A single-user cellular communication system between a base station (BS) and a mobile user equipment (UE), assisted by intelligent reflecting surfaces is considered. The goal is to estimate the individual channels, i.e., BS-IRS-2, IRS-2-IRS-1 and UE-IRS-1. We propose a two-phase channel estimation (CE) algorithm wherein the BS-IRS-2 and IRS-1-IRS-2 channels are estimated simultaneously by utilizing the sparsity offered by mmWave channels in phase-1. In phase-2, in order to track the time-varying channel UE-IRS-1, we consider a channel model involving dynamic sparsity, and estimate it by using the dynamic turbo orthogonal approximate message passing (D-TOAMP) and dynamic compressive sensing AMP (DCS-AMP). Alternatively, we use Kalman-like filter (KLF) followed by Kalman filter (KF) to track UE-IRS-1 channel. Numerical results of phase-1 and phase-2 (for D-TOAMP, DCS-AMP) demonstrate the performance of our CE algorithm for double IRS assisted communication in terms of estimation error, computation time, and also provide a guideline for the choice of the appropriate version of the algorithm in each phase. Also, simulations of phase-2 (under KLF, KF) demonstrate the efficacy of our proposed KLF-KF algorithm over the Unscented Kalman filter (UKF)-KF in terms of estimation error.

A document by Hassan Zarrar . Click on the document to view its contents.

Quantum Machine Learning (QML) is an emerging technology that only recently has begun to take root in the research fields of Earth Observation (EO) and Remote Sensing (RS),  and whose state of the art is roughly divided into one group oriented to fully quantum solutions, and in another oriented to hybrid solutions.  Very few works applied QML to EO tasks, and none of them explored a methodology able to give guidelines on the hyperparameter tuning of the quantum part. As a first step in the direction of quantum advantage for RS data classification, this letter opens new research lines, allowing us to demonstrate that there are more convenient solutions to simply increasing the number of qubits in the quantum part. To pave the first steps for researchers interested in the above, the structure of a new hybrid quantum neural network is proposed with a strategy to choose the number of qubits to find the most efficient combination in terms of both system complexity and results accuracy. We sampled and tried a number of configurations, and using the suggested method we came up with the most efficient solution (in terms of the selected metrics). Better performance is achieved with less model complexity when tested and compared with state-of-the-art techniques for identifying volcanic eruptions  chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a major problem when training classical models. Lastly, the code is freely available so that interested researchers can  reproduce and extend the results.

Energy transition is promoted by the international and scientific community to reduce society’s carbon footprint and fight against climate change. In this context, power systems are increasingly equipped with distributed energy resources (DER) and variable energy resources (VER). Because of the variability introduced by these resources and power electronics converters, serving as the interface between them and AC power system, power systems operators are facing more challenges to maintain network reliability. Even if most of DERs are connected to distribution system, their integration will also impact the transmission system behavior. Furthermore, the utilities must ensure a better coordination and communications of the actions taken by the transmission system operator (TSO) and distribution system operator (DSO). In the context of end-to-end electricity system transition, development, and integration, the main objective of this transversal study is to analyze the content of published work. The aim is to target the new challenges to solve in order to maintain the reliability of the transmission and distribution power systems. The analysis is based on a bibliometric study of the Scopus database for published documents from 2011 to 2020. By using of statistical methods, a quantitative analysis is done on the one hand to highlight the evolution of science in this specific research area and on the other hand to identify current and future trends and challenges.

Liquid Crystal-based mm-wave spatially fed antennas with electronic reconfiguration are a promising solution at the higher frequencies required in next generation networks. However, one of the main drawbacks of the technology in these bands stems from the high reconfigurability times they present. Through this work, a relevant step towards overcoming the temporal problem by using Dual Frequency Liquid Crystals (DFLC) is presented. This paper details, for the first time, both the electromagnetic and temporal characterization of four commercially available DFLC mixtures in W-band, enabling their use in designing faster devices. To evaluate the experimental characterization, a reflectarray surface (made of 50x50 cells) specifically designed to achieve fast switching times with a sufficient phase range has been manufactured and measured. For this cell, a preliminary addressing technique based on overdriving has been used, exhibiting reconfigurability (rise and decay) times of 20 ms, one order of magnitude faster than the current state of the art of LC-based mm-wave planar devices. The measured results match the simulations, and reveal that a precisely designed biasing technique using overdrive must be used for DFLC-cells to achieve time reduction. Additionally, the benefits of this technology compared with other LC acceleration strategies in mm-wave are discussed.

Feline soft tissue sarcoma (STS) and injection site sarcoma (fISS) are rapidly growing tumors with low metastatic potential, but locally aggressive behavior. Histotripsy is a non-invasive focused ultrasound therapy using controlled acoustic cavitation to mechanically disintegrate tissue. In this study, we investigated the in vivo safety and feasibility of histotripsy to treat fISS using a custom 1 MHz transducer. Three cats with naturally-occurring STS were treated with histotripsy before surgical removal of the tumor 3 to 6 days later. Gross and histological analyses were used to characterize the ablation efficacy of the treatment, and routine immunohistochemistry and batched cytokine analysis were used to investigate the acute immunological effects of histotripsy. Results showed that histotripsy ablation was achievable and well-tolerated in all three cats. Precise cavitation bubble clouds were generated in all patients, and hematoxylin & eosin stained tissues revealed ablative damage in targeted regions. Immunohistochemical results identified an increase in IBA-1 positive cells in treated tissues, and no significant changes in cytokine concentrations were identified post-treatment. Overall, the results of this study demonstrate the safety and feasibility of histotripsy to target and ablate superficial feline STS and fISS tumors and guide the clinical development of histotripsy devices for this application.

The emerging QUIC protocol provides efficient migration of IP addresses in a device without breaking the existing connection. This mechanism has been exploited in MIMIQ to provide user privacy and obstruct traffic analysis attacks. The frequent change of a device’s IP address thwarts traffic analysis attacks, and hinders the derivation of user behavioral patterns. This protocol can be useful for ISPs to provide privacy-preserving telecom services. In the original paper of MIMIQ, the authors demonstrate a proof-of-concept (POC) implementation on a single switch. This is a simplified example that is not scalable for real-world applications. In this project, we aim to implement MIMIQ on a user application. To make it easier to adapt, we implement it as a plug-in service on popular QUIC implementations. In the original POC, the address allocation scheme was naive and detailed analysis can reveal the user identity. In this article, we study the security properties of MIMIQ and propose an efficient, secure, and scalable address allocation mechanism.

In our previous work on image reconstruction for single-layer collimatorless scintigraphy, we introduced the min-min weighted robust least squares (WRLS) optimization algorithm to address the challenge of reconstructing images when both the system matrix and the projection data are uncertain. Whereas the WRLS algorithm has been successful in two-dimensional (2D) reconstruction, expanding it to three-dimensional (3D) reconstruction is difficult since the WRLS optimization problem is neither smooth nor strongly-convex. To overcome these difficulties and achieve robust image reconstruction in the presence of system uncertainties and projection noise, we propose a generalized iterative method based on the maximum likelihood expectation maximization (MLEM) algorithm, hereinafter referred to as the Masked-MLEM algorithm. In the Masked-MLEM algorithm, only selected subsets (“masks”) in the system matrix and the projection contribute to the image update to satisfy the constraints imposed by the system uncertainties. We validate the Masked-MLEM algorithm and compare it to the standard MLEM algorithm using data from both collimated and uncollimated imaging instruments, including parallel-hole collimated SPECT, 2D collimatorless scintigraphy, and 3D collimatorless tomography. The results show that the Masked-MLEM and standard MLEM reconstructions are similar in SPECT imaging, while the Masked-MLEM algorithm outperforms the standard MLEM algorithm in collimatorless imaging. A good choice of system uncertainty can make the Masked-MLEM reconstruction more robust than the standard MLEM reconstruction, effectively reducing the likelihood of reconstructing higher activities in regions without radioactive sources.

A wireless, wearable magnetic eye tracker is described and characterized. The proposed instrumentation enables simultaneous evaluation of eye and head angular displacements. Such a system can be used to determine the absolute gaze direction as well as to analyze spontaneous eye re-orientation in response to stimuli consisting in head rotations. The latter feature has implications to analyze the vestibulo-ocular reflex and constitutes an interesting opportunity to develop medical (oto-neurological) diagnostics. Details of data analysis are reported together with some results obtained in-vivo or with simple mechanical simulators that enable measurements under controlled  conditions.

Microservice-based architecture is a relatively new paradigm of software development that has gained tremendous popularity recently. A microservice-based application comprises multiple relatively small independent functional components (microservices) that interact to accomplish a sophisticated function. Microservices enable agile development and deployment of applications and enhanced scalability and resiliency. However, microservices-based systems have more significant security concerns than traditional monolithic systems. In this article, we discuss the various security loopholes of a microservice-based architecture and how these diverse components increase the attack surface of the aggregate framework. This article also proposes a machine learning (ML) based behavioral analysis framework that analyzes the network traffic and API calls to detect flaws and vulnerabilities in the microservice architecture to overcome these challenges. Prior research has demonstrated the potential of network monitoring to secure microservice-based cloud applications. However, they used hand-designed policies to enforce security compliance. Manually designed policies have their drawbacks. This article discusses these challenges and proposes a novel ML-based pattern recognition to automate the manual definition of policies. ML-based attack detection techniques have achieved stateof-the-art performance in various cybersecurity applications like malware detection and vulnerability detection. However, security monitoring of microservice-based applications is still a developing research field that has not experienced the impact of ML yet. This article proposes implementing supervised ML-based security monitoring of microservice-based applications based on Seeker to detect vulnerability exploits in real-time.

Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial approach, in which a convolution or message-passing operation is conducted based on a hypergraph algebraic descriptor. While many HyperGNNs have been proposed and achieved state-of- the-art performance on broad applications, there have been limited attempts at exploring high dimensional hypergraph descriptors (tensors) and joint node interactions carried by hyperedges. In this paper, we depart from hypergraph matrix representations and present a new tensor-HyperGNN framework (T-HyperGNN) with cross-node interactions. The T-HyperGNN framework consists of T-spectral convolution, T-spatial convo- lution, and T-message-passing HyperGNNs (T-MPHN). The T- spectral convolution HyperGNN is defined under the t-product algebra that closely connects to the spectral space. To im- prove computational efficiency for large hypergraphs, we localize the T-spectral convolution approach to formulate the T-spatial convolution and further devise a novel tensor message-passing algorithm for practical implementation by studying a compressed adjacency tensor representation. Compared to the state-of-the- art approaches, our T-HyperGNNs preserve intrinsic high-order network structures without any hypergraph reduction and model the joint effects of nodes through a cross-node interaction layer. These advantages of our T-HyperGNNs are demonstrated in a wide range of real-world hypergraph datasets.

Cooperative control involves developing control strategies for individual robots that guarantee synchronized behavior of the states of all the robots in a team in some prescribed sense. This work presents a novel controller that achieves formation control for a group of differential-drive robots. First, we propose a nonlinear feedback control law that guarantees stable tracking of a reference trajectory for a single robot without exceeding the velocity limits of the robot. Using Lyapunov analysis, we obtain the necessary conditions on the control parameters and establish ultimate boundedness on error terms. Next, we formulate the formation control problem as a trajectory tracking problem for the multi-robot system and solve it using the proposed controller. Additionally, we provide constraints on formation size for a planned reference trajectory, ensuring smooth cornering of multi-robot formation without exceeding actuation limits.

Quantization of synaptic weights using emerging non-volatile memory devices has emerged as a promising solution to implement computationally efficient neural networks on resource constrained hardware. However, the practical implementation of such synaptic weights is hampered by the imperfect memory characteristics, specifically the availability of limited number of quantized states and the presence of large intrinsic device variation and stochasticity involved in writing the synaptic state. This article presents on-chip training and inference of a neural network using quantized magnetic domain wall (DW) based synaptic array and CMOS peripheral circuits.  The quantization of the synaptic device offers a balanced neural network performance in terms of accuracy, power, and area. A rigorous model of the magnetic DW device considering stochasticity and process variations has been utilized for the synapse. To achieve stable quantized weights, DW pinning has been achieved by means of physical constrictions. Finally, VGG8 architecture for CIFAR-10 image classification has been simulated by using the extracted synaptic device characteristics. The performance in terms of accuracy, energy, latency, and area consumption has been evaluated while considering the process variations and non-idealities in the DW device as well as the peripheral circuits. The proposed quantized neural network architecture achieves efficient on-chip learning with 92.4% and 90.4 % training and inference accuracy, respectively. In comparison to pure CMOS based design, it demonstrates an overall improvement in area, energy, and latency by 13.8×, 9.6×, and 3.5×, respectively.

Object detection models commonly focus on utilizing the visible spectrum via Red-Green-Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) (7.5 - 13.5 µm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and LWIR-RGB fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation finding that a blended RGB-LWIR model generally exhibits superior performance compared to traditional RGB or LWIR approaches. For example, an RGB-LWIR blend only performed 1-5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlayed provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms. This approach has the ability to improve object detection performance for a range of use cases in industrial, consumer, government, and military applications. Finally, this research additionally contributes a novel open labeled training dataset of 6,300 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from air-based platforms, enabling further multispectral machine-driven object detection research.

For autonomous driving, it is important to develop safe and efficient decision algorithms to handle multi-vehicle interactions. Game theory is suitable to manage the interactive driving decision modelling, however, common approaches of multi-player game formulation is computationally complex for dynamic and intense interactions. The main contributions of this work are two-fold: 1) a global sorting-local gaming framework, namely GLOSO-LOGA, is proposed to solve the intersection interaction problem for autonomous driving, which can comprehensively consider the advantages of multi-vehicle collaboration and single-vehicle intelligence approaches; 2) an interaction disturbance function is used to quantify the impact of indirect interactions on ego vehicle. To validate the algorithm performances, corner case simulations and human-in-the-loop simulator experiments are carried out, in which a four-armed intersection scenario with various urgent and challenging interaction conditions is used. Compared to a traditional approach that decomposes a multi-vehicle game into multiple two-vehicle games, the proposed algorithm can improve both safety and traffic efficiency in intensively interactive driving scenarios.

The discontinuous reception mechanism is modeled as a single Markov decision process. The MDP is solved using value iteration and a method is proposed to deploy the optimal DRX timer in a heterogeneous network.

Smart grid technologies have been transforming the power grid operation paradigms by integrating smart sensing devices, advanced communication networks, and powerful computing resources. In addition, data-driven applications have significantly increased in recent years, accelerating the use of smart sensors, such as phasor measurement units (PMU), in power grid monitoring. It necessitates a well-functioning communication network (CN) for PMU measurement data transfer to the control center even in the event of failures. This paper proposes a PMU network routing algorithm to ensure data transfer for control center’s resilient observability to the power grid. The interdependent roles of PMUs in power grid observability is first identified based on the power grid topology. Then, a failure-tolerant routing algorithm is proposed to find data transfer paths in the CN that meets the power grid monitoring needs. The resultant routing paths ensure resilience against single link failure, where the resilience is defined in terms of grid observability. Besides, a cost metric is defined to minimize end-to-end delay in the network to facilitate real-time data transfer. Simulation results verify the superiority of the proposed routing algorithm compared with conventional fault-tolerant routing algorithms that are agnostic to the domain knowledge of power grid observability.

A novel portable perturbation-based balance training device using a mecanum wheel to perturb in eight directions.