising task. Most deep learning methods for denoising focus on additive Gaussian noise removal. However, SAR images are usually contaminated by non-Gaussian multiplicative speckle noise. In this paper, we propose a novel deep unrolling network named SAR-DURNet to deal with the SAR image despeckling problem. We establish optimization problem of speckle noise removal by using the priori of noise distribution, which can be sovled by half-quadratic splitting (HQS) method with iterative steps. We unroll the iterative process into a trainable deep unrolling network(SAR-DURNet). The parameters of the SAR-DURNet are trained end-to-end with simulated SAR image dataset. Experimental results on simulated test data and real SAR data show that the …

Authors

Che Chen,Lin Chen,Xue Jiang,Xingzhao Liu,Abdelhak M Zoubir

Published Date

2024/4/14

Emergency response person localization and vital sign estimation using a semi-autonomous robot mounted SFCW radar

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is …

Authors

Christian A Schroth,Christian Eckrich,Ibrahim Kakouche,Stefan Fabian,Oskar von Stryk,Abdelhak M Zoubir,Michael Muma

Journal

IEEE Transactions on Biomedical Engineering

Published Date

2024/1/8

Transmit Waveform Design for Integrated Wideband MIMO Radar and Bi-Directional Communications

In this work, we propose an integrated MIMO system which performs target tracking and downlink communications, as well as receiving uplink signals from other communication nodes to achieve bi-directional communications. The key to such a system is the constrained joint transmit waveform design, which constitutes the main contribution of this work. Per the practical requirement of waveform design, we introduce a new realistic power constraint, referred to as peak-to-valley-power-ratio control with energy budget (PVRC-EB), to remedy the shortcoming of the traditional energy constraint in terms of the transmit elemental power uniformity control. To realize the integrated function of radar and communication, we formulate two joint transmit waveform designs, which achieve a tradeoff between radar function and downlink communication subject to spectral or spatio-spectral energy constraints for accommodating …

Authors

Xuan Zhang,Xiangrong Wang,HC So,Abdelhak M Zoubir,J Andrew Zhang,Y Jay Guo

Journal

IEEE Transactions on Vehicular Technology

Published Date

2024/4/12

Depletion depth studies with the MALTA2 sensor, a depleted monolithic active pixel sensor

MALTA2 is a depleted monolithic active pixel sensor (DMAPS) developed in the Tower 180 nm CMOS imaging process. Monolithic CMOS sensors offer advantages over current hybrid imaging sensors both in terms of increased tracking performance due to lower material budget but also in terms of ease of integration and construction costs due to the monolithic design. Current research and development efforts are aimed towards radiation-hard designs up to 100 Mrad in Total Ionizing Dose and 3× 1 0 15 1 MeV n e q/c m 2 in Non-Ionizing Energy Loss. One important property of a sensor’s radiation hardness is the depletion depth at which efficient charge collection is achieved via drift movement. Grazing angle test-beam data was taken during the 2023 SPS CERN test beam with the MALTA telescope and Edge Transient Current Technique studies were performed at DESY in order to develop a quantitative study of …

Authors

DV Berlea,P Allport,I Asensi Tortajada,P Behera,D Bortoletto,C Buttar,F Dachs,V Dao,G Dash,D Dobrijevic,L Fasselt,L Flores Sanz de Acedo,M Gazi,L Gonella,V Gonzalez,G Gustavino,P Jana,M LeBlanc,L Li,H Pernegger,P Riedler,W Snoeys,CA Solans Sanchez,T Suligoj,M van Rijnbach,M Vázquez Nuñez,A Vijay,J Weick,S Worm,AM Zoubir

Published Date

2024/3/19

Radiation hardness of MALTA2 monolithic CMOS imaging sensors on Czochralski substrates

MALTA2 is the latest full-scale prototype of the MALTA family of Depleted Monolithic Active Pixel Sensors (DMAPS) produced in Tower Semiconductor 180 nm CMOS sensor imaging technology. In order to comply with the requirements of high energy physics (HEP) experiments, various process modifications and front-end changes have been implemented to achieve low power consumption, reduce random telegraph signal (RTS) noise, and optimise the charge collection geometry. Compared to its predecessors, MALTA2 targets the use of a high-resistivity, thick Czochralski (Cz) substrates in order to demonstrate radiation hardness in terms of detection efficiency and timing resolution up to 3 10 1 MeV with backside metallisation to achieve good propagation of the bias voltage. This manuscript shows the results that were obtained with non-irradiated and irradiated MALTA2 samples on Cz substrates …

Authors

Milou van Rijnbach,Dumitru Vlad Berlea,Valerio Dao,Martin Gaži,Phil Allport,Ignacio Asensi Tortajada,Prafulla Behera,Daniela Bortoletto,Craig Buttar,Florian Dachs,Ganapati Dash,Dominik Dobrijević,Lucian Fasselt,Leyre Flores Sanz de Acedo,Andrea Gabrielli,Laura Gonella,Vicente González,Giuliano Gustavino,Pranati Jana,Long Li,Heinz Pernegger,Francesco Piro,Petra Riedler,Heidi Sandaker,Carlos Solans Sánchez,Walter Snoeys,Tomislav Suligoj,Marcos Vázquez Núñez,Anusree Vijay,Julian Weick,Steven Worm,Abdelhak M Zoubir

Journal

The European Physical Journal C

Published Date

2024/3/10

Asymptotically optimal procedures for sequential joint detection and estimation

We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is …

Authors

Dominik Reinhard,Michael Fauß,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2024/2/5

Robust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation

Distributed networks are widely used in industrial and consumer applications. As the communication capabilities of such networks are usually limited, it is important to develop algorithms which are capable of handling the vast amount of data processing locally and only communicate some aggregated value. Additionally, these algorithms have to be robust against outliers in the data, as well as faulty or malicious nodes. Thus, we propose a robust distributed expectation maximization (EM) algorithm based on Real Elliptically Symmetric (RES) distributions, which is highly adaptive to outliers and moreover is combined with a robust data aggregation step which provides robustness against malicious nodes. In the simulations, the proposed algorithm shows its effectiveness over non-robust methods.

Authors

Christian A Schroth,Stefan Vlaski,Abdelhak M Zoubir

Published Date

2023/6/4

Convergence analysis of consensus-ADMM for general QCQP

We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs. We prove that the augmented Lagrangian function value is monotonically non-increasing as long as the augmented Lagrangian parameter is chosen to be sufficiently large. Simulation results show that the augmented Lagrangian function is bounded from below when the matrix in the quadratic term of the objective function is positive definite. In such a case, the consensus-ADMM is convergent.

Authors

Huiping Huang,Hing Cheung So,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2023/7/1

arXiv: Radiation Hardness of MALTA2 Monolithic CMOS Sensors on Czochralski Substrates

MALTA2 is the latest full-scale prototype of the MALTA family of Depleted Monolithic Active Pixel Sensors (DMAPS) produced in Tower Semiconductor 180 nm CMOS technology. In order to comply with the requirements of High Energy Physics (HEP) experiments, various process modifications and front-end changes have been implemented to achieve low power consumption, reduce Random Telegraph Signal (RTS) noise, and optimise the charge collection geometry. Compared to its predecessors, MALTA2 targets the use of a high-resistivity, thick Czochralski (Cz) substrates in order to demonstrate radiation hardness in terms of detection efficiency and timing resolution up to 3E15 1 MeV neq/cm2 with backside metallisation to achieve good propagation of the bias voltage. This manuscript shows the results that were obtained with non-irradiated and irradiated MALTA2 samples on Cz substrates from the CERN SPS test beam campaign from 2021-2023 using the MALTA telescope.

Authors

Milou van Rijnbach,Craig Buttar,Steven Worm,Lucian Fasselt,Daniela Bortoletto,Anusree Vijay,Heidi Sandaker,Valerio Dao,Prafulla Behera,Phil Allport,Marcos Vázquez Núñez,Leyre Flores Sanz de Acedo,Giuliano Gustavino,Walter Snoeys,Tomislav Suligoj,Abdelhak M Zoubir,Dumitru Vlad Berlea,Heinz Pernegger,Ignacio Asensi Tortajada,Dominik Dobrijević,Julian Weick,Martin Gaži,Carlos Solans Sánchez,Vicente González,Petra Riedler,Ganapati Dash,Florian Dachs,Pranati Jana,Andrea Gabrielli

Published Date

2023/8/25

MALTA-Cz: A radiation hard full-size monolithic CMOS sensor with small electrodes on high-resistivity Czochralski substrate

Depleted Monolithic Active Pixel Sensor (DMAPS) sensors developed in the Tower Semiconductor 180 nm CMOS imaging process have been designed in the context of the ATLAS ITk upgrade Phase-II at the HL-LHC and for future collider experiments. The" MALTA-Czochralski (MALTA-Cz)" full size DMAPS sensor has been developed with the goal to demonstrate a radiation hard, thin CMOS sensor with high granularity, high hit-rate capability, fast response time and superior radiation tolerance. The design targets radiation hardness of> 10 15 (1 MeV) n eq/cm 2 and 100 Mrad TID. The sensor shall operate as tracking sensor with a spatial resolution of≈ 10 μm and be able to cope with hit rates in excess of 100 MHz/cm 2 at the LHC bunch crossing frequency of 40 MHz. The 512× 512 pixel sensor uses small collection electrodes (3.5 μm) to minimize capacitance. The small pixel size (36.4× 36.4 μm 2) provides high …

Authors

H Pernegger,P Allport,DV Berlea,A Birman,D Bortoletto,Craig Buttar,E Charbon,F Dachs,V Dao,H Denizli,D Dobrijevic,M Dyndal,A Fenigstein,L Flores Sanz de Acedo,P Freeman,A Gabrielli,M Gazi,L Gonella,V Gonzalez,G Gustavino,A Haim,T Kugathasan,M LeBlanc,M Munker,KY Oyulmaz,Francesco Piro,P Riedler,H Sandaker,EJ Schioppa,A Sharma,W Snoeys,C Solans Sanchez,T Suligoj,E Toledano,M van Rijnbach,M Vazquez Nunez,J Weick,S Worm,AM Zoubir

Journal

Journal of Instrumentation

Published Date

2023/9/13

The Heterogeneity-Intensified and Heterogeneity Ratio-Stratified Bootstrap (HiS-and HeRS-Boot) Oversampling to Boost a Detector Performance

We investigated two variations of the previously proposed heterogeneity-stratified bootstrap (HSBoot) oversampling method, namely the improved Heterogeneity-Stratified (HiS-) and Heterogeneity Ratio-Stratified (HeRS-) Boot, for balancing a data set by assigning higher resampling probabilities to sample points in less homogeneous regions. Our study focused on two detection cases: spoiled food and allergen. Results demonstrate the effectiveness and generalizability of our method across different sensors, highlighting its potential for real-world applications and positive impact on daily life.

Authors

Pertami J Kunz,Syrine ben Abid,Abdelhak M Zoubir

Published Date

2023/10/29

arXiv: MALTA-Cz: A radiation hard full-size monolithic CMOS sensor with small electrodes on high-resistivity Czochralski substrate

Abstract Depleted Monolithic Active Pixel Sensor (DMAPS) sensors developed in the Tower Semiconductor 180 nm CMOS imaging process have been designed in the context of the ATLAS ITk upgrade Phase-II at the HL-LHC and for future collider experiments. The" MALTA-Czochralski (MALTA-Cz)" full size DMAPS sensor has been developed with the goal to demonstrate a radiation hard, thin CMOS sensor with high granularity, high hit-rate capability, fast response time and superior radiation tolerance. The small pixel size ( m ) provides high spatial resolution. Its asynchronous readout architecture is designed for high hit-rates and fast time response in triggered and trigger-less detector applications. The readout architecture is designed to stream all hit data to the multi-channel output which allows an off-sensor trigger formation and the use of hit-time information for event tagging. The sensor manufacturing has been optimised through process adaptation and special implant designs to allow the manufacturing of small electrode DMAPS on thick high-resistivity p-type Czochralski substrate. The special processing ensures excellent charge collection and charge particle detection efficiency even after a high level of radiation. Furthermore the special implant design and use of a Czochralski substrate improves the sensor's time resolution. This paper presents a summary of sensor design optimisation through process and implant choices and TCAD simulation to model the signal response. Beam and laboratory test results on unirradiated and irradiated sensors have shown excellent detection efficiency after a dose of 1 MeV n /cm  …

Authors

H Pernegger,A Fenigstein,E Toledano,V Gonzalez,J Weick,P Riedler,F Dachs,C Solans Sanchez,T Suligoj,M LeBlanc,D Bortoletto,M Dyndal,M Vazquez Nunez,M van Rijnbach,A Birman,E Charbon,P Allport,A Haim,H Denizli,M Munker,M Gazi,AM Zoubir,H Sandaker,DV Berlea,A Gabrielli,C Buttar,S Worm,D Dobrijevic,P Freeman,T Kugathasan,W Snoeys,KY Oyulmaz,L Gonella,G Gustavino,V Dao,EJ Schioppa,F Piro,A Sharma,L de Acedo

Journal

JINST

Published Date

2023/1/10

Sparse array design for dual-function radar-communications system

The problem of sparse array design for dual-function radar-communications is investigated. Our goal is to design a sparse array which can simultaneously shape desired beam responses and serve multiple downlink users with the required signal-to-interference-plus-noise ratio levels. Besides, we also take into account the limitation of the radiated power by each antenna. The problem is formulated as a quadratically constrained quadratic program with a joint-sparsity-promoting regularization, which is NP-hard. The resulting problem is solved by the consensus alternating direction method of multipliers, which enjoys parallel implementation. Numerical simulations exhibit the effectiveness and superiority of the proposed method which leads to a more power-efficient solution.

Authors

Huiping Huang,Linlong Wu,Bhavani Shankar,Abdelhak M Zoubir

Journal

IEEE Communications Letters

Published Date

2023/1/2

Spatial Inference Using Censored Multiple Testing with Fdr Control

A wireless sensor network performs spatial inference on a physical phenomenon of interest. The areas in which this phenomenon exhibits interesting or anomalous behavior are identified whilst controlling false positives. We expand our previous work based on multiple hypothesis testing (MHT) and local false discovery rates to save energy and reduce spectrum use. The number of transmissions from sensors producing uninformative statistics are reduced by introducing censoring for MHT that imposes a communication rate constraint while maintaining the desired performance. Two novel methods are proposed. As our numerical experiments demonstrate, both approaches reduce the number of transmissions while maintaining false discovery rate control. In addition, one method allows to either define a fixed number of total transmissions or to trade the number of transmissions off against the achieved detection power.

Authors

Martin Gölz,Abdelhak M Zoubir,Visa Koivunen

Published Date

2023/6/4

Radiation Hardness of MALTA2 Monolithic CMOS Sensors on Czochralski Substrates

MALTA2 is the latest full-scale prototype of the MALTA family of Depleted Monolithic Active Pixel Sensors (DMAPS) produced in Tower Semiconductor 180 nm CMOS technology. In order to comply with the requirements of High Energy Physics (HEP) experiments, various process modifications and front-end changes have been implemented to achieve low power consumption, reduce Random Telegraph Signal (RTS) noise, and optimise the charge collection geometry. Compared to its predecessors, MALTA2 targets the use of a high-resistivity, thick Czochralski (Cz) substrates in order to demonstrate radiation hardness in terms of detection efficiency and timing resolution up to 3E15 1 MeV neq/cm2 with backside metallisation to achieve good propagation of the bias voltage. This manuscript shows the results that were obtained with non-irradiated and irradiated MALTA2 samples on Cz substrates from the CERN SPS test beam campaign from 2021-2023 using the MALTA telescope.

Authors

Milou van Rijnbach,Dumitru Vlad Berlea,Valerio Dao,Martin Gaži,Phil Allport,Ignacio Asensi Tortajada,Prafulla Behera,Daniela Bortoletto,Craig Buttar,Florian Dachs,Ganapati Dash,Dominik Dobrijević,Lucian Fasselt,Leyre Flores Sanz de Acedo,Andrea Gabrielli,Vicente González,Giuliano Gustavino,Pranati Jana,Heinz Pernegger,Petra Riedler,Heidi Sandaker,Carlos Solans Sánchez,Walter Snoeys,Tomislav Suligoj,Marcos Vázquez Núñez,Anusree Vijay,Julian Weick,Steven Worm,Abdelhak M Zoubir

Journal

arXiv preprint arXiv:2308.13231

Published Date

2023/8/25

Method and electronic device for recovering data using adaptive rank-one matrix completion

A method for performing data recovering operation by an electronic device is provided. The method includes: receiving, by a processor of the electronic device, object data, wherein the object data comprises an incomplete matrix; identifying, by the processor, a plurality of first entries (xi, j) of the incomplete matrix according to the object data; inputting, by the processor, the first entries (xi, j) and a preset maximum loop count (Kmax) into an executed analysis model using Bi-Branch Neural Network (BiBNN) Algorithm; and obtaining, by the processor, a plurality of second entries (mi, j) of a recovered complete matrix corresponding to the incomplete matrix from the analysis model, wherein values of the second entries are determined as original values of the first entries of the incomplete matrix, such that incorrect data in the incomplete matrix is recovered.

Authors

Xiao Peng LI,Hing Cheung SO,WANG Maolin

Published Date

2022/6/8

Radiation hardness of MALTA2, a monolithic active pixel sensor for tracking applications

MALTA is a depleted monolithic active pixel sensor (DMAPS) developed in the Tower Semiconductor 180-nm CMOS imaging process. Monolithic CMOS sensors offer advantages over current hybrid imaging sensors in terms of both increased tracking performance due to lower material budget and ease of integration and construction costs due to the integration of read-out and active sensor into one ASIC. Current research and development efforts are aimed toward radiation hard designs up to 100 Mrad in total ionizing dose (TID) and in nonionizing energy loss (NIEL). The design of the MALTA sensors was specifically chosen to achieve radiation hardness up to these requirements and satisfy current and future collider constraints. The current MALTA pixel architecture uses small electrodes which provide less noise, higher signal voltage, and a better power-to-performance ratio. To counteract …

Authors

DV Berlea,P Allport,I Asensi Tortajada,D Bortoletto,C Buttar,E Charbon,F Dachs,V Dao,H Denizili,D Dobrijevic,L Flores Sanz De Acedo,A Gabrielli,M Gazi,L Gonella,V Gonzalez,G Gustavino,M LeBlanc,KY Oyulmaz,H Pernegger,F Piro,P Riedler,M Van Rijnbach,H Sandaker,A Sharma,W Snoeys,CA Solans Sanchez,T Suligoj,M Vazquez Nunez,J Weick,S Worm,AM Zoubir

Journal

IEEE Transactions on Nuclear Science

Published Date

2023/9/11

Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers

To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity. However, the thresholding functions of these nonconvex regularizers may not have closed-form expressions and thus iterations are needed, which increases the computational loads. To solve this issue, we devise a framework to generate sparsity-inducing regularizers with closed-form thresholding functions. These regularizers are applied to low-tubal-rank tensor completion, and efficient algorithms based on the alternating direction method of multipliers are developed. Furthermore, convergence of our methods is analyzed and it is proved that the generated sequences are bounded and any limit point is a stationary point. Experimental results using synthetic and real-world datasets show that the proposed algorithms outperform the state-of-the-art methods in terms of restoration performance.

Authors

Zhi-Yong Wang,Hing Cheung So,Abdelhak M Zoubir

Journal

arXiv preprint arXiv:2310.06233

Published Date

2023/10/10

arXiv: Development of novel low-mass module concepts based on MALTA monolithic pixel sensors

The MALTA CMOS monolithic silicon pixel sensors has been developed in the Tower 180 nm CMOS imaging process. It includes an asynchronous readout scheme and complies with the ATLAS inner tracker requirements for the HL-LHC. Several 4-chip MALTA modules have been built using Al wedge wire bonding to demonstrate the direct transfer of data from chip-to-chip and to read out the data of the entire module via one chip only. Novel technologies such as Anisotropic Conductive Films (ACF) and nanowires have been investigated to build a compact module. A lightweight flex with 17 μm trace spacing has been designed, allowing compact packaging with a direct attachment of the chip connection pads to the flex using these interconnection technologies. This contribution shows the current state of our work towards a flexible, low material, dense and reliable packaging and modularization of pixel detectors.

Authors

J Weick,I Asensi Tortajada,AM Zoubir,D Dobrijevic,P Riedler,R de Oliveira,D Dannheim,M Van Rijnbach,H Pernegger,V Dao,A Sharma,JV Schmidt,F Dachs,M Pinto,C Solans Sanchez,L de Acedo

Published Date

2023/3/10

Identifying the Complete Correlation Structure in Large-Scale High-Dimensional Data Sets with Local False Discovery Rates

The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the data sets are high-dimensional with few observations or available samples and contain latent components with unknown probability distributions. A novel mathematical formulation of this problem is proposed, which enables the inference of the underlying correlation structure with strict false positive control. In particular, the false discovery rate is controlled at a pre-defined threshold on two levels simultaneously. The deployed test statistics originate in the sample coherence matrix. The required probability models are learned from the data using the bootstrap. Local false discovery rates are used to solve the multiple hypothesis testing problem. Compared to the existing techniques in the literature, the developed technique does not assume an a priori correlation structure and work well when the number of data sets is large while the number of observations is small. In addition, it can handle the presence of distributional uncertainties, heavy-tailed noise, and outliers.

Authors

Martin Gölz,Tanuj Hasija,Michael Muma,Abdelhak M Zoubir

Journal

arXiv preprint arXiv:2305.19121

Published Date

2023/5/30

Radar Based Humans Localization with Compressed Sensing and Sparse Reconstruction

Localization and detection is a vital task in emergency rescue operations. Devastating natural disasters can create environments that are inaccessible or dangerous for human rescuers. Contaminated areas or buildings in danger of collapsing can be searched by rescue robots which are equipped with diverse sensors such as optical and radar sensors. In scenarios where the line of sight is blocked, e.g., by a wall, a door or heavy smoke or dust, sensors like LiDAR or cameras are not able to provide sufficient information. The usage of radar in these kinds of situations can drastically improve situational awareness and hence the likelihood of rescue. In this paper, we present a method that is used for radar imaging behind obstacles by utilizing a signal model that includes the floor reflection propagation path in addition to the direct path of the radar signal. Additionally, compressed sensing methods are presented and …

Authors

Christian Eckrich,Christian A Schroth,Vahid Jamali,Abdelhak M Zoubir

Published Date

2023/6/11

Sparse Array Design for Joint Communication Radar System via Antenna Selection

This work investigates the design of sparse transmit arrays via antenna selection for joint communication radar (JCR) systems. Antenna selection is performed in terms of optimizing the ambiguity function of the transmit signal for radar detection and guaranteeing the communication quality of service. The optimization problem is formulated with respect to a boolean vector, that is used to denote the antenna selection directly without any approximation. To address the non-convex combinational optimization problem, zero-forcing beamforming is applied, and subsequently an alternative optimization method with a genetic algorithm incorporated is proposed. Simulation results validate the superiority of the proposed JCR system with the sparse transmit array and demonstrate its better performance compared with other sparse array design.

Authors

Jing Xu,Xiangrong Wang,Xingshuai Qiao,Abdelhak M Zoubir

Published Date

2023/7/13

Sparsity-aware block diagonal representation for subspace clustering

A block diagonally structured affinity matrix is an informative prior for subspace clustering which embeds the data points in a union of low-dimensional subspaces. Structuring a block diagonal matrix can be challenging due to the determination of an appropriate sparsity level, especially when outliers and heavy-tailed noise obscure the underlying subspaces. We propose a new sparsity-aware block diagonal representation (SABDR) method that robustly estimates the appropriate sparsity level by leveraging upon the geometrical analysis of the low-dimensional structure in spectral clustering. Specifically, we derive the Euclidean distance between the embeddings of different clusters to develop a computationally efficient density-based clustering algorithm. In this way, the sparsity parameter selection problem is re-formulated as a robust approximation of target between-clusters distances. Comprehensive experiments …

Authors

Aylin Taştan,Michael Muma,Esa Ollila,Abdelhak M Zoubir

Published Date

2023/9/4

Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer

This paper presents a novel loss function referred to as hybrid ordinary-Welsch (HOW) and a new sparsity-inducing regularizer associated with HOW. We theoretically show that the regularizer is quasiconvex and that the corresponding Moreau envelope is convex. Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived. Compared with nonconvex regularizers like the lp-norm with 0<p<1 that requires iterations to find the corresponding proximity operator, the developed regularizer has a closed-form proximity operator. We apply our regularizer to the robust matrix completion problem, and develop an efficient algorithm based on the alternating direction method of multipliers. The convergence of the suggested method is analyzed and we prove that any generated accumulation point is a stationary point. Finally, experimental results based on synthetic and real-world datasets demonstrate that our algorithm is superior to the state-of-the-art methods in terms of restoration performance.

Authors

Zhi-Yong Wang,Hing Cheung So,Abdelhak M Zoubir

Journal

arXiv preprint arXiv:2310.04762

Published Date

2023/10/7

Fast and Robust Sparsity-Aware Block Diagonal Representation

The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal affinity matrix is challenging in real-world applications, in which the data may be subject to outliers and heavy-tailed noise that obscure the hidden cluster structure. To address this issue, we first analyze the effect of different fundamental outlier types in graph-based cluster analysis. A key idea that simplifies the analysis is to introduce a vector that represents a block diagonal matrix as a piece-wise linear function of the similarity coefficients that form the affinity matrix. We reformulate the problem as a robust piece-wise linear fitting problem and propose a Fast and Robust Sparsity-Aware Block Diagonal Representation (FRS-BDR) method, which jointly estimates cluster …

Authors

Aylin Taştan,Michael Muma,Abdelhak M Zoubir

Journal

IEEE Transactions on Signal Processing

Published Date

2023/12/19

Adaptive rank-one matrix completion using sum of outer products

Matrix completion refers to recovering a matrix from a small subset of its entries. It is an important topic because numerous real-world data can be modeled as low-rank matrices. One popular approach for matrix completion is based on low-rank matrix factorization, but it requires knowing the matrix rank, which is difficult to accurately determine in many practical scenarios. We propose a novel algorithm based on rank-one approximation that a matrix can be decomposed as a sum of outer products. The key idea is to find the basis vectors of the underlying matrix according to the observed entries, and gradually increase the vector number until an appropriate rank estimate is reached. In contrast to the conventional rank-one schemes that employ unchanging rank-one basis matrices, our algorithm performs completion from the vector viewpoint and is able to generate continuously updated rank-one basis matrices …

Authors

Zhi-Yong Wang,Xiao Peng Li,Hing Cheung So,Abdelhak M Zoubir

Journal

IEEE Trans. Circuits Syst. Video Technol.

Published Date

2023/3/1

Development of novel low-mass module concepts based on MALTA monolithic pixel sensors

The MALTA CMOS monolithic silicon pixel sensors has been developed in the Tower 180 nm CMOS imaging process. It includes an asynchronous readout scheme and complies with the ATLAS inner tracker requirements for the HL-LHC. Several 4-chip MALTA modules have been built using Al wedge wire bonding to demonstrate the direct transfer of data from chip-to-chip and to read out the data of the entire module via one chip only. Novel technologies such as Anisotropic Conductive Films (ACF) and nanowires have been investigated to build a compact module. A lightweight flex with 17 μm trace spacing has been designed, allowing compact packaging with a direct attachment of the chip connection pads to the flex using these interconnection technologies. This contribution shows the current state of our work towards a flexible, low material, dense and reliable packaging and modularization of pixel detectors.

Authors

Julian Weick,F Dachs,P Riedler,M Vicente Barreto Pinto,AM Zoubir,L Flores Sanz de Acedo,I Asensi Tortajada,V Dao,D Dobrijevic,H Pernegger,M Van Rijnbach,A Sharma,C Solans Sanchez,R de Oliveira,D Dannheim,JV Schmidt

Journal

Journal of Instrumentation

Published Date

2023/4/3

Heterogeneity-Stratified Bootstrap Oversampling for Training a Spoiled Food Detector

We propose the Heterogeneity-Stratified Bootstrap (HSBoot), a stratification method that gives higher resampling probabilities to the sample points in the less homogeneous regions. We demonstrate its advantage in the case of training a detector by oversampling the under-represented class in an imbalanced data set. We took a case study of a spoiled food detector in form of an electronic nose. The performance metrics were calculated on the out-of-bag test set as well as on measurements collected from another sensor.

Authors

Pertami J Kunz,Abdelhak M Zoubir

Published Date

2023/6/11

Performance of the MALTA telescope

MALTA is part of the Depleted Monolithic Active Pixel sensors designed in Tower 180 nm CMOS imaging technology. A custom telescope with six MALTA planes has been developed for test beam campaigns at SPS, CERN, with the ability to host several devices under test. The telescope system has a dedicated custom readout, online monitoring integrated into DAQ with realtime hit map, time distribution and event hit multiplicity. It hosts a dedicated fully configurable trigger system enabling to trigger on coincidence between telescope planes and timing reference from a scintillator. The excellent time resolution performance allows for fast track reconstruction, due to the possibility to retain a low hit multiplicity per event which reduces the combinatorics. This paper reviews the architecture of the system and its performance during the 2021 and 2022 test beam campaign at the SPS North Area.

Authors

Milou Van Rijnbach,Giuliano Gustavino,Phil Allport,Ignacio Asensi Tortajada,Dumitru Vlad Berlea,Daniela Bortoletto,Craig Buttar,Edoardo Charbon,Florian Dachs,Valerio Dao,Dominik Dobrijevic,Leyre Flores Sanz de Acedo,Andrea Gabrielli,Martin Gazi,Laura Gonella,Vicente Gonzalez,Stefan Guidon,Matt LeBlanc,Heinz Pernegger,Francesco Piro,Petra Riedler,Heidi Sandaker,Abhishek Sharma,Carlos Solans Sanchez,Walter Snoeys,Tomislav Suligoj,Marcos Vazquez Nunez,Julian Weick,Steven Worm,Abdelhak M Zoubir

Published Date

2023/7/8

Complex Seasonal Circular Block Bootstrap for Electricity Load Forecasting

We propose the Complex Seasonal Circular Block Bootstrap (XSCBB), a variation of seasonal (circular) block bootstrap that caters for multiple seasonality components in a time series. Electricity consumption (load) prediction is important to balance the supply and load demand, to plan facilities construction and maintenance, to plan distribution, and avoid outages or excess loss. We apply the XSCBB method parametrically to calculate the prediction interval of future electricity consumption given a relatively small amount of historical sample points using the composite ARMA(p, q) - GARCH(r, s) model.

Authors

Pertami J Kunz,Abdelhak M Zoubir

Published Date

2023/9/4

A Data-driven Deep Learning Approach for Bitcoin Price Forecasting

Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.

Authors

Parth Daxesh Modi,Kamyar Arshi,Pertami J Kunz,Abdelhak M Zoubir

Journal

arXiv e-prints

Published Date

2023/10

Attentional Graph Neural Networks for Robust Massive Network Localization

Graph neural networks (GNNs) have gained significant popularity for classification tasks in machine learning, yet their applications to regression problems remain limited. Concurrently, attention mechanisms have emerged as powerful tools in sequential learning tasks. In this paper, we employ GNNs and attention mechanisms to address a classical but challenging nonlinear regression problem: network localization. We propose a novel GNN-based network localization method that achieves exceptional stability and accuracy in the presence of severe non-line-of-sight (NLOS) propagations, while eliminating the need for laborious offline calibration or NLOS identification. Extensive experimental results validate the effectiveness and high accuracy of our GNN-based localization model, particularly in challenging NLOS scenarios. However, the proposed GNN-based model exhibits limited flexibility, and its accuracy is highly sensitive to a specific hyperparameter that determines the graph structure. To address the limitations and extend the applicability of the GNN-based model to real scenarios, we introduce two attentional graph neural networks (AGNNs) that offer enhanced flexibility and the ability to automatically learn the optimal hyperparameter for each node. Experimental results confirm that the AGNN models are able to enhance localization accuracy, providing a promising solution for real-world applications. We also provide some analyses of the improved performance achieved by the AGNN models from the perspectives of dynamic attention and signal denoising characteristics.

Authors

Wenzhong Yan,Juntao Wang,Feng Yin,Abdelhak M Zoubir

Journal

arXiv preprint arXiv:2311.16856

Published Date

2023/11/28

Low-rank and row-sparse decomposition for joint DOA estimation and distorted sensor detection

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. In this article, we consider an array model within which a small number of sensors are distorted by unknown sensor gain and phase errors. With such an array model, the problem of joint direction-of-arrival (DOA) estimation and distorted sensor detection is formulated under the framework of low-rank and row-sparse decomposition. We derive an iteratively reweighted least squares (IRLS) algorithm to solve the resulting problem. The convergence property of the IRLS algorithm is analyzed by means of the monotonicity and boundedness of the objective function. Extensive simulations are conducted regarding parameter selection, convergence speed, computational complexity, and performances of DOA estimation as well as distorted sensor detection. Even though the IRLS algorithm is slightly worse than the alternating …

Authors

Huiping Huang,Qi Liu,Hing Cheung So,Abdelhak M Zoubir

Journal

IEEE Transactions on Aerospace and Electronic Systems

Published Date

2023/2/3

arXiv: Performance of the MALTA Telescope

MALTA is part of the Depleted Monolithic Active Pixel sensors designed in Tower 180nm CMOS imaging technology. A custom telescope with six MALTA planes has been developed for test beam campaigns at SPS, CERN, with the ability to host several devices under test. The telescope system has a dedicated custom readout, online monitoring integrated into DAQ with realtime hit map, time distribution and event hit multiplicity. It hosts a dedicated fully configurable trigger system enabling to trigger on coincidence between telescope planes and timing reference from a scintillator. The excellent time resolution performance allows for fast track reconstruction, due to the possibility to retain a low hit multiplicity per event which reduces the combinatorics. This paper reviews the architecture of the system and its performance during the 2021 and 2022 test beam campaign at the SPS North Area.

Authors

Milou van Rijnbach,Vicente Gonzalez,Craig Buttar,Steven Worm,Stefan Guindon,Daniela Bortoletto,Heidi Sandaker,Edoardo Charbon,Valerio Dao,Phil Allport,Leyre Flores Sanz de Acedo,Giuliano Gustavino,Abhishek Sharma,Walter Snoeys,Dominik Dobrijevic,Abdelhak M Zoubir,Dumitru Vlad Berlea,Carlos Solans Sanchez,Petra Riedler,Heinz Pernegger,Tomislav Suligoj,Julian Weick,Marcos Vazquez Nunez,Matt LeBlanc,Martin Gazi,Laura Gonella,Florian Dachs,Francesco Piro,Andrea Gabrielli,Igancio Asensi

Published Date

2023/4/3

Attacks on robust distributed learning schemes via sensitivity curve maximization

Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local interactions. Most such strategies rely on a mixture of local adaptation and aggregation steps, either among peers or at a central fusion center. Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents. This observation has motivated a number of recent works, which develop robust aggregation schemes by employing robust variations of the mean. We present a new attack based on sensitivity curve maximization (SCM), and demonstrate that it is able to disrupt existing robust aggregation schemes by injecting small, but effective perturbations.

Authors

Christian A Schroth,Stefan Vlaski,Abdelhak M Zoubir

Published Date

2023/6/11

Robust low-rank matrix recovery via hybrid ordinary-Welsch function

As a widely-used tool to resist outliers, the correntropy criterion or Welsch function has recently been exploited for robust matrix recovery. However, it down-weighs all observations including uncontaminated data. On the other hand, its implicit regularizer (IR) cannot achieve sparseness, which is a desirable property in many practical scenarios. To address these two issues, we devise a novel M-estimator called hybrid ordinary-Welsch (HOW) function, which only down-weighs the outlier-contaminated data, and the IR generated by the HOW can attain sparseness. To verify the effectiveness of the HOW function, we apply it to robust matrix completion and principal component analysis. An efficient algorithm is developed and we prove that any generated limit point is a critical point. Finally, extensive experimental results based on synthetic and real-world data demonstrate that the proposed approach outperforms the …

Authors

Zhi-Yong Wang,Hing Cheung So,Abdelhak M Zoubir

Journal

IEEE Transactions on Signal Processing

Published Date

2023/7/7

Data-adaptive M-estimators for robust regression via bi-level optimization

M-estimators are widely used in robust regression to handle heavy-tailed data corrupted by outliers. Although they have been applied to a plethora of real scenarios, it remains a challenge to practitioners how to set the tuning parameters. Often, it is set by manual tuning or according to the asymptotic efficiency rule, being sub-optimal for a real dataset with finite size. In this paper, we explore a data-driven paradigm where the optimal tuning parameters are determined by the dataset itself. Specifically, we treat the tuning parameters as hyper-parameters in robust regression, formulate the tuning problem via a novel bi-level optimization framework, and solve the regression model parameters and the tuning parameters in a joint manner. To solve this problem efficiently, especially when using neural network as the regression model, we further employ an online approximation strategy to iteratively optimize the model …

Authors

Ceyao Zhang,Tianjian Zhang,Feng Yin,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2023/9/1

Sparse array beamformer design via ADMM

In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, we prove that the proposed algorithm converges to the set of Karush-Kuhn-Tucker stationary points. Numerical results exhibit its excellent performance, which is comparable to that of the exhaustive search approach, slightly better than those of the state-of-the-art solvers, including the semidefinite relaxation (SDR), its variant (SDR-V), and the successive convex approximation (SCA) approaches, and significantly …

Authors

Huiping Huang,Hing Cheung So,Abdelhak M Zoubir

Journal

IEEE Transactions on Signal Processing

Published Date

2023/9/14

Transmit Sparse Array Beamformer Design for Dual-Function Radar Communication Systems

Sparse arrays could achieve curtailed mutual coupling and reduced hardware cost while preserving an intact large array aperture, which is conducive to the joint design of radar and communication. In this paper, transmit sparse array beam former design for dual-function radar communication (DFRC) systems is discussed. We propose an integrated model that takes both radar transmit beam-forming for power concentration and array sparsity into account. Meanwhile, communication is realized by embedding information into beampattern via amplitude modulation (AM) and phase modulation (PM). Different from our previous work, one common sparse array with different beam-formers is designed for the constellation of all communication symbols. To solve this problem, we decompose the non-convex optimization problem subject to multiple constraints into two subproblems within the ADMM framework, and the …

Authors

Jiayi Huang,Xuan Zhang,Xiangrong Wang,Abdelhak M Zoubir

Published Date

2023/11/6

Development of a large-area, light-weight module using the MALTA monolithic pixel detector

The MALTA pixel chip is a 2 cm× 2 cm large monolithic pixel detector developed in the Tower 180 nm imaging process. The chip contains four CMOS transceiver blocks at its sides which allow chip-to-chip data transfer. The power pads are located mainly at the side edges on the chip which allows for chip-to-chip power transmission. The MALTA chip has been used to study module assembly using different interconnection techniques to transmit data and power from chip to chip and to minimize the overall material budget. Several 2-chip and 4-chip modules have been assembled using standard wire bonding, ACF (Anisotropic Conductive Films) and laser reflow interconnection techniques. These proceedings will summarize the experience with the different interconnection techniques and performance tests of MALTA modules with 2 and 4 chips tested in a cosmic muon telescope. They will also show first results on …

Authors

F Dachs,P Allport,I Asensi Tortajada,DV Berlea,D Bortoletto,C Buttar,E Charbon,V Dao,H Denizli,D Dobrijevic,L Flores Sanz de Acedo,A Gabrielli,L Gonella,V Gonzalez,G Gustavino,M LeBlanc,KY Oyulmaz,H Pernegger,F Piro,P Riedler,M van Rijnbach,H Sandaker,A Sharma,W Snoeys,CA Solans Sanchez,T Suligoj,M Vásquez,M Vicente Barreto Pinto,J Weick,S Worm,AM Zoubir

Journal

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Published Date

2023/2/1

Timing performance of radiation hard MALTA monolithic pixel sensors

The MALTA family of Depleted Monolithic Active Pixel Sensor (DMAPS) produced in Tower 180 nm CMOS technology targets radiation hard applications for the HL-LHC and beyond. Several process modifications and front-end improvements have resulted in radiation hardness up to 2× 10 15 1 MeV n eq/cm 2 and time resolution below 2 ns, with uniform charge collection efficiency across the pixel of size 36.4× 36.4 μm 2 with a 3 μm 2 electrode size. The MALTA2 demonstrator produced in 2021 on high-resistivity epitaxial silicon and on Czochralski substrates implements a new cascoded front-end that reduces the RTS noise and has a higher gain. This contribution shows results from MALTA2 on timing resolution at the nanosecond level from the CERN SPS test-beam campaign of 2021.

Authors

Giuliano Gustavino,P Allport,I Asensi,DV Berlea,D Bortoletto,Craig Buttar,F Dachs,V Dao,H Denizli,D Dobrijevic,L Flores,A Gabrielli,L Gonella,V González,M LeBlanc,K Oyulmaz,H Pernegger,F Piro,P Riedler,H Sandaker,C Solans,W Snoeys,T Suligoj,M van Rijnbach,A Sharma,M Vázquez Nuñez,J Weick,S Worm,A Zoubir

Journal

Journal of Instrumentation

Published Date

2023/3/15

Improving inference for spatial signals by contextual false discovery rates

A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal’s spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No …

Authors

Martin Gölz,Abdelhak M Zoubir,Visa Koivunen

Published Date

2022/5/23

Semi-Supervised Online Speaker Diarization using Vector Quantization with Alternative Codebooks

Speaker diarization systems process audio files by labelling speech segments according to speakers' identities. Many speaker diarization systems work offline and are not suited for online applications. We present a semi-supervised, online, low-complexity system. While, in general, speaker diarization operates in an unsupervised manner, the presented system relies on the enrollment of the participating speakers in the conversation. The diarization system has two main novel aspects. The first one is a proposed online learning strategy that evaluates processed segments according to their usefulness for learning a speaker, i.e. update a speaker model with it. The segment is evaluated using two metrics to determine whether to use the segment to update the system. The second novel aspect is a proposed vector quantization approach that models the score not only depending on the target speaker codebook but also …

Authors

Mahmoud El-Hindi,Michael Muma,Abdelhak M Zoubir

Published Date

2022/8/29

Bayesian sequential joint detection and estimation under multiple hypotheses

We consider the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution. This problem is investigated in a sequential setup under mild assumptions on the underlying random process. The optimal method minimizes the expected number of samples while ensuring that the average detection/estimation errors do not exceed a certain level. After converting the constrained problem to an unconstrained one, we characterize the general solution by a nonlinear Bellman equation, which is parameterized by a set of cost coefficients. A strong connection between the derivatives of the cost function with respect to the coefficients and the detection/estimation errors of the sequential procedure is derived. Based on this fundamental property, we further show that for suitably chosen cost coefficients the solutions of the constrained and the unconstrained problem coincide. We …

Authors

Dominik Reinhard,Michael Fauß,Abdelhak M Zoubir

Journal

Sequential Analysis

Published Date

2022/4/3

Eigenvalue-Based Block Diagonal Representation and Application to p-Nearest Neighbor Graphs

Block diagonal structure of the affinity matrix is advantageous, e.g. in graph-based cluster analysis, where each block corresponds to a cluster. However, constructing block diagonal affinity matrices may be challenging and computationally demanding. We propose a new eigenvalue-based block diagonal representation (EBDR) method. The idea is to estimate a block diagonal affinity matrix by finding an approximation to a vector of target eigenvalues. The target eigenvalues, which follow the ideal block-diagonal model, are efficiently determined based on a vector derived from the graph Laplacian that represents the blocks as a piece-wise linear function. The proposed EBDR shows promising performance compared to four optimally tuned state-of-the-art methods in terms of clustering accuracy and computation time using real-data examples.

Authors

Aylin Taştan,Michael Muma,Abdelhak M Zoubir

Published Date

2022/8/29

Min-max optimization for MIMO radar waveform design with improved power efficiency

Transmit waveform design with power efficiency constraint is a prominent problem for colocated multiple-input-multiple-output (MIMO) radar. Though there are extensive relevant works in the literature, the existing peak-to-average-power ratio (PAR) constraint is insufficient in controlling the transmit power uniformity and the conventional constant modulus (CM) is imposed on both waveform temporal and spatial dimensions, resulting in an unnecessary loss of degrees of freedom. Thereby, we introduce an individual antenna power control (IAPC) scheme with a new peak-to-valley-power-ratio (PVR) constraint to flexibly control the transmit power uniformity along the spatial dimension. We then devise two min-max waveform designs for shaped transmit beampattern synthesis. To tackle the resultant non-convex problems, we propose a surrogate primal alternating direction method of multipliers (SP-ADMM) to …

Authors

Xuan Zhang,Xiangrong Wang,Hing Cheung So,Abdelhak M Zoubir,Guolong Cui

Journal

IEEE Transactions on Signal Processing

Published Date

2022/12/30

Estimating test statistic distributions for multiple hypothesis testing in sensor networks

We recently proposed a novel approach to perform spatial inference using large-scale sensor networks and multiple hypothesis testing [1]. It identifies the regions in which a spatial phenomenon of interest exhibits different behavior from its nominal statistical model. To reduce the intra-sensor-network communication overhead, the raw data is pre-processed at the sensors locally and a summary statistic is send to the cloud or fusion center where the actual spatial inference using multiple hypothesis testing and false discovery control takes place. Local false discovery rates (lfdrs) are estimated to express local believes in the state of the spatial signal. In this work, we extend our approach by proposing two novel lfdr estimators stemming from the Expectation-Maximization method. The estimation bias is considered to explain the differences in performance among the compared lfdr estimators.

Authors

Martin Gölz,Abdelhak M Zoubir,Visa Koivunen

Published Date

2022/3/9

Robust and efficient aggregation for distributed learning

Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange raw data. Instead, agents compute model updates locally based on their available data, and subsequently share the update model with a parameter server or their peers. This is followed by an aggregation step, which traditionally takes the form of a (weighted) average. Distributed learning schemes based on averaging are known to be susceptible to outliers. A single malicious agent is able to drive an averaging-based distributed learning algorithm to an arbitrarily poor model. This has motivated the development of robust aggregation schemes, which are based on variations of the median and trimmed mean. While such procedures ensure robustness to outliers and malicious behavior, they come at the cost of significantly reduced sample …

Authors

Stefan Vlaski,Christian Schroth,Michael Muma,Abdelhak M Zoubir

Published Date

2022/8/29

An efficient normalized LMS algorithm

The task of adaptive estimation in the presence of random and highly nonlinear environment such as wireless channel estimation and identification of non-stationary system etc. has been always challenging. The least mean square (LMS) algorithm is the most popular algorithm for adaptive estimation and it belongs to the gradient family, thus inheriting their low computational complexity and their slow convergence. To deal with this issue, an efficient normalization of the LMS algorithm is proposed in this work which is achieved by normalizing the input signal with an intelligent mixture of weighted signal and error powers which results in a variable step-size type algorithm. The proposed normalization scheme can provide both significant faster convergence in initial adaptation phase while maintaining a lower steady-state mean-square-error compared to the conventional normalized LMS (NLMS) algorithm. The …

Authors

Azzedine Zerguine,Jawwad Ahmad,Muhammad Moinuddin,Ubaid M Al-Saggaf,Abdelhak M Zoubir

Journal

Nonlinear Dynamics

Published Date

2022/12

Multiple hypothesis testing framework for spatial signals

The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated …

Authors

Martin Gölz,Abdelhak M Zoubir,Visa Koivunen

Journal

IEEE Transactions on Signal and Information Processing over Networks

Published Date

2022/7/14

Recent results with radiation-tolerant TowerJazz 180 nm MALTA sensors

To achieve the physics goals of future colliders, it is necessary to develop novel, radiation-hard silicon sensors for their tracking detectors. We target the replacement of hybrid pixel detectors with Depleted Monolithic Active Pixel Sensors (DMAPS) that are radiation-hard, monolithic CMOS sensors. We have designed, manufactured and tested the MALTA series of sensors, which are DMAPS in the 180 nm TowerJazz CMOS imaging technology. MALTA have a pixel pitch well below current hybrid pixel detectors, high time resolution (< 2 ns) and excellent charge collection efficiency across pixel geometries. These sensors have a total silicon thickness of between 50–300 μ m, implying reduced material budgets and multiple scattering rates for future detectors which utilise such technology. Furthermore, their monolithic design bypasses the costly stage of bump-bonding in hybrid sensors and can substantially reduce …

Authors

Matt LeBlanc,Phil Allport,Igancio Asensi,Dumitru-Vlad Berlea,Daniela Bortoletto,Craig Buttar,Florian Dachs,Valerio Dao,Haluk Denizli,Dominik Dobrijevic,Leyre Flores,Andrea Gabrielli,Laura Gonella,Vicente González,Giuliano Gustavino,Kaan Oyulmaz,Heinz Pernegger,Francesco Piro,Petra Riedler,Heidi Sandaker,Carlos Solans,Walter Snoeys,Tomislav Suligoj,Milou van Rijnbach,Abhishek Sharma,Marcos Vázquez Núñez,Julian Weick,Steven Worm,Abdelhak Zoubir

Journal

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Published Date

2022/10/11

Off-grid direction-of-arrival estimation using second-order Taylor approximation

The problem of off-grid direction-of-arrival (DOA) estimation is investigated. We develop a grid-based method to jointly estimate the closest spatial frequency (the sine of DOA) grids, and the gaps between the estimated grids and the corresponding frequencies. By using a second-order Taylor approximation, the data model under the framework of joint-sparse representation is formulated. We point out an important property of the signals of interest in the model, namely the proportionality relationship, which is empirically demonstrated to be useful in the sense that it increases the probability of the mixing matrix satisfying the block restricted isometry property. Simulation examples demonstrate the effectiveness and superiority of the proposed method against several state-of-the-art grid-based approaches.

Authors

Huiping Huang,Hing Cheung So,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2022/7/1

arXiv: Timing performance of radiation hard MALTA monolithic Pixel sensors

The MALTA family of Depleted Monolithic Active Pixel Sensor (DMAPS) produced in Tower 180 nm CMOS technology targets radiation hard applications for the HL-LHC and beyond. Several process modifications and front-end improvements have resulted in radiation hardness up to and time resolution below 2 ns, with uniform charge collection efficiency across the Pixel of size with a electrode size. The MALTA2 demonstrator produced in 2021 on high-resistivity epitaxial silicon and on Czochralski substrates implements a new cascoded front-end that reduces the RTS noise and has a higher gain. This contribution shows results from MALTA2 on timing resolution at the nanosecond level from the CERN SPS test-beam campaign of 2021.

Authors

G Gustavino,P Riedler,V González,K Oyulmaz,J Weick,F Dachs,M Vázquez Núñez,T Suligoj,M LeBlanc,D Bortoletto,M van Rijnbach,S Worm,P Allport,I Asensi,H Denizli,H Sandaker,C Solans,DV Berlea,A Gabrielli,C Buttar,D Dobrijevic,W Snoeys,H Pernegger,L Flores,L Gonella,A Zoubir,V Dao,F Piro,A Sharma

Published Date

2022/9/29

Robust Bayesian cluster enumeration based on the t distribution

A major challenge in cluster analysis is that the number of data clusters is mostly unknown and it must be estimated prior to clustering the observed data. In real-world applications, the observed data is often subject to heavy tailed noise and outliers which obscure the true underlying structure of the data. Consequently, estimating the number of clusters becomes challenging. To this end, we derive a robust cluster enumeration criterion by formulating the problem of estimating the number of clusters as maximization of the posterior probability of multivariate t ν distributed candidate models. We utilize Bayes’ theorem and asymptotic approximations to come up with a robust criterion that possesses a closed-form expression. Further, we refine the derivation and provide a robust cluster enumeration criterion for data sets with finite sample size. The robust criteria require an estimate of cluster parameters for each candidate …

Authors

Freweyni K Teklehaymanot,Michael Muma,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2021/5/1

Introduction to the issue on recent advances in automotive radar signal processing

The goal of this special issue is to present advanced signal processing techniques as an enabler for novel technological advances. Both industrial and academic communities are targeted with this collection of papers. We provide a detailed introductory discussion on the main differences between automotive radar and traditional radar, and present an overview of the articles in this special issue.

Authors

Philipp Heidenreich,Abdelhak Zoubir,Igal Bilik,Maria Greco,Murat Torlak

Published Date

2021/6/8

Robust copula-based detection of shallow-buried landmines with forward-looking radar

We propose a technique for landmine detection using forward-looking ground-penetrating radar. The detector is applied to radar images obtained from multiple viewpoints of the region of interest and is based on a robust version of the likelihood ratio test (LRT). We incorporate the statistical dependence between multiview images into the test via copula-based model. The test is designed to maximize the worst-case performance over all feasible pairs of target and clutter distributions, thereby eliminating the need for strong assumptions about the image statistics. We evaluate the detection performance of the proposed technique for different copula functions representing the dependence structure. Using electromagnetic modeled data of shallow-buried targets under varying ground surface roughness profiles, we demonstrate the superiority of the robust copula-based detector over existing parametric and robust LRT …

Authors

Afief Dias Pambudi,Fauzia Ahmad,Abdelhak M Zoubir

Journal

IEEE Transactions on Aerospace and Electronic Systems

Published Date

2021/9/10

Automotive radar signal processing: Research directions and practical challenges

Automotive radar is used in many applications of advanced driver assistance systems and is considered as one of the key technologies for highly automated driving. An overview of state-of-the-art signal processing in automotive radar is presented along with current research directions and practical challenges. We provide a comprehensive signal model for the multiple-target case using multiple-input multiple-output schemes, and discuss a practical processing chain to calculate the target list. To demonstrate the capabilities of a modern series production high-performance radar sensor, real data examples are given. An overview of conventional target processing and recent research activities in machine learning and deep learning approaches is presented. Additionally, recent methods for practically relevant radar-camera fusion are discussed.

Authors

Florian Engels,Philipp Heidenreich,Markus Wintermantel,Lukas Stäcker,Muhammed Al Kadi,Abdelhak M Zoubir

Published Date

2021/3/3

A Robust Copula Model for Radar-Based Landmine Detection

We present a robust copula model for landmine detection based on a likelihood ratio test. The test is applied to radar-based imagery from multiple viewpoints of the interrogation area. Different copula density functions are investigated in terms of their effectiveness in incorporating the statistical dependence between multi-view images. The test is designed to maximize the worst-case performance over all feasible mine and clutter distributions. Using numerical radar data of shallow buried targets under varying surface roughness, we demonstrate that the robust copula-based detector outperforms existing approaches and provides a high detection performance for a wide range of false-alarm rates.

Authors

Afief D Pambudi,Fauzia Ahmad,Abdelhak M Zoubir

Published Date

2021/6/6

Robust spectral clustering: A locality preserving feature mapping based on M-estimation

Dimension reduction is a fundamental task in spectral clustering. In practical applications, the data may be corrupted by outliers and noise, which can obscure the underlying data structure. The effect is that the embeddings no longer represent the true cluster structure. We therefore propose a new robust spectral clustering algorithm that maps each high-dimensional feature vector onto a low-dimensional vector space. Robustness is achieved by posing the locality preserving feature mapping problem in form of a ridge regression task that is solved with a penalized M-estimation approach. An unsupervised penalty parameter selection strategy is proposed using the Fiedler vector, which is the eigenvector associated with the second smallest eigenvalue of a connected graph. More precisely, the penalty parameter is selected, such that, the corresponding Fiedler vector is Δ-separated with a minimum information loss on …

Authors

Aylin Taştan,Michael Muma,Abdelhak M Zoubir

Published Date

2021/8/23

Minimax robust detection: Classic results and recent advances

This paper provides an overview of results and concepts in minimax robust hypothesis testing for two and multiple hypotheses. It starts with an introduction to the subject, highlighting its connection to other areas of robust statistics and giving a brief recount of the most prominent developments. Subsequently, the minimax principle is introduced and its strengths and limitations are discussed. The first part of the paper focuses on the two-hypothesis case. After briefly reviewing the basics of statistical hypothesis testing, uncertainty sets are introduced as a generic way of modeling distributional uncertainty. The design of minimax detectors is then shown to reduce to the problem of determining a pair of least favorable distributions, and different criteria for their characterization are discussed. Explicit expressions are given for least favorable distributions under three types of uncertainty: ε-contamination, probability density …

Authors

Michael Fauß,Abdelhak M Zoubir,H Vincent Poor

Published Date

2021/2/24

An Asymptotically Pointwise Optimal Procedure For Sequential Joint Detection And Estimation

We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on sequentially observed data whose distribution belongs to the exponential family. The aim is to design a scheme which minimizes the expected number of used samples while limiting the detection and estimation errors to pre-set lev-els. This constrained problem is first converted to an unconstrained problem which is then reduced to an optimal stopping problem. To solve the optimal stopping problem, we propose an asymptotically pointwise optimal (APO) stopping rule, i.e., a stopping rule that is optimal when the tolerated detection and estimation errors tend to zero. The policy parameterizing coefficients are then chosen such that the constraints on the detection and estimation errors are fulfilled. The proposed theory is illustrated with a numerical example.

Authors

Dominik Reinhard,Michael Fauß,Abdelhak M Zoubir

Published Date

2021/6/6

Methods to estimate the blood pressure and the arterial stiffness based on photoplethysmographic (ppg) signals

The present invention relates to a method to estimate the blood pressure and the arterial stiffness based on photoplethysmographic (PPG) signals. New algorithms have been developed and validated based on PPG signals to analyze the cardiovascular condition of a person by estimating cardiovascular parameters. With the present invention a method for measuring one or more cardiovascular parameters in a subject based on PPG signals is provided.

Published Date

2021/8/12

Distributed sequential joint detection and estimation for non-Gaussian noise

The problem of jointly testing a hypothesis and estimating a random parameter in non-Gaussian noise is investigated in a sequential and distributed setup. The non-Gaussian noise is modeled by a mixture of a completely known Gaussian distribution and an unknown contaminating distribution. Starting from the consensus+innovations approach, we present two robust communication schemes that are insensitive to the contaminating distribution. After deriving upper bounds for the variances of the estimators, a sequential scheme is designed at every sensor such that i) detection and estimation errors are limited for all possible contaminating distributions ii) the resulting scheme uses a minimum number of samples on average. A numerical example validates the proposed method.

Authors

Dominik Reinhard,Abdelhak M Zoubir

Published Date

2021/1/18

Low-rank and sparse decomposition for joint DOA estimation and contaminated sensors detection with sparsely contaminated arrays

Many works have been done in direction-of-arrival (DOA) estimation in the presence of sensor gain and phase uncertainties in the past decades. Most of the existing approaches require either auxiliary sources with exactly known DOAs or perfectly partly calibrated arrays. In this work, we consider sparsely contaminated arrays in which only a few sensors are contaminated by sensor gain and phase errors, and moreover, the number of contaminated sensors as well as their positions are unknown. Such arrays exist in many real-world scenarios, and it can be regarded as a general case of the partly calibrated arrays, in which the number and positions of calibrated (or uncontaminated) sensors are known a priori. Based on the sparsity of sensor errors, we formulate the DOA estimation problem under the framework of low-rank and sparse decomposition. We develop an iteratively reweighted least squares method to …

Authors

Huiping Huang,Abdelhak M Zoubir

Published Date

2021/6/6

Robust regularized locality preserving indexing for Fiedler vector estimation

The Fiedler vector of a connected graph is the eigenvector associated with the algebraic connectivity of the graph Laplacian and it provides substantial information to learn the latent structure of a graph. In real-world applications, however, the data may be subject to heavy-tailed noise and outliers which results in deteriorations in the structure of the Fiedler vector estimate. We design a Robust Regularized Locality Preserving Indexing (RRLPI) method for Fiedler vector estimation that aims to approximate the nonlinear manifold structure of the Laplace Beltrami operator while minimizing the negative impact of outliers. First, an analysis of the effects of two fundamental outlier types on the eigen-decomposition for block affinity matrices which are essential in cluster analysis is conducted. Then, an error model is formulated and a robust Fiedler vector estimation algorithm is developed. An unsupervised penalty parameter selection algorithm is proposed that leverages the geometric structure of the projection space to perform robust regularized Fiedler estimation. The performance of RRLPI is benchmarked against existing competitors in terms of detection probability, partitioning quality, image segmentation capability, robustness and computation time using a large variety of synthetic and real data experiments.

Authors

Aylin Tastan,Michael Muma,Abdelhak M Zoubir

Journal

arXiv preprint arXiv:2107.12070

Published Date

2021/7/26

Preparations containing anthocyanins for use in the influence of cardiovascular conditions

The present invention is related to preparations containing one or more anthocyanins for use in the prevention and treatment of cardiovascular diseases and reduction of arterial stiffness in a subject.

Published Date

2021/11/25

Blind equalization via polynomial optimization

A polynomial optimization based blind equalizer (POBE) is proposed. Different from the popular constant modulus algorithm and its variants, the POBE adopts an eighth-order multivariate polynomial as the loss function. Since the loss function is sensitive to phase rotation, the POBE can achieve automatic carrier phase recovery. A gradient descent method with optimal step size is developed for solving the optimization problem. We reveal that this optimal step size is one root of a seventh-order univariate polynomial and hence, can be computed easily. Compared with the blind equalizers based on stochastic gradient descent with empirical step size, which suffers from slow convergence or even divergence, the POBE significantly accelerates the convergence rate. Moreover, it attains a much lower inter-symbol interference (ISI), resulting in a noticeable improvement of equalization performance. Simulation results …

Authors

Xue Jiang,Wen-Jun Zeng,Jiayi Chen,Abdelhak M Zoubir,Xingzhao Liu

Published Date

2021/1/18

Method for processing electronic data

A method for processing electronic data includes the steps of transforming the electronic data to a matrix representation including a plurality of matrices; decomposing the matrix representation into a series of matrix approximations; and processing, with an approximation process, the plurality of matrices thereby obtaining a low-rank approximation of the plurality of matrices.

Published Date

2021/5/18

Exploiting sparsity of ranging biases for NLOS mitigation

We study robust network localization for realistic mixed line-of-sight and non-line-of-sight (LOS/NLOS) scenarios, where (i) NLOS identification is not performed, (ii) no statistical knowledge of the LOS/NLOS measurement error is available, and (iii) no experimental campaign is affordable. We treat the bias term of each range measurement, both for LOS and NLOS, as an unknown parameter. Based on this, we indicate that the ranging biases possess a sparsity property in LOS-heavy scenarios. To exploit this sparsity, we propose the inclusion of a sparsity-promoting term into the conventional cost functions, giving rise to a generic sparsity-promoting regularized formulation. By bounding the cost function, we further develop an alternative generic bound-constrained regularized formulation. To ensure global optimality, we specify the residual error function in these formulations so that they are conveniently solved via …

Authors

Di Jin,Feng Yin,Abdelhak M Zoubir,Hing Cheung So

Journal

IEEE Transactions on Signal Processing

Published Date

2021/6/24

Sparsity-aware robust community detection (SPARCODE)

Community detection refers to finding densely connected groups of nodes in graphs. In important applications, such as cluster analysis and network modelling, the graph is sparse but outliers and heavy-tailed noise may obscure its structure. We propose a new method for Sparsity-aware Robust Community Detection (SPARCODE). Starting from a densely connected and outlier-corrupted graph, we first extract a preliminary sparsity-improved graph model where we optimize the level of sparsity by mapping the feature vectors from different communities such that the distance of their embedding is maximal. Then, undesired edges are removed and the graph is constructed robustly by detecting the outliers using the connectivity of nodes in the improved graph model. Finally, fast spectral partitioning is performed on the resulting robust sparse graph model. The number of communities is estimated using modularity …

Authors

Aylin Taştan,Michael Muma,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2021/10/1

Distributed joint detection and estimation: A sequential approach

We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on data that is observed sequentially by sensors in a distributed network. In particular, we assume the data to be drawn from a Gaussian distribution, whose random mean is to be estimated. Forgoing the need for a fusion center, the processing is performed locally and the sensors interact with their neighbors following the consensus+innovations approach. We design the test at the individual sensors such that the performance measures, namely, error probabilities and mean-squared error, do not exceed predefined levels while the average sample number is minimized. After converting the constrained problem to an unconstrained problem and the subsequent reduction to an optimal stopping problem, we solve the latter utilizing dynamic programming. The solution is shown to be characterized by a set of non …

Authors

Dominik Reinhard,Michael Fauß,Abdelhak M Zoubir

Published Date

2020/3/18

Doppler radar for the extraction of biomechanical parameters in gait analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance …

Authors

Ann-Kathrin Seifert,Martin Grimmer,Abdelhak M Zoubir

Journal

IEEE Journal of Biomedical and Health Informatics

Published Date

2020/5/13

Linear multiple low-rank kernel based stationary Gaussian processes regression for time series

Gaussian processes (GPs) for machine learning have been studied systematically over the past two decades. However, kernel design for GPs and the associated hyper-parameters optimization are still difficult, and to a large extent open problems. We consider GP regression for time series modeling and analysis. The underlying stationary kernel is approximated closely by a new grid spectral mixture (GSM) kernel, which is a linear combination of low-rank sub-kernels. In the case where a large number of the involved sub-kernels are used, either the Nyström or the random Fourier feature approximations can be adopted to reduce the required computer storage. The unknown GP hyper-parameters consist of the nonnegative weights of all sub-kernels as well as the noise variance, and they are determined through the maximum-likelihood estimation method. Two optimization methods for solving the unknown hyper …

Authors

Feng Yin,Lishuo Pan,Tianshi Chen,Sergios Theodoridis,Zhi-Quan Tom Luo,Abdelhak M Zoubir

Journal

IEEE Transactions on Signal Processing

Published Date

2020/9/9

Phase-only robust minimum dispersion beamforming

A phase-only robust minimum dispersion (PO-RMD) beamformer is devised for non-Gaussian signals. Unlike conventional beamformers that adjust the complex-valued weights, including both amplitude and phase, of each antenna to fulfill spatial filtering, the proposed PO-RMD employs a unit-modulus constraint on the weights, which is equivalent to simply phase shifting at each antenna. Instead of the widely used minimum variance criterion, the PO-RMD adopts the minimum dispersion criterion, which minimizes the ℓ p -norm of the array output to utilize the non-Gaussianity of the signals. To achieve robustness against model uncertainty, the magnitude response of any steering vector within an uncertainty region is forced to exceed the threshold. A gradient projection (GP) algorithmic framework is developed to solve the resulting nonconvex optimization problem. In order to find a feasible point in the intersection of …

Authors

Xue Jiang,Jiayi Chen,Xingzhao Liu,Abdelhak M Zoubir,Zhixin Zhou

Journal

IEEE Transactions on Signal Processing

Published Date

2020/9/23

Systems and methods for signal processing using coordinate descent techniques for unit modulus least squares (UMLS) and unit-modulus quadratic program (UMQP)

The present disclosure relates to methods and systems for signal processing using coordinate descent technique for solving technical implementation problems that are expressed as unit-modulus least squares (UMLS) and unit-modulus quadratic program (UMQP) problems. Embodiments provide for iteratively minimizing an objective function of a signal vector associated with a UMLS/UMQP problem expression over a set of coordinates of the signal vector to a convergence point. The objective function is minimized with respect to a vector element corresponding to a selected coordinate index, while other vector elements that do not correspond to the selected coordinate index are fixed. Accordingly, at each iteration, minimizing the objective function involves a solution to a one-dimensional univariate quadratic minimization. Embodiments also provide various coordinate index selection rules that include a cyclic CD …

Published Date

2020/3/3

Exploiting sparsity for robust sensor network localization in mixed LOS/NLOS environments

We address the problem of robust network localization in realistic mixed LOS/NLOS environments. We make use of the fact that the bias of range measurement errors is not only non-negative but also sparse when LOS dominates, which has been long overlooked in the existing literature. To exploit these two properties, we introduce a sparsity-promoting regularization term and relax the resulting optimization problem to a semi-definite programming (SDP) problem. The proposed method admits a neat mathematical formulation and is computationally cheap. Moreover, its global convergence is guaranteed and it achieves good robustness against NLOS measurements. In numerical results, the proposed method outperforms representative state-of-the-art SDP approaches, in terms of both localization accuracy and computational efficiency.

Authors

Di Jin,Feng Yin,Michael Fauß,Michael Muma,Abdelhak M Zoubir

Published Date

2020/5/4

A robust adaptive Lasso estimator for the independent contamination model

The Lasso has become a benchmark method for simultaneous parameter estimation and variable selection in regression analysis. It is based on the least-squares estimator and, therefore, suffers from the presence of outliers. Robust Lasso methods combine the objective function of a robust estimator with ℓ1-penalization. We address robustness for cases in which the number of observations is smaller (or not much larger) than the number of predictors. Further, we assume that the regression matrix may contain cellwise outliers. In such settings, even a few highly contaminated predictors can cause existing robust methods that are based on the commonly used rowwise contamination model to break down. Therefore, we propose a new adaptive Lasso type regularization. It takes into account cellwise outlyingness in the regression matrix and uses this information for robust variable selection. The proposed …

Authors

Jasin Machkour,Michael Muma,Bastian Alt,Abdelhak M Zoubir

Journal

Signal Processing

Published Date

2020/9/1

Copula-Based Robust Landmine Detection in Muti-View Forward-Looking GPR Imagery

We propose a scheme for detecting landmines using forward-looking ground-penetrating radar. The detector is applied to tomographic radar images obtained from multiple viewpoints of the investigation area and is based on a robust version of the likelihood-ratio test. The statistical dependence between multi-view images is incorporated via a copula-based function. The test is designed to maximize the worst-case performance over all feasible target and clutter distribution pairs, thereby eliminating the need for a strong assumption about the clutter distribution. Using numerical radar data of shallow buried targets, we demonstrate the superior performance of the proposed detector over existing approaches.

Authors

Afief D Pambudi,Fauzia Ahmad,Abdelhak M Zoubir

Published Date

2020/9/21

Minimax robust landmine detection using forward-looking ground-penetrating radar

We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using forward-looking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The LRT is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst case performance over all feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions. The proposed technique is evaluated using electromagnetic field simulation data of shallow-buried targets. We show that, compared to detectors based on parametric models, robust detectors can lead to significantly reduced false alarm rates, particularly in cases where there is a mismatch between the assumed model and the true distributions.

Authors

Afief D Pambudi,Michael Fauß,Fauzia Ahmad,Abdelhak M Zoubir

Journal

IEEE Transactions on Geoscience and Remote Sensing

Published Date

2020/2/14

Robust Matrix Completion via ℓP-Greedy Pursuits

A novel ℓ p -greedy pursuit (GP) algorithm for robust matrix completion, i.e., recovering a low-rank matrix from only a subset of its noisy and outlier-contaminated entries, is devised. The ℓ p -GP uses the strategy of sequential rank-one update. In each iteration, a rank-one completion is solved by minimizing the ℓ p -norm of the residual. Unlike the existing greedy methods that use the principal singular vectors of the residual matrix as the solution to the rank-one completion with the index information of the observed entries being ignored, the ℓ p -GP employs alternating minimization to obtain an improved solution by fully exploiting the index information. More importantly, it achieves outlier-robustness by setting p = 1. For p = 1, only computing the weighted medians is involved, which yields that the complexity is near-linear with the number of observations. The low complexity enables the ℓ 1 -GP to be applicable to very …

Authors

Xue Jiang,Abdelhak M Zoubir,Xingzhao Liu

Published Date

2020/5/4

Special Issue on Robust Multi-Channel Signal Processing and Applications: On the Occasion of the 80th Birthday of Johann F. Böhme

Special Issue on Robust Multi-Channel Signal Processing and Applications - CityU Scholars | A Research Hub of Excellence Researcher login City University of Hong Kong City University of Hong Kong City University of Hong Kong CityU Scholars A Research Hub of Excellence Home Researchers Research Units Research Output Projects Activities Prizes/Honours Student Theses Datasets Impact Press/Media Special Issue on Robust Multi-Channel Signal Processing and Applications : On the Occasion of the 80th Birthday of Johann F. Böhme Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › Editorial Preface › peer-review Overview 2 Scopus Citations Scopus Metrics View graph of relations Author(s) Abdelhak Zoubir Marius Pesavento Mohammed Nabil El Korso Hing Cheung So Xue Jiang Related Research Unit(s) Department of Electrical Engineering Detail(s) Original language English …

Authors

Abdelhak Zoubir,Marius Pesavento,Mohammed Nabil El Korso,Hing Cheung So,Xue Jiang

Journal

Signal Processing

Published Date

2020/7

A spatial inference approach for landmine detection using forward-looking GPR

We propose to detect landmines and unexploded ordnance in forward-looking ground-penetrating radar imagery by applying a spatial multiple hypothesis testing method. Homogeneous regions in an investigation area are identified based on spatial proximity and similarity of the observed decision statistics in order to discriminate targets against clutter. The proposed method is designed to control the proportion of false alarms among all pixels declared to be associated with a target. The detection performance of the proposed method is evaluated using numerical data of shallow-buried targets and compared to existing approaches.

Authors

Afief D Pambudi,Martin Gölz,Fauzia Ahmad,Abdelhak M Zoubir

Published Date

2020/9/21

Bayesian cooperative localization using received signal strength with unknown path loss exponent: Message passing approaches

We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The …

Authors

Di Jin,Feng Yin,Carsten Fritsche,Fredrik Gustafsson,Abdelhak M Zoubir

Journal

IEEE Transactions on Signal Processing

Published Date

2020/1/29

Sequential joint detection and estimation with an application to joint symbol decoding and noise power estimation

Jointly testing multiple hypotheses and estimating a random parameter of the underlying model is investigated in a sequential setup. The optimal scheme is designed such that it minimizes the expected number of used samples while keeping the probabilities of falsely rejecting a hypothesis and the mean-squared estimation errors below a pre-set level. The underlying constrained problem is first converted to an unconstrained problem and then reduced to an optimal stopping problem, whose solution is characterized by a non-linear Bellman equation. The optimal cost coefficients are obtained by exploiting a connection between the derivatives of the cost function and the detection/estimation errors. The paper concludes with a numerical example, namely solving the problem of sequential joint amplitude-shift keying symbol decoding and noise power estimation.

Authors

Dominik Reinhard,Michael Fauß,Abdelhak M Zoubir

Published Date

2020/5/4

A Compressive Sensing Approach for Single-Snapshot Adaptive Beamforming

This paper introduces a compressive sensing approach for single-snapshot adaptive beamforming. The observation data model is considered as source components in additive white noise, and then a compressive sensing formulation is introduced to estimate the parameters of the interference signals. That is, a LASSO regression problem is proposed and solved, yielding the directions as well as the powers of the interference signals. On the other hand, the noise power is estimated by means of averaging the squares of the difference between the observation data and the estimate of the source components. Finally, the interference-plus-noise covariance matrix is reconstructed and used for adaptive beamformer design. Simulation results show better performance of the proposed beamformer than several existing beamformers, in the case of a single snapshot.

Authors

Huiping Huang,Abdelhak M Zoubir,Hing Cheung So

Published Date

2020/6/8

An unsupervised approach for graph-based robust clustering of human gait signatures

Classification of gait abnormalities plays a key role in medical diagnosis, sports, physiotherapy and rehabilitation. We demonstrate the effectiveness of a new graph construction-based outlier detection method and and the applicability of a new parameter-free clustering approach on radar-based human gait signatures. Micro-Doppler radar-based human gait signatures of ten test subjects for five different gait types consisting of normal, simulated abnormal and assisted walks are clustered using five different clustering algorithms. The proposed algorithm outperforms existing methods both in cluster enumeration and partition and achieves an overall correct clustering rate of 92.8%. The developed method is promising for performing medical diagnosis in a robust unsupervised fashion.

Authors

Aylin Taştan,Michael Muma,Abdelhak M Zoubir

Published Date

2020/9/21

Supplement to “Minimax optimal sequential hypothesis tests for Markov processes.”

and (β0, β1)= β∈(0, 1) 2 characterizes the dependence structure of the process. See [12, Definition 1.1, Remark 1.2] for a formal definition and more details on the parameter β and its feasible values. The sufficient statistic of the binomial AR (1) process is given by Θn= Xn with ΩΘ= ΩX={0, 1,..., M}. In what follows, M= 7.

Authors

Michael Fauß,Abdelhak M Zoubir,H Vincent Poor

Published Date

2020

Extended cyclic coordinate descent for robust row-sparse signal reconstruction in the presence of outliers

The problem of row-sparse signal reconstruction for complex-valued data with outliers is investigated in this paper. First, we formulate the problem by taking advantage of a sparse weight matrix, which is used to down-weight the outliers. The formulated problem belongs to LASSO-type problems, and such problems can be efficiently solved via cyclic coordinate descent (CCD). We propose an extended CCD algorithm to solve the problem for complex-valued measurements, which requires careful characterization and derivation. Numerical simulation results show that the proposed algorithm is robust against outliers and has a higher empirical probability of exact recovery compared with other tested methods.

Authors

Huiping Huang,Hing Cheung So,Abdelhak M Zoubir

Published Date

2020/5/4

Dynamic pattern matching with multiple queries on large scale data streams

Similarity search in data streams is an important but challenging task in many practical areas where real-time pattern retrieval is required. Dynamic and fast updating data streams are often subject to outliers, noise and potential distortions in amplitude and time dimensions. Such conditions typically lead to a failure of existing pattern matching algorithms and to inability to retrieve required patterns from the stream. The main reason for such failures is the limitation of data normalization utilized in the majority of methods. Another reason is the lack of means to consider multiple examples of the same template to account for possible variations of the query signal. In this paper, we propose a dynamic normalization approach that allows bringing streaming signal subsequences to the scale of the query template. This significantly improves pattern retrieval capabilities, especially when sampling variance or time distortions are …

Authors

Sergey Sukhanov*,Renzhi Wu*,Christian Debes,Abdelhak M Zoubir,(*equal contribution)

Journal

Signal Processing

Published Date

2020/6/1

Globally optimal robust matrix completion based on M-estimation

Robust matrix completion allows for estimating a low-rank matrix based on a subset of its entries, even in presence of impulsive noise and outliers. We explore recent progress in the theoretical analysis of non-convex low-rank factorization problems to develop a robust approach that is based on a fast factorization method. We propose an algorithm that uses joint regression and scale estimation to compute the estimates. We prove that our algorithm converges to a global minimum with random initialization. An example function for which the guarantees hold is the pseudo-Huber function. In simulations, the proposed approach is compared to state-of the art robust and nonrobust methods. In addition, its applicability to image inpainting and occlusion removal is demonstrated.

Authors

Felicia Ruppel,Michael Muma,Abdelhak M Zoubir

Published Date

2020/9/21

Minimax optimal sequential hypothesis tests for Markov processes

Under mild Markov assumptions, sufficient conditions for strict minimax optimality of sequential tests for multiple hypotheses under distributional uncertainty are derived. First, the design of optimal sequential tests for simple hypotheses is revisited, and it is shown that the partial derivatives of the corresponding cost function are closely related to the performance metrics of the underlying sequential test. Second, an implicit characterization of the least favorable distributions for a given testing policy is stated. By combining the results on optimal sequential tests and least favorable distributions, sufficient conditions for a sequential test to be minimax optimal under general distributional uncertainties are obtained. The cost function of the minimax optimal test is further identified as a generalized f-dissimilarity and the least favorable distributions as those that are most similar with respect to this dissimilarity. Numerical …

Authors

Michael Fauss,Abdelhak M Zoubir,H Vincent Poor

Journal

The Annals of Statistics

Published Date

2020/10/1

See List of Professors in Abdelhak M. Zoubir University(Technische Universität Darmstadt)

Abdelhak M. Zoubir FAQs

What is Abdelhak M. Zoubir's h-index at Technische Universität Darmstadt?

The h-index of Abdelhak M. Zoubir has been 34 since 2020 and 50 in total.

What are Abdelhak M. Zoubir's top articles?

The articles with the titles of

Joint design of transmit precoding and antenna selection for multi-user multi-target MIMO DFRC

Corrections to “Semiparametric CRB and Slepian-Bangs Formulas for Complex Elliptically Symmetric Distributions”

Deep Unrolling Network for SAR Image Despeckling

Emergency response person localization and vital sign estimation using a semi-autonomous robot mounted SFCW radar

Transmit Waveform Design for Integrated Wideband MIMO Radar and Bi-Directional Communications

Depletion depth studies with the MALTA2 sensor, a depleted monolithic active pixel sensor

Radiation hardness of MALTA2 monolithic CMOS imaging sensors on Czochralski substrates

Asymptotically optimal procedures for sequential joint detection and estimation

...

are the top articles of Abdelhak M. Zoubir at Technische Universität Darmstadt.

What are Abdelhak M. Zoubir's research interests?

The research interests of Abdelhak M. Zoubir are: Signal Processing

What is Abdelhak M. Zoubir's total number of citations?

Abdelhak M. Zoubir has 10,797 citations in total.

What are the co-authors of Abdelhak M. Zoubir?

The co-authors of Abdelhak M. Zoubir are Ali H Sayed, Moeness G. Amin, Fredrik Gustafsson, Hing Cheung So, Visa Koivunen, Fauzia Ahmad.

    Co-Authors

    H-index: 91
    Ali H Sayed

    Ali H Sayed

    École Polytechnique Fédérale de Lausanne

    H-index: 88
    Moeness G. Amin

    Moeness G. Amin

    Villanova University

    H-index: 73
    Fredrik Gustafsson

    Fredrik Gustafsson

    Linköpings Universitet

    H-index: 68
    Hing Cheung So

    Hing Cheung So

    City University of Hong Kong

    H-index: 62
    Visa Koivunen

    Visa Koivunen

    Aalto-yliopisto

    H-index: 48
    Fauzia Ahmad

    Fauzia Ahmad

    Temple University

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