In this study, we solved a noisy spatiotemporal spike pattern detection task on an analog neuromorphic chip using an unsupervised learning rule. Spike-timing-dependent plasticity (STDP) is the most widespread unsupervised learning rule implemented in Spiking Neural Networks (SNNs) and neuromorphic chips. It has been shown to perform well in conventional benchmark tasks such as spike pattern classification and image classification in SNN simulations. However, a significant performance gap exists between its ideal model simulation and neuromorphic implementation. The learning rate of STDP learning depends on the resolution of synaptic efficacy, high resolution efficacy leads to a small learning rate and stable performance. In computer simulation, synaptic efficacy is configured using 64-bit floating-point precision whereas in low-power neuromorphic chips the resolution is generally restricted to under 5-bit fixed point precision due to silicon area and power constraints. This leads to a degradation in the performance. To solve this problem we proposed a bioinspired learning rule named adaptive STDP learning in a previous study and demonstrated via numerical simulation that the performance of adaptive STDP learning  (using 4-bit fixed point synapses) is similar to STDP learning (using 64-bit floating-point precision) in a noisy spatiotemporal spike pattern detection task. In this study, we present the experimental results for the same. The experimental results are similar to those obtained in our simulation-based study. To our best knowledge, this is the first time that an unsupervised, noisy spatiotemporal spike pattern detection task has been demonstrated to perform well on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy.

For antenna measurements, the device under test (DUT) is usually transported to an anechoic chamber equipped with a scanner system. To measure on-site, unmanned aerial vehicles (UAVs) are a flexible solution as they can precisely maneuver a probe antenna over a surface in the DUT’s near-field region. In this work, the designed multi-rotor UAV enables electromagnetically optimal placement of the probe antenna and effective gravity center re-balacing by payload position adjustment. The on-board software-defined radio (SDR) serves as a dual-channel receiver for the dual-polarized probe and as a signal source. For phase-coherent measurements, the source signal is transmitted to the DUT via an optical fiber tethering the UAV to the ground control station. Power supply cables allow unlimited flight times. A laser-based tracking system originating from virtual reality (VR) applications measures the probe position and orientation. The developed operator software guides the UAV along a trajectory and records the irregularly distributed near-field samples. An inverse equivalent sources solver (IESS) with full probe correction computes equivalent sources for antenna diagnostics and the DUT far field. The deployed components make the system very cost-effective. Still, verification measurements demonstrate its usability and the accuracy of the results for frequencies up to several GHz.

Optical full-field recovery makes it possible to compensate fiber impairments such as chromatic dispersion and polarization mode dispersion (PMD) in the digital signal processing. Coherent detection dominates over the long-haul transmission system due to its high spectral efficiency and tolerance against optical impairments. For cost-sensitive short-reach optical networks, some advanced single-polarization (SP) optical field recovery schemes are recently proposed to avoid chromatic dispersion-induced power fading effect, and improve the spectral efficiency for larger potential capacity. Polarization division multiplexing (PDM) can further double both the spectral efficiency and the system capacity of these SP carrier-assisted direct detection (DD) schemes. However, the so-called polarization fading phenomenon induced by random polarization rotation is a fundamental obstacle to exploit the polarization diversity for carrier-assisted DD systems. In this paper, we propose a receiver of Jones-space field recovery (JSFR) to realize polarization diversity with SP carrier-assisted DD schemes in the Jones space. There different receiver structures of proposed JSFR and the simplification for JSFR are explored theoretically. The proposed JSFR pushes the SP DD schemes towards PDM without no extra optical signal-to-noise ratio (OSNR) penalty. In addition, the JSFR shows good tolerance to PMD since the optical field recovery is conducted before polarization recovery. In the concept-of-proof experiment, we demonstrate 448-Gb/s reception over 80-km single-mode fiber using the proposed JSFR based on 2×2 couplers. Qualitatively, we compare the optical field recovery in the Jones space and Stokes space from the perspective of the modulation dimension.

In this paper, we introduce new distributed diffusion algorithms to track a sequence of hidden random matrices that evolve on the special orthogonal group. The algorithms are based on the Adapt-then-Combine and the Random Exchange methods, and diffuse Gaussian approximations of posterior densities computed in the Lie algebra of the special orthogonal group. Simulation results show that, in a scenario with strongly nonlinear observation functions, the proposed algorithms perform similarly to the centralized particle filter estimator and can outperform competing Extended Kalman Filters.

Power distribution grids are commonly controlled through centralized approaches, such as the optimal power flow. However, the current pervasive deployment of distributed renewable energy sources and the increasing growth of active players, providing ancillary services to the grid, have made these centralized frameworks no longer appropriate. In this context, we propose a novel noncooperative control mechanism for optimally regulating the operation of power distribution networks equipped with traditional loads, distributed generation, and active users. The latter, also known as prosumers, contribute to the grid optimization process by leveraging their flexible demand, dispatchable generation capability, and/or energy storage potential. Active users participate in a noncooperative liberalized market designed to increase the penetration of renewable generation and improve the predictability of power injection from the high voltage grid. The novelty of our game-theoretical approach consists in incorporating economic factors as well as physical constraints and grid stability aspects. Lastly, by integrating the proposed framework into a rolling-horizon approach, we show its effectiveness and resiliency through numerical experiments. Postprint accepted for publication in IEEE Transactions on Control of Network Systems How to cite:  P. Scarabaggio, R. Carli and M. Dotoli, (2022)  “Noncooperative Equilibrium Seeking in Distributed Energy Systems Under AC Power Flow Nonlinear Constraints,” in IEEE Transactions on Control of Network Systems. DOI: https://doi.org/10.1109/TCNS.2022.3181527 © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This letter presents a concept for a surface exhibiting tunable reflectivity that operates within the K-band using varactor diodes. Thereby, the impedance of a two-layer metasurface is controlled through lumped components at its back side and not from the front, where discrete elements would scatter the incident wave. A reflector's design, verification, and modelling through an equivalent circuit as well as full-wave simulations are presented in detail. Finally, the tunable reflection parameters of the surface as well as its reflection beam pattern under different biasing conditions are shown.

The proliferation of the Internet of Things (IoT) devices and advances in their computing capabilities give an impetus to the Edge Computing (EC) paradigm that can facilitate localize computing and data storage. As a result, limitations like network connectivity issues, data mobility constraints, and real-time processing delays, in Cloud computing can be addressed more efficiently. EC can create a lot of opportunities across the breadth of the IT domains and cyber-physical systems. Several studies have been conducted describing EC general requirements, challenges, and issues. However, considering the complexity involved in the EC paradigm, non-functional requirements (NFRs) are equally important as functional requirements, to be thoroughly investigated. This paper discusses NFRs, namely, performance, reliability, scalability, and security that can assist in maturing the EC paradigm. To accomplish the objective, available case studies and the state-of-the-art related to non-functional requirements, real-world issues, and challenges concerning EC are reviewed. Ultimately, the paper anatomizes the aforementioned NFRs leveraging the six-part scenario form of source-stimulus-artifact-environment-response-response measure to assert Quality of Service (QoS) in EC.

The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things (IIoT), automotive, robotics, etc. In this paper, we make the first attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.

It is well-known that probabilistic constellation shaping (PCS) provides a shaping-gain of 1.53dB asymptotically as signal-to-noie (SNR) increases. This is however, under ideal assumptions that the considered system operates in optimal sense and can achieve the Shannon capacity. While in practice the system can operate below the capacity, and in particular when block-error rate (BLER) and throughput are considered which are the most relevant merits, the benefits of PCS are unclear. In this paper, we propose a PCS-transceiver for the fifth-generation new-radio (5G-NR) that supports quadrature-amplitude-modulation (QAM) constellations shaped with PCS, namely, PCS-QAM. We put a special interest in the throughput achieved under a stringent BLER constraint, and validate the effectiveness of PCS with practical detection and decoding algorithms, in comparison to conventional uniform QAM constellations, namely, Uniform-QAM. We also analyze the properties of power-gain, entropy-loss, and peak-to-average power-ratio (PAPR), in connections to the PCS design. Further, we derive a necessary and sufficient condition for a PCS-QAM to outperform a Uniform-QAM in throughput, and prove that the normalized entropy-loss with the PCS-QAM must be less than the BLER obtained with the Uniform-QAM. Furthermore, we demonstrate that the PCS-transceiver is flexible in rate-adaption without affecting the encoder, and yields a better throughput-envelope by mitigating the SNR-gaps between two adjacent modulation and coding scheme (MCS) indexes in 5G-NR.

Jamming attacks significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. Although different jamming techniques are discussed in the literature, numerous open-source implementations have used expensive equipment in the range of thousands of dollars with the exception of a few. These implementations have also tended to be partial band, and do not cover the whole available bandwidth of the system under attack. In this work, we demonstrate that flexible, reliable, and low priced software-defined radio (SDR) jamming is feasible by designing and implementing different types of jammers against IEEE 802.11n networks. First, to demonstrate the optimal jamming waveform, we present an analytical bit error rate expression of the system under attack by employing two common jamming waveforms: Gaussian noise and digitally modulated. Then, we validate this analysis through simulations using the MATLAB WLAN toolbox. Afterwards, we implement JamRF, a toolkit that employs a low-cost SDR to implement numerous types of jammers to validate the analysis. Obtained results showed that, to jam the whole 2.4GHz spectrum, a stateful-reactive jammer employing random channel hopping jamming strategy, achieves a packet loss ratio above 90%.

We study a variant of a robust description source coding framework via its corresponding characterization, which is a relevant model for goal-oriented semantic information transmission. Considering two individual single-letter separable distortion constraints and input and output data acting as the intrinsic and extrinsic message, respectively, we first derive bounds on the optimal rates of the problem, as well as necessary and sufficient conditions for these bounds to be tight. Subsequently, we prove a general result that provides in parametric form the optimal solution of the characterization of this problem. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where a Hamming and an erasure distortion coexist, as a means to highlight the importance of selecting the type of the distortion constraint in goal-oriented semantic communication. We also develop a semantic-aware Blahut-Arimoto (BA) algorithm, which can be used for the computation of any finite alphabet intrinsic or extrinsic message under individual distortion criteria. Finally, we revisit the problem for multidimensional independent and identically distributed (IID) jointly Gaussian processes with individual mean-square error (MSE) distortion constraints, providing new insights that have previously been overlooked. This work reveals the cardinal role of context-dependent fidelity criteria in goal-oriented semantic communication.

We present a direct solution to the problem of constructing a stochastic matrix with prescribed eigenspectrum, widely referred to as the stochastic inverse eigenvalue problem. The solution uses Markov state disaggregation to construct a Markov chain with stochastic transition matrix possessing the required eigenspectrum. Existing solutions that follow the same approach are limited to constructing matrices with real-valued eigenspectra only. The novel solution directly constructs matrices with complex-valued eigenspectra by applying a new disaggregation technique in tandem with a technique from a previous solution. Due to this generalization, the novel solution is able to successfully model physical systems from a larger family. Furthermore, the novel solution constructs the matrix in a finite and predetermined number of iterations, and without numerical approximation. The solution is demonstrated by deriving an expression for a set of 4 x 4 stochastic matrices sharing the same prescribed complex-valued eigenspectrum and indexed by a real parameter.

Recently, a necessary and sufficient condition for multivaluedness to be implicitly exhibited by counter-cascaded systems was presented. Subsequently, several systems that exhibit multivaluedness were reported. This brief interprets a general information transmission system as a counter-cascaded system with Shannon's noisy-channel coding theorem providing the necessary and sufficient conditions for multivaluedness and is therefore a particular instance of the counter-cascaded network framework.

Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet). From both the environmental and economical perspectives, non-homogeneous QoS demands obstruct the minimization of the energy footprints and operational costs of the envisioned robust networks. As such, network intelligentization is expected to play an essential role in the realization of such sophisticated aims. The fusion of artificial intelligence (AI) and mobile networks will allow for the dynamic and automatic configuration of network functionalities. Machine learning (ML), one of the backbones of AI, will be instrumental in forecasting changes in network loads and resource utilization, estimating channel conditions, optimizing network slicing, and enhancing security and encryption. However, it is well known that ML tasks themselves incur massive computational burdens and energy costs. To overcome such obstacles, we propose a novel layer-based HetNet architecture which optimally distributes tasks associated with different ML approaches across network layers and entities; such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency via collaborative learning and communications.

Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however non-trivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analystics perspective. Thus, it is imperative to carry out an overview of tensor tracking to help researchers and practitioners understand its development and achievements, summarise the current trends and advances, and identify challenging problems. In this article, we provide a contemporary and comprehensive survey on different types of tensor tracking techniques. We particularly categorize the state-of-the-art methods into three main groups: streaming CP decompositions, streaming Tucker decompositions, and streaming decompositions under other tensor formats (i.e., tensor-train, t-SVD, and BTD). In each group, we further divide the existing algorithms into sub-categories based on their main optimization framework and model architectures. Finally, we present several research challenges, open problems, and potential directions of tensor tracking in the future.

While stroke survivors with moderate or mild impairment are typically able to open their hand at will, those with severe impairment are not. Abnormal synergies govern the arm and hand in stoke survivors with severe impairment, so hand opening, which is required to overcome the working synergy, is a task extremely difficult for them to achieve. It is universally accepted that alternative tracts including the cortico-reticulospinal tract (CRST), employed in the case that the corticospinal tract (CST) is damaged by stroke, brings about such abnormal synergies. Here we note that hand closing is enabled by alternative tracts as well as the CST, and raise a research question: does motor characteristics while closing the hand depend on the integrity of the CST? In this study, we evaluate the ability of 17 stroke survivors to flex and relax the metacarpophalangeal (MCP) joints and investigate whether motor characteristics can be distinguished based on CST integrity which is estimated using upper-extremity Fugl-Meyer (UEFM). UEFM scores have been perceived as an indirect indicator of CST integrity. We found that participants with the UEFM score above a certain value, who are assumed to use the CST, moves the MCP joints more smoothly (p<0.05) and activates the flexor to flex the joints faster (p<0.05), in comparison to participants with low UEFM scores, who are assumed to use alternative tracts. The results present evidence that use of alternative tracts (i.e. the CRST) results in a degradation in movement smoothness and slow activation of the MCP flexor.

The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory National Solar Radiation Database Spectral on Demand (NSRDB-S) satellite-based spectral irradiance products are tested here against benchmark data and models at seven ground stations: one in Spain for CM-SAF SRI and six in North America for NSRDB-S. Benchmarks include WISER spectroradiometers, spectra modeled from SolarSIM-G measurements and the SMARTS radiative code with two alternate input sources: AErosol RObotic NETwork (AERONET) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. The satellite products are tested in terms of their ability to estimate photovoltaic (PV) spectral effects for six PV module technologies. The spectra are also compared directly under clear-sky conditions. Both CM-SAF SRI and NSRDB-S outperform the simple benchmark of neglecting spectral effects in terms of predicting instantaneous spectral mismatch factors, but only CM-SAF SRI generally does better at predicting long-term spectral derate factors. The clear-sky results reveal systematic differences between NSRDB-S and benchmark spectra, likely due to the NSRDB-S treatment of aerosols. Meanwhile, the mean SMARTS spectra with AERONET and MERRA-2 inputs are in good agreement, showing promise for the use of MERRA-2 as input to clear-sky models.

The unavailability of a reliable and affordable gait assessment system for quantifying gait abnormality is responsible for the slow and inaccurate diagnosis of gait disorders. This study explores the feasibility of using a low-cost lower limb kinematics estimation device for clinical applications. The low-cost lower limb kinematics estimation device used in the study consists of an instrumented outsole (IO), an instrumented insole (II), and an artificial intelligence model (foot2hip). The low-cost system was compared with the standard system for reconstruction errors. Four analyses were performed to understand the nature of performance variability concerning clinically relevant parameters, including automated-gait assessment score (A-GAS). The study’s results suggest that the proposed system is unsuitable for detailed gait analysis (in the swing phase). However, the device can be used for personal use for tracking global gait abnormalities, and for clinical use in the screening of patients. The results of the analyses will help the users of the proposed system in framing the optimum data collection protocol.