In a conventional power grid, energy theft is difficult to detect due to limited communication and data transition. The smart meter along with big data mining technology leads to significant technological innovation in the field of energy theft detection. This paper proposes a convolutional long and shortterm memory (ConvLSTM) based energy theft detection (ETD) model to identify electricity theft users. In this work, electricity consumption data is reshaped quarterly into a 2-dimensional matrix and used as the sequential input to the ConvLSTM. The convolutional neural network (CNN) embedded into the long short-term memory (LSTM) can better learn the features of the data on different quarters, months, weeks, and days. Besides, the proposed model incorporates batch normalization. This allows the proposed ETD model to support raw format electricity consumption data input, reducing training time and increasing the efficiency of model deployment. The final experimental results show that the proposed ConvLSTM model exhibits good robustness. It outperforms the multilayer perceptron (MLP) and CNN-LSTM in terms of performance metrics and model generalization capability.

In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

Reconfigurable intelligent surface (RIS)-assisted power-domain non-orthogonal multiple access (PD-NOMA) system has emerged as a revolutionary technology to enhance the spectrum efficiency for future wireless networks. This work introduces two novel RIS systems, namely, RIS partition-assisted (RISP) PD-NOMA (RISP-PD-NOMA) and RISP-Quadrature NOMA (RISP-Q-NOMA), to improve the signal quality of all users by dedicating fixed RIS units to each user for phase cancellation. The closed-form expressions of average sum-rate, outage probability, and diversity order of both systems are evaluated under the Rician fading channel for perfect and imperfect successive interference cancellation (SIC). Further, the performance of both systems is compared with RIS-division PDNOMA (RISD-PD-NOMA) system where the RIS subsurface assigned to one user is not exposed to another user. It is noticed from the analysis that under perfect SIC, RISP-PD-NOMA outperforms RISP-Q-NOMA and RISD-PD-NOMA. However, under imperfect SIC, RISP-Q-NOMA demonstrates superior performance than other systems, due to lesser number of SIC operations that lead to less stringent constraints on the power allocation, reduce detection delay, and improve SIC stability. Furthermore, the analytical expression for bit error rate (BER) of RISP-PD-NOMA and RISP-Q-NOMA is derived, and then closed-form expression of average-BER is evaluated. Numerical results demonstrate that RISP-Q-NOMA is superior to RISPPD-NOMA and RISD-PD-NOMA.

Signal flow graph (SFG) representation of small-signal responses of nonlinear microwave circuits around a large-signal operating point is developed using the X-parameters. It is shown that, unlike the SFGs for linear circuits, negative-frequency nodes need to be included explicitly. The development elucidates the circuit-operational meaning of the elusive T-type small-signal X-parameters, which represent interaction between positive- and negative-frequency components. As an example, such an SFG is used to derive a closed-form expression of the output power of an amplifier as a function of the load reflection coefficient. It is then used to plot approximate load-pull power contours. The result is consistent with the expressions of the optimum load reflection coefficient derived by Root et al. (EuMIC 2017) and of power contours derived by Peláez-Pérez et al. (TMTT 2013). SFGs provide an alternative systematic means to derive closed-form expressions in terms of X-parameters and to gain illuminating insights into the workings of weakly nonlinear circuits.

Nowadays, sparse multiple input and multiple output (MIMO) radar design considers the difference coarray of the sum coarray (DCSC) concept and the uniform degrees of freedom is improved significantly. Current sparse MIMO arrays are designed based on transmit and receive arrays with hole-free difference coarrays (DCAs). Recently, the uniform linear array (ULA) fitting (UF) principle is proposed which aims to design sparse arrays (SAs) with desired capacity using cascaded ULAs. However, SAs designed via UF can not guarantee hole-free DCAs. In this letter, we aim to use SAs designed via UF to devise sparse MIMO arrays with low mutual coupling. Strategies of using SAs with non hole-free DCA to design sparse MIMO arrays is introduced as well. Numerical simulations verifies the good performance of the proposed sparse MIMO array in high coupling scenarios.

We analytically study the localized running waves in the discrete Josephson transmission lines (JTL), constructed from Josephson junctions (JJ) and capacitors. The quasi-continuum approximation reduces calculation of the running wave properties to the problem of equilibrium of an elastic rod in the potential field. Making additional approximation, we reduce the problem to the motion of the fictitious Newtonian particle in the potential well. We show that there exist running waves in the form of supersonic kinks and solitons and calculate their velocities and profiles. We show that the nonstationary smooth waves which are small perturbations on the homogeneous non-zero background are described by Korteweg-de Vries equation, and those on zero background – by modified Korteweg-de Vries equation. We also study the effect of dissipation on the running waves in JTL and find that in the presence of the resistors, shunting the JJ and/or in series with the ground capacitors, the only possible stationary running waves are the shock waves, whose profiles are also found.

Despite coverage enhancement in rural areas is one of the main requirements in next generations of wireless networks (i.e., 5G and 6G), the low expected profit prevents telecommunication providers from investing in such sparsely populated areas. Hence, cost efficient alternatives are required for extending the cellular infrastructure to these regions. A concrete mathematical model that characterizes and clearly captures the aforementioned problem might be a key-enabler for studying the efficiency of any potential solution. Unfortunately, the commonly used mathematical tools that model large scale wireless networks are not designed to capture the unfairness, in terms of cellular coverage, suffered by exurban and rural areas. In big cities, in fact, cellular deployment is essentially capacity driven and thus cellular base station densities are maximum in the town centers and decline when getting far from them. In this paper, a new stochastic geometry-based model is implemented in order to show the coverage spatial variation among urban, suburban, and exurban settlements. Indeed, by implementing inhomogeneous Poisson point processes (PPPs) it is possible to study the performance metrics in a realistic scenario where terrestrial base stations (TBSs) are clustered around the urban center while outer aerial base stations (ABSs) are uniformly distributed outside an urban exclusion zone. Based on this, our simulation results can quantify the improvement, in terms of coverage probability, that even a surprisingly low density of ABSs can bring to peripheral regions depending on the extension of the exclusion zone, enabling us to draw insightful considerations.

This document describes the design and implementation sequence of both hardware and firmware components of two microcontroller boards of 8 and 32 bit architectures, which use similar form factor and share common input/output devices and recently developed web-based user interfaces. Both have been applied mainly to educational and research systems in renewable energy. The focus has been on increasing reliability in the hardware aspects and using a set of in-house C language coding rules for new firmware development and legacy code refactoring. Testing of the low-level drivers, and use of automated tools such as Ceedling for higher level module testing is also described.

Recently, uniform linear array (ULA) fitting (UF) principle is proposed for sparse array (SA) design using pseudo-polynomial equations. Typically, it is verified that the designed SAs via UF enjoy a lower bound on uniform degrees of freedom (uDOF), which is approximately $0.5N^2$ with $N$ sensors. Herein, an improved UF (IUF) scheme is proposed to significantly increase the lower bound on uDOF, which is realized by introducing two sub-ULAs, with name of adjoint transfer arrays (ATAs), on both sides of a transfer sub-ULA. The ATAs together with the transfer sub-ULA serve as a new layer to construct an adjoint transfer layer (ATL) that has improved aperture in comparison with traditional transfer layer to improve the lower bound on uDOF, which is derived in detail. Furthermore, two novel SAs are developed based on the ATL to verify the effectiveness of the proposed IUF scheme to increase the uDOF. Numerical simulations verify the superiority of devised SAs for direction-of-arrival (DOA) estimations.

Given the success of the open source phenomenon, it is not surprising that many organizations are seeking to emulate this success by adopting open source practices internally in what is termed InnerSource. However, while open source development and InnerSource are similar in some aspects, they differ significantly on others, and thus need to be implemented and managed differently. To the best of our knowledge, there is no significant account of a successful InnerSource incentivization program. Here we describe a comprehensive InnerSource incentivization program which was implemented at Huawei. The program is based on theories of motivation, both intrinsic and extrinsic, and also includes incentives at the individual, project, and divisional level, which helps to overcome the barriers that arise when implementing InnerSource. The program has had very impressive early results, leading to significant increases in the number of InnerSource projects, contributors, departments, and lines of code contributed.

The uncertain data in our work is when values along each dimension of a database may contain both exact and continuous range values.

The diversity-multiplexing trade-off (DMT) of a diamond relay channel in a quasi-static slow Rayleigh fading environment is studied, where one of the relays operates in half-duplex mode while the other one is full-duplex; hence, the term hybrid is used to refer to this channel. DMT is a known powerful tool that captures the inherent tension between the reliability and the transmission rate in a wireless communication network at high signal-to-noise ratios (SNR). In this study, we have assumed that the average SNRs of different links vary at different rates, each represented by an exponent at high SNR. These average SNR exponents are categorized into two different classes: In Class I, both of the relays are placed either in close proximity to the source or in close proximity to the destination, whereas in Class II one of the relays is closer to the source while the other one is closer to the destination. Subsequently, for Class I and for a special symmetric case of Class II, an explicit expression of the DMT of the static quantize-map-and-forward (SQMF) strategy and the optimal static schedule in the half-duplex relay, is derived. The non-symmetric case of Class II can numerically be studied using the optimization problem provided in the paper for any given parameters. As a corollary, our analysis shows that for Class I, the SQMF strategy in the proposed hybrid diamond relay channel reaches the ideal full-duplex DMT upper bound if the degrees of freedom provided by the path through the full-duplex relay is greater than or equal to that provided by the path through the half-duplex relay, or if the degrees of freedom provided by the former is less than that provided by the latter, but still large enough to support the desired multiplexing gain.

Sixth generation (6G) internet of things (IoT) networks will modernize the applications and satisfy user demands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable intelligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks.

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by sending their local gradients to a server for aggregation, without having to disclose their data. This paper proposes SVFL, an efficient protocol for cross-silo FL, that supports both secure gradient aggregation and verification. We evaluate the performance of SVFL and show, by complexity analysis and experimental evaluations, that its computation and communication overhead remain low even on large datasets, with a negligible accuracy loss (less than $1\%$). Furthermore, we conduct an experimental comparison between SVFL and other existing FL protocols, and show that SVFL achieves significant efficiency improvements in both computation and communication.

The optimal combination of energy conversion and storage technologies with local energy demand is a key but in its result not obvious challenge of distributed energy. Although a variety of possible approaches to the optimal design of limited technology selections can be found in the literature, the previous design step, the actual technology selection, and the subsequent step, the selection of the optimal operating strategy, are often neglected. We develop and demonstrate a methodology, which can optimise energy systems with arbitrary technology selection and under multi-criteria optimality definitions. The energy system modelled in oemof.solph is optimised using a MOEA/D approach with regard to economic, ecological and technical key performance indicators. The aim is to find trends and tendencies with a methodology that is as generalised as possible in order to integrate it into the decision-making process in energy system planning. We demonstrate the method by means of a German district for which an integrated supply concept is being sought. Different evaluation and visualisation possibilities are presented and the chances and limitations of the developed methodology are identified. We show that not only the choice of technology, but especially its sizing and operational strategy have a decisive influence on the optimality.

Quality of Experience in Internet of Things: A Systematic Literature Review

We propose an energy-based framework to analyze the statics and dynamics of a ferroelectric negative capacitance-hybrid Microelectromechanical System (MEMS) actuator. A mapping function that relates the charge on the ferroelectric to displacement of the movable electrode, is used to obtain the Hamiltonian of the hybrid actuator in terms of displacement. We then use graphical energy-displacement and phase portrait plots to analyze static pull-in, dynamic pull-in and pull-out phenomena of the hybrid actuator. Using these, we illustrate the low-voltage operation of the hybrid actuator to static and step inputs, as compared to the standalone MEMS actuator. The results obtained are in agreement with the analytical predictions and numerical simulations. The proposed framework enables straightforward inclusion of adhesion between the contacting surfaces, modeled using van der Waals force. We show that the pull-in voltage is not affected, while the pull-out voltage is reduced due to adhesion. The proposed framework provides a physics-based tool to design and analyze negative capacitance based low-voltage MEMS actuators.