Abdelhak M Zoubir

Technische Universität Darmstadt | TU · Department of Electrical Engineering and Information Technology (Dept.18)

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 achie...

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 resc...

Errors in [1] are corrected below. 1. In Eq. (17), $\mathrm{vecs}(\boldsymbol{\Sigma}_{0})$ should be $\mathrm{vec}(\boldsymbol{\Sigma}_{0})$. 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}_{\bold...

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....

The Fiedler vector is the eigenvector associated with the algebraic connectivity of the graph Laplacian. It is central to graph analysis as 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 deteriorate the structure of t...

p>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...

p>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...

p>To alleviate the bias generated by the $\ell_1$-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 a...

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 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...

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 ra...

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 i...

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 co...

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. 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 unde...

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...

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 rob...

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 s...

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 rob...

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...

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 formu...

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 resc...

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...

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 D...

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-t...

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 po...

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...

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...

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 po...

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....

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 itera...

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...

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...

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 inheritin...

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 $2 \times 10^{15}~1~\text{MeV}~\text{n}_{eq}/\text{cm}^2$ and time resolution b...

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 MAL...

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...

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 itera...

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 MAL...

In this letter, 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 consensus-ADMM converges under a mild condition, namely, the augmented Lagrangian parameter is chosen to be sufficiently large. It is shown that...

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...

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...

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...

We recently proposed a novel approach to perform spatial inference using large-scale sensor networks and multiple hypothesis testing. 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-proce...

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...

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 env...

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...

The problem of off-grid direction-of-arrival (DOA) estimation is investigated in this paper. 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 fram...

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 multi-view images into the test via c...

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 env...

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 eac...

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 structur...

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...

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 sens...

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...

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...

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. Ba...

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 con...

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 i...

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...

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...

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...

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 con...

This is the presentation slide of the paper "Copula-Based Robust Landmine Detection in Multi-View Forward-Looking GPR Imagery". The paper was awarded second place in the student paper competition at the 2020 IEEE Radar Conference in Florence, Italy.

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...

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-b...

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 clutte...

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 sol...

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 rig...

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 rig...

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 c...

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....

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 test minimizes the expected number of samples while ensuring that the average detection/estimation er...

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 per...

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 per...

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 th...

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 message...

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 t...