Abstract: Two recursive least-square (RLS) adaptive filtering algorithms are most often used in practice, the exponential and sliding (rectangular) window RLS algorithms. This popularity is mainly due to existence of low-complexity versions of these algorithms. However, these two windows are not always the best choice for identification of fast time-varying systems, when the identification performance is most important. In this paper, we show how RLS algorithms with arbitrary finite-length windows can be implemented at a complexity comparable to that of the exponential and sliding window RLS algorithms. Then, as an example, we show an improvement in the performance when using the proposed finite-window RLS algorithm with the Hanning window for identification of fast time-varying systems. p, li { white-space: pre-wrap; }

With recent advances in the field of IoT (Internet of Things), the quantity of data being generated by various sensors and Internet users has increased dramatically which in turn has skyrocketed the need for efficient data compression methods. Data summarization is an efficient and effective technique for data compression that can generate a brief and succinct summary from typically larger quantities of data in an intelligent and highly useful manner, which can be done at various levels of abstraction. The impact of using such a technique in large IoT networks can be significantly advantageous in terms of reduction in the processing time, overall computation, data storage-transmission requirements, energy consumption and possible workload on IoT users. In this work, a review of existing methods for summarization techniques at various levels of abstraction of typical IoT networks are discussed. The levels of categorization that are considered are Low-level and High-level. Under each abstraction level, various techniques are further classified while briefly describing their essential characters.

The performance of most array signal processing tasks relies on the presence of correlation between sensor signals. In a wireless sensor network, where sensor nodes are spread out over a relatively large area, it is useful to identify nodes observing similar sensor signals and hence common phenomenons, for example to partition the network according to the observed latent signals and corresponding correlation structure. This can be achieved via the so-called MAXVAR formulation of generalized canonical correlation analysis, which finds a low-dimensional subspace that highlights correlated signal components between multiple nodes' observed signal subspaces. The classical procedure for computing the solutions of MAXVAR consists in performing a generalized eigenvalue decomposition after collecting all the sensors' signals at a fusion center. However, this typically incurs high communication and computational costs. In this paper, we describe a low communication and computational cost distributed algorithm that computes the solutions of MAXVAR without aggregating the nodes' observations at a central location. We show how a subset of those solutions can be used locally by each node to estimate the global correlation structure across all nodes in the network, thereby allowing any node to evaluate the presence of correlated signals at any other node, even if no direct link is shared. We prove the convergence of the algorithm and validate our method for estimating the correlation structure via simulations.

Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low-signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for 5G MaMIMO channel estimation. After a first preliminary model with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low-SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.

This work reports a compact behavioral model for the hysteretic conduction characteristics of Al/Gd0.1Ca0.9MnO3(GCMO)/Au resistive switching devices suitable for SPICE simulations. The devices are nonvolatile, forming-less, compliance-free, and self-rectifying multistate memristive structures which makes them of maximum interest for neuromorphic computing and memory applications. The proposed model relies on two coupled equations, one for the electron transport and a second one for the vacancy displacement. The proposed model considers a novel approach for solving the internal state of the device based on the so-called generalized quasi-static hysteron whose application can be extended to other structures and dynamics in addition to the ones discussed here.

The fifth-generation networks, designed to provide a better quality of service and spectrum utilization, are rapidly being deployed across the world. 3GPP has proposed a fairness criterion, referred to as “3GPP fairness”, for the coexistence of 5G New Radio in unlicensed spectrum (NR-U) and WiFi in Release 16. In this correspondence, we derive an analytical expression to determine if the 3GPP fairness is achieved in a given network configuration. We model achieving the 3GPP fairness as an optimization problem and show how to use Sequential Quadratic Programming to find the optimal 5G NR U parameters from a pre-decided range. We test the optimizer in various conditions and inspect the effect of various parameters. Our results reveal that the optimizer is able to predict the best possible parameters for 3GPP fairness and therefore the proposed method proves useful for tuning 5G NR-U parameters during their coexistence with WiFi.

This paper addresses high frequency admittance modelling of current-controlled voltage source converters (VSCs). Recent studies have shown that harmonic instability may also occur at frequencies above the Nyquist frequency. To form an accurate multiple-frequency model in this frequency range, sidebands that originate from modulation and sampling must be examined. In this paper, an accurate small-signal model is developed taking into account an adequate digital pulse-width modulator (DPWM) representation, which allows to predict dependence of the frequency response on the steady-state DC (SSDC) operating point. It is shown that, when center-pulse sampling is implemented, PWM sidebands do not create additional loops leaving only sampling sidebands to be considered. Using the same approach as for SS-DC operation, a model which accurately represents admittance measurements during sinusoidal AC (S-AC) operation is developed. Its basis is a novel DPWM model suitable for S-AC regime, which allows to predict dependence of the VSC’s input admittance on the grid voltage magnitude. Experimental and simulated admittance measurements, performed on a single-phase two-level VSC during various SS-DC and S-AC regimes, show an excellent match with the proposed models up to twice the sampling frequency

Non-orthogonal multiple access principle applied in power domain (power-domain NOMA) enhances spectrum efficiency and connectivity of mobile communication systems through sharing between Orthogonal Multiple Access (OMA) and other multiple users. It is the almost main current trend for research and standards for 5G and beyond. Spectrum sharing generates multi-users interference (Multiple Access Interference, MAI) which destroys orthogonality in OMA systems. Meanwhile the so-called Successive Interference Cancellation (SIC) techniques are used to suppress MAI and the Cognitive Radio (CR) principle (CR-NOMA) for decoding order for multiple users inspired effective SIC algorithms. The CR-NOMA principle is applied in the present material as well, but with significant changes for SIC design as for Doubly Selective channels. Due to the above reasons, two original SIC design methods are proposed hereafter following the CR-NOMA principle. Regarding to it the OMA signal (primary user) is decoded first using a Chaos-based quasi-optimum Extended Kalman Filter (EKF). The set of “secondary users” (multiusers) are proposed to be decoded secondly by the following two different methods: - Chaos-based EKF filters or , - Sequential m hypothesis testing. The proposed approach for SIC design is rather simple, fast and accurate for practical implementation.

Fiber optic sensors are becoming key tools in building more robust and accurate traffic monitoring systems. In this survey, we present various preliminary implementations and a discussion about some of the key design points. The scanning rates of the sensors should be at least 500 Hz to correctly capture the moving vehicles. The size of the sensors should not be larger than approximately 50 cm to enable individual axle detection. A minimum of 1με sensor resolution is required to detect light weight vehicles. Adaptive filtering, Wavelet denoising and neural networks are some of the tools used to improve the performance. Sensors that reside on the surface of the pavement might lead to biased data and cause destructive implementation. There is yet to be a unified solution. We need a global, user-friendly, consistent, durable, environment-friendly solution with high system availability that would work on bridges, in urban areas and in tunnels. We believe precast slabs of road pavement embedded with sensors that are manufactured at a facility would give us the best opportunity to meet those demands.

FEM_2D is an open-source Finite Element Method library implemented in Rust. This MetaPaper describes some of the libraries basic functionality, as well as its unique contributions to the FEM research community. This package exhibits a powerful *hp*-refinement API with anisotropic refinements and no constraints on mesh irregularity. It also leverages Rust’s Trait-Based Genericism to provide straightforward application to many problem domains. This library has been featured in some recent research in Computational Electromagnetics (see references), depicting the validity of the underlying methodology. FEM_2D has been made available as an open-source library with the intention of providing additional explanatory value to the existing related research, as well as to facilitate the expansion of this research area

A discursive piece aimed at practitioners in Engineering Management. On the subject of Complexity Leadership and KPIs, this short article presents three models which expand on the work of Uhl-Bien and Marion (2007) by focusing on Administrative, Enabling and Adaptive Leaderships for managing complexity.

A novel switching-linear hybrid dynamic regulator architecture with dual-supply low dropout regulators (LDOs) is presented in this paper. This architecture leverages the intrinsic dual supplies to extend the operating range of the LDOs. Furthermore, it increases the thermo-optic tuning efficiency by reducing the LDO dropout voltage through dynamic supply modulation. This architecture is suitable for large-scale thermooptic tuning in silicon photonics. The efficiency improvement is particularly effective when tracking signals of several adjacent channels are close to each other, e.g., wavelength tuning of a microring array. The principle of this architecture is general and can be implemented using different switching converters and LDOs. A specific design with extensive post-layout simulation results is used to verify the effectiveness of our architecture. Implemented in a 130 nm CMOS process, this design can simultaneously regulate eight output channels with an output swing of 0.8 Vpp. Its peak efficiency when driving 100 ohms loads is 92% at 1 V output, and the dynamic efficiency is around 86% when tracking 50 kHz sinusoidal signals. To the best of our knowledge, this is the first time that dynamic supply modulation has been applied to the thermo-optic tuning of silicon photonic devices.

Greenhouse gasses (GHGs) play an important role in controlling local air pollution as well as climate change. In the present study, we retrieved column-averaged dry-air (X) mole fractions of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier Transform Infrared Spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this paper, the diurnal variation and the time series of daily averaged XCO2, XCH4, and XCO covering the period December 2020 to May 2021 are analyzed. The maximum values of XCO2, XCH4, and XCO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb respectively. Less diurnal (XCO2 ~0.44 ppm; XCH4=0.004 ppm, and XCO=4.84 ppb) but clear seasonal changes are observed during the study period. The XCH4 and XCO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient ‘r’ between the EM27/SUN retrieved XCH4 and XCO against S5P/TROPOMI are 0.75 and 0.94 respectively.

© 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. DOI (identifier) 10.1109/TRO.2022.3226887 Abstract: Most impedance-based walking controllers for powered knee-ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g. walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this paper presents a data-driven, phase-based controller for variable-task walking that uses continuously-variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants (N=2) show that our data-driven controller 1) features highly-linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning.

Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML energy use (across research, development, and production) held steady at <15% of Google’s total energy use for the past three years. If the whole ML field were to adopt best practices, total carbon emissions from training would reduce. Hence, we recommend that ML papers include emissions explicitly to foster competition on more than just model quality. As estimates of emissions in papers that omitted them have been off 100x–100,000x, publishing emissions has the added benefit of ensuring accurate accounting. Given the importance of climate change, we must get the numbers right to make certain that we work on its biggest challenges.

Internet-of-drones (IoD) systems require enhanced data transmission security and efficient interference management to accommodate the rapidly growing drone-based and rate-intensive applications. This paper develops a novel resource allocation scheme to jointly manage interference and enhance the physical layer security  of  cellular-connected IoD networks in the presence of a multi-band eavesdropping drone. Our envisioned cellular-connected IoD network has multiple full-duplex cellular base stations (CBSs), where each CBS reserves an orthogonal cellular  radio resource block (RRB) for the aerial communication links. To efficiently utilize the cellular RRBs, each CBS is connected to a cluster of data transmitting drones using uplink  non-orthogonal multiple access (NOMA) scheme. In addition, all the CBSs simultaneously transmit  artificial noise signals to weaken the eavesdropper links.  A joint optimization problem, considering the transmit power allocation and clustering of the legitimate drones, and the jamming power allocation of the CBSs, is formulated to maximize the worst-case average sum-secrecy-rate of the  network. The joint optimization problem  is decomposed into  drone-clustering and power allocation sub-problems to obtain an efficient solution. A multi-agent reinforcement-learning framework is devised to solve the drone-clustering sub-problem.  Meanwhile, the transmit and jamming power allocation sub-problem is solved by employing fractional programming, successive convex approximation, and alternating optimization techniques. By iteratively solving these two sub-problems, a convergent resource allocation algorithm, namely, security and interference management with reinforcement-learning and NOMA (SIREN), is proposed.  The superiority of SIREN over several benchmark schemes is verified via extensive simulations.

Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime of an electrical machine. In an induction machine (IM) driven by an inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced by a fault due to the fault-controller interaction. Based on this observation, this study developed neural network models using inverter switching statistics to detect the ISCF of an IM. The method was non-invasive, and it did not require any additional sensors. In the fault detection task, an area under receiver operating characteristics curve value of 0.9950 (95% Confidence IntervaI: 0.9949 - 0.9951) was obtained. At the rated operating conditions, the neural network model detected and located an ISCF of 2-turns (out of 104 turns per phase) under 0.1 seconds, a speedup of more than two times compared to the thresholding-based method. Moreover, we published the switching vector data collected at various load torque and shaft speed values for healthy and faulty states of the IM, becoming the first publicly available ISCF detection dataset. Together with the dataset, we provided performance baselines for three main neural network architectures, namely, multi-layer perceptron, convolutional neural network, and recurrent neural network.

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. Preprint of paper submitted to IEEE Transactions on Automation Science and Engineering (T-ASE).