This paper starts by reviewing demand response models in MISO’s energy management and market systems. It then discusses a recent enhancement that improves the ability of demand response to participate in MISO energy and ancillary service markets. Some future enhancements are also discussed.

Compressive sensing (CS) is quite appealing as a low-complexity method for the compression of ECG data in resource-limited wearable devices. We propose a codec architecture comprising adaptive quantization and asymmetric numeral systems (ANS) based entropy coding of compressive measurements that can boost the compression ratio without sacrificing reconstruction performance. The quantized Gaussian entropy model for the compressive measurements is estimated directly from the data and is adapted dynamically to achieve better compression. We have tested our encoder with Block Sparse Bayesian learning as well as CS-NET sparse recovery algorithms on the MIT-BIH Arrhythmia database. Our encoder can achieve 5-25\% of additional space savings over compressive sensing. The software code implementing this codec and all scripts for the experimental studies conducted in this work have been released as opensource software on GitHub.

In this study, a deep neural network (DNN) for a massive multiple-input-multiple-output (mMIMO) nonorthogonal multiple access (NOMA) system’s channel estimation and detection is presented. The proposed network applies deep learning (DL) to a discrete wavelet transform (DWT) based orthogonal frequency-division-multiplexing (OFDM) to reduce the noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. The transmission overhead is reduced because the presented network is trained to estimate and detect symbols with and without pilot signals. The results of this study demonstrate that our provided DL-based successive interference cancellation (SIC) method performs better than the conventional SIC-based signal detection in the mMIMONOMA network. The suggested wavelet-based mMIMO-NOMA is also compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA, particularly in terms of symbol error rate (SER).

The 2x-thru calibration has emerged as an attractive alternative to the classical TRL for S parameter measurement of printed circuit board and packaging devices, due to its simplicity that only one THRU standard suffices to fully characterize the test fixtures, thus eliminating the LINE measurement in TRL and also overcoming the bandwidth limit. Although various 2x-thru calibration tools are available nowadays, there are still some issues that have not been studied in detail. This paper addresses the following two fundamental questions: (i) what is the reference impedance (Zref) of the S parameters after 2x-thru calibration? (ii) what is the time-domain reflectometry (TDR) response of the THRU for high conductor-loss transmission lines, and how to estimate Zref given such TDR? For problem (i), we rigorously show that the Zref is equal to the characteristic impedance of the center transmission line in the THRU standard, and provide the important interpretation for the S parameters in terms of the traveling-wave and pseudowave concepts. For problem (ii), we show that the TDR impedance is a monotonically increasing function instead of constant, and we propose a new method to estimate the characteristic impedance by fitting the TDR with a causal impedance model. Simulation and measurement examples are provided to validate the proposed theory and techniques. In the last example, we also compare five commercial/open-source 2x-thru calibration tools; the results reveal that with the proposed method of impedance estimation, better accuracy can be achieved.

This study proposes an effective method to predict the development of technology from an evolutionary perspective. Product evolution is the result of technological evolution and market selection. A phylogenetic network is the main method to study product evolution. The formation of the dominant design determines the trajectory of technology development. How to predict future dominant design has become a key issue in technology forecasting and new product development. This study uses machine learning methods, combined with product evolutionary theory, and constructs a Fully Connected Phylogenetic Network dataset to effectively predict the future dominant design.

A novel vector network analyzer multimode multiline thru-reflect-line calibration is presented. It has the advantage over previous calibrations that it is accurate for all measured modes, and more important, accuracy can be increased by simply adding more transmission medium sections to the calibration standards set. The calibration is based on considering each of the k measurements of k transmission media lengths as part of a tensor that takes into account N modes. The measurement of a WR-90 band-pass filter in multimode operation, from 20.5 GHz to 23.5 GHz, has been carried out by implementing a multimode multiline calibration kit. Measurements show excellent agreement with theory.

Artificial Neural Networks and Convolutional Neural Networks have become common tools for classification and object detection, owing to their ability to learn features without prior knowledge. During training, these networks learn the parameters, weights, and biases. This paper proposes a simple Neural Network and Convolutional Neural Network (CNN) for a classification task. Furthermore, the Bayesian neural network work is reproduced as a baseline for comparing my proposed networks. All experiments were conducted using the MNIST dataset. While the simple neural networks and the convolutional networks adjust their parameters based on the cost function during training, the Bayesian convolutional neural network updates its parameters based on the backdrop that drives a variational approximation to the true posterior. Hyperparameters such as optimizer, learning rate, regularizers, dropout, epochs, etc., were varied to train the two proposed networks. The proposed networks achieved better classification accuracy, approximately 99\%, than the previously implemented Bayesian convolutional neural network. However, it is difficult to predict the certainty of the predictions made by my proposed networks, unlike Bayesian learning, which makes it easy to do so.  \href{https://github.com/Simeon340703/Classification_Networks}{You can find the code for this work at}.

One of the candidate technologies for sixth-generation (6G) wireless communication systems is cell-free massive multiple-input multiple-output (CF-mMIMO) communication, which can control inter-cell interference in MIMO systems. This paper investigates the performance of  a full-duplex (FD) CF-mMIMO system with practical limited-capacity fronthaul links. The proposed system employs a large number $M$ distributed FD access points (APs), arbitrarily distributed $K_d$ downlink (DL) and $K_u$ uplink (UL) half-duplex (HD) single-antenna user equipments (UEs), and a central processing unit (CPU). To exploit energy efficiency (EE) and potential throughput gains of FD systems, each AP is linked to the CPU by a fronthaul link with limited capacity that handles the quantized UL/DL data to and from the CPU. On the same spectrum resource, each AP is expected to support $K$ single-antenna HD UEs, where $K = (K_u + K_d)$. Despite having a minimal signal processing complexity, our approach offers a uniform quality of service (QoS) to all UEs while providing improved spectrum efficiency and EE. Imperfect channel state information and mobility of the UEs are also considered. Closed-form expression for the outage probability is derived using the optimal uniform quantization and maximum-ratio combining/maximum-ratio transmission considering the Welch-Satterthwaite approximation. Additionally, the asymptotic and infinite-$M$ outage performance of the proposed system are analytically studied and verified via Monte-Carlo simulation.

Future vehicular networks must ensure ultra-reliable low-latency communication (URLLC) for the timely delivery of safety-critical information. Previously proposed resource allocation schemes for URLLC mostly rely on centralized optimization-based algorithms and cannot guarantee the reliability and latency requirements of vehicle-to-vehicle (V2V) communications. This paper investigates the joint power and blocklength allocation to minimize the worst-case decoding-error probability in the finite blocklength (FBL) regime for a URLLC-based V2V communication network. We formulate the problem as a non-convex mixed-integer nonlinear programming problem (MINLP). We first develop a centralized optimization theory-based algorithm based on the derivation of the joint convexity of the decoding error probability in the blocklength and transmit power variables within the region of interest. Next, we propose a two-layered multi-agent deep reinforcement learning based centrally trained and distributively executed framework. The first layer involves establishing multiple deep Q-networks (DQNs) at the central trainer to train the local DQNs for block length optimization. The second layer involves an actor-critic network and utilizes the deep deterministic policy-gradient (DDPG)-based algorithm to train the local actor network for each V2V link. Simulation results demonstrate that the proposed distributed scheme can achieve close-to-optimal solutions with a much lower computational complexity than the centralized optimization based solution.

This paper proposes an inflow scenario reduction framework applied in long-term hydro-thermal scheduling. The strategic management of limited stored hydro energy in interaction with the electricity system is defined in long-term hydro-thermal. The Scenario Fan Problem (SFP) is selected as a long-term hydro-thermal scheduling approach. This approach is suitable to represent the disaggregated physical representation of the hydro stations  and captures the short-term flexibility of individual hydropower plants in the long-term scheduling. We develop a shape-based feature extraction machine learning method to capture the hydro inflow features. To highlight the performance, we compare the shape-based  method with five alternative clustering methods via the validation process on a hydro-thermal test case, using in-sample and out-of-sample data analysis. Our results show that the proposed framework can significantly reduce the computational time of long-term hydro-thermal scheduling by around 90\%. Results reveal that the shape-based feature extraction performs better regarding the computational time and the expected reservoir volumes. Moreover, this study demonstrates the possibility of performing long-term hydro-thermal scheduling at a disaggregated level with detailed topology information and a small time scale. This provides more opportunities to deal with the penetration of renewable energy resources in long-term hydro-thermal scheduling.

Emotion is defined (from Britannica) as a complex combination of experiences of consciousness, bodily sensation and behavior, reflecting personal significance of externally and/or internally triggered changes. As an indispensable part of human communication, emotions could be represented within the digital world as means that could control the human interaction with the digital artifacts. When this is combined with gamification, a more engaging communication for the player could be established. In this article, we explore how multisensed emotions of players could be used as adaptation controllers within the emotion-aware serious neurogames (SNGs). We first briefly describe the emotion sensing and recognition landscape, examining the emotion-source sensing and emotion inferring approaches. We then discuss the SNGs design framework and the way human-to-SNG communication is set. Additionally, we describe the way the adaptation of the SNG characteristics can be driven by the inferred emotions, towards the achievement of the desired SNG goals. In addition, we demonstrate the feasibility and design characteristics of such multisensed emotion-aware SNGs, by presenting a related application case, namely EmoSense, and evaluate its acceptance as a technological solution in the field of neurofeedback. Finally, we discuss challenges and opportunities in the field.

In this paper, we propose a parasitic patch-based method to improve the power transfer efficiency of radiative wireless power transfer (WPT) systems involving implanted biomedical devices. The proposed WPT system uses circularly polarised antennas, having a four-element sequentially rotated antenna array, as the Tx and a miniaturized circularly polarized antenna implanted inside a body mimicking gel as the Rx with a parasitic patch placed in close vicinity. The design guidelines for the dimensions of the parasitic patch and its distance from the implanted antenna are discussed in-depth based on rigorous parametric studies and analysis. The proposed parasitic patch-based method improves the implantable antenna gain by 7.83 dBic, thereby increasing the power transfer efficiency at 2.4 GHz operating frequency. Improvement in power transfer is experimentally demonstrated with the help of a low-cost experimental setup having a software-defined radio (SDR) and a spectrum analyzer. Further to analyze the effect of interfering signals on the performance of the WPT system, power received by the implantable antenna is measured in presence of a secondary transmitter.

This paper consists of an overview and a brief description of all the Beagle Boards from the original Beagle Board released in 2008 to the latest board BeagleV released in 2019. The Beaglebone board is one of the high-end hobby-grade boards and has quite a few applications in industrial/consumer electronics as well. Many beginners prefer Arduino boards and Raspberry Pi boards for tinkering. The Beagle Boards are an amalgamation of both Arduino and Raspberry Pi boards. Owing to their high prices, they are not as widely used as the Arduino boards and Raspberry Pi boards, but the Beagle Boards are robust and have an added advantage in the form of on-board GPU and DSP. Furthermore, the on-chip PRU-ICSS contribute to the computing process increasing the efficiency of the boards. The Beagle boards have multiple OS Support (Debian, LXDE, Ubuntu, etc.). The paper also discusses the interfacing of Beagle boards with various peripherals and components. The various types of capes available for the Beagle boards are discussed as well. The paper also presents an introduction on how to get started with the Beagle boards. The applications of the Beagle boards are discussed as well.

We designed and developed an EEG to help predict neural disorders. This project achieved both high accuracy and low cost with cost-effective components such as the AD622ANZ instrumentation amplifier and TL048x operational amplifier. Signal processing software and hardware filters were implemented. In addition, a machine learning approach was utilized to develop a binary seizure classifier. Seizure noise was simulated, and future work would revolve around collecting live data from epileptic patients. Future work could show that our design could detect and diagnose other neural disorders. We aim to make this design a closed loop system and BCI (Brain-Computer-Interface) compatible.

Surrogate-assisted evolutionary algorithms (SAEAs) are a promising approach for solving expensive multiobjective optimization problems, but they often cannot address high-dimensional problems. Although one common approach to this challenge is to construct reliable surrogates, their accuracy inevitably deteriorates in a high-dimensional search space. Thus, this paper presents a novel SAEA based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems. The intent is to decrease the risk of search performance degradations caused by unreliable approximations and retain the redundancy of the surrogate-assisted search to hedge the risk of over-fitting. Furthermore, each approximation model is adapted to a promising region of its corresponding decomposed problem to reduce the complexity of model fitting given a limited number of training samples. Experimental results show that the proposed algorithm is competitive with state-of-the-art SAEAs adapted for high-dimensional problems.

This research introduces the importance of the workload prediction for virtualized radio access network (RAN) deployment in the edge micro data center (EMDC). For workload prediction, several machine learning algorithms are evaluated in terms of central processing unit (CPU) usage while deploying the vRAN components.

This paper presents a new design of diverse frequency time-modulation for linear frequency modulated (LFM) radar passive false target spoofing. To realize the passive diverse frequency time-modulation, the modulated metasurface is designed accordingly, which is composed by a set of elements, each of which is independently time-modulated to realize specific false targets in the high-resolution range profile (HRRP) of the radar system. Thanks to the control of the modulation scheme in each element and the combined response of the overall surface, we demonstrate the capability to realize several false target patterns consisting of single and multiple targets symmetrically, or asymmetrically, distributed around the actual target. A HRRP general expression of the time modulated diverse frequency metasurface at different observation angles is given to show the relation between the generated false targets and frequency components. Since having more flexible capacity of reflected signal manipulation comparing to false target spoofing only through time modulation with consistent modulation frequency, the proposed method has enhanced performance on control of false targets at different observation angles. Both simulated and measured results demonstrate the validity of false targets manipulation, demonstrating its effectiveness as electromagnetic countermeasure for achieving radar camouflaging and deception.

Backgrounds: Based on SYK-related models, Majorana particle is a fermion that is its own antiparticle. Recent discovery on simulating traversable wormhole by quantum processor based on Majorana particle is a significant progress in quantum simulation. By adding possible extendable approaches, more possibility could be suggested. Objectives: Discovering symmetry conservation attribute from Majorana particle to expand its usage for wormhole. Methods: By applying to the simplified non-unit model, additional considerations for simulating wormhole would be discovered. Results: Additional considerations of expanding holographic wormhole could be discussed in model.