The controversy and argument on the usefulness of the physical layer (PHY) academic research for wireless communications are long-standing since the cellular communication paradigm gets to its maturity. In particular, researchers suspect that the performance improvement in cellular communications is primarily attributable to the increases in telecommunication infrastructure and radio spectrum instead of the PHY academic research, whereas concrete evidence is lacking. To respond to this controversy from an objective perspective, we employ econometric approaches to quantify the contributions of the PHY academic research and other performance determinants. Through empirical analysis and the quantitative evidence obtained, albeit preliminary, we shed light on the following issues: 1) what determines the cross-national differences in cellular network performance; 2) to what extent the PHY academic research and other factors affect cellular network performance; 3) what suggestions we can obtain from the data analysis for the stakeholders of the PHY research. To the best of our knowledge, this article is the first `empirical telecommunication research,' and the first effort to involve econometric methodologies to evaluate the usefulness of the PHY academic research.

Despite the continued evolution of information systems methodologies for more than three decades, the rates of software rejection and failure are still high. This paper investigates the technological environment as a major cause of such disruptions. Additionally, the paper evaluates Agile and DevOps as remedial methodologies for managing the adverse impact of technological disruptions. The main findings affirm both Agile and DevOps as methodologies that emanated from improvements or re-engineering of earlier methodologies. Further findings discern most methodologies; including agile and DevOps; as not strategically focused but appraise DevOps as the most progressive methodology towards this respect. Rather than re-invent the wheel and come up with a new methodology, a framework that aligns DevOps for use in strategic information systems development is proposed. Besides, a more realistic definition of operations is postulated to bolster the alignment.  Keywords software

This paper introduces a virtual synchronous machine (VSM) control scheme which is based on a minimized emulation of the model of a synchronous generator (SG) for a grid-forming wind turbine (WT) inverter. Based on this VSM control scheme, state-space representation and block diagram of the small-signal model for the VSM-controlled grid-forming inverter connected to an infinite bus are derived, which are used for small-signal stability analysis of the power angle in the VSM control scheme. This power angle small-signal stability (PAS) analysis is proposed as a simpler way for analyzing the influence on the rotor angle stability of power system from large share of VSM-controlled grid-forming inverters. Influence of parameters in the power loop of the control scheme on the PAS is investigated by analyzing the eigenvalue trajectories of the state matrix.

The standardization of fifth generation (5G) communications has been completed, and the 5G network should be commercially launched in 2020. As a result, the visioning and planning of sixth generation (6G) communications has begun, with an aim to provide communication services for the future demands of the 2030s. Here we provide a vision for 6G that could serve a research guide in the post-5G era. We suggest that human-centric mobile communications will still be the most important application of 6G and the 6G network should be human centric. Thus, high security, secrecy, and privacy should be key features of 6G and should be given particular attention by the wireless research community. To support this vision, we provide a systematic framework in which potential application scenarios of 6G are anticipated and subdivided. We subsequently define key potential features of 6G and discuss the required communication technologies. We also explore the issues beyond communication technologies that could hamper research and deployment of 6G.

To meet the increasing demands for passenger data rates, modern railway communication networks face significant challenges. The advent of 5G communications after the long-term evolution (LTE) and LTE-Advanced (LTE-A) systems provides several technological advances to address these challenges. In this paper, after reviewing the main 5G communication aspects for modern railways, we present methods to ensure connectivity and energy efficiency for the passengers’ user equipment (UE) through the use of mobile relays (MRs) on top of the train wagons in conjunction with intelligent resource allocation. Relevant challenges to train connectivity are also presented, and suitable solutions are outlined.

Providing connectivity to around half of the World population living in rural or underprivileged areas is a tremendous challenge, but also a unique opportunity. In this paper, a survey of technologies for providing connectivity to rural areas, and that can help address this challenge, is provided. Fronthaul and backhaul techniques are discussed. In addition, energy and cost efficiency of the studied technologies are analyzed. Typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. Directions for future evolution of rural connectivity are outlined.

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs.

The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.

Malware behavioral graphs provide a rich source of information that can be leveraged for detection and classification tasks. In this paper, we propose a novel behavioral malware detection method based on Deep Graph Convolutional Neural Networks (DGCNNs) to learn directly from API call sequences and their associated behavioral graphs. In order to train and evaluate the models, we created a new public domain dataset of more than 40,000 API call sequences resulting from the execution of malware and goodware instances in a sandboxed environment. Experimental results show that our models achieve similar Area Under the ROC Curve (AUC-ROC) and F1-Score to Long-Short Term Memory (LSTM) networks, widely used as the base architecture for behavioral malware detection methods, thus indicating that the models can effectively learn to distinguish between malicious and benign temporal patterns through convolution operations on graphs. To the best of our knowledge, this is the first paper that investigates the applicability of DGCNN to behavioral malware detection using API call sequences.

Great people like Stephen Hawking and Max Brito have been suffering from this crippling phenomenon. Our project is an attempt to make lives of the physically challenged people by our extravagant project. We mean self-reliant, which will thereby reinstate their confidence and their happiness. The idea is to create an Eye Monitored System which allows movement of the patient‟s wheelchair depending on the eye movements. We know that a person suffering from quadriplegia can partially move his eyes and tilt his head, thus presenting an opportunity for detecting those movements. We have created a device where a patient sitting on the Wheel Chair assembly looking directly at the camera, is able to move in a direction just by looking in that direction. The camera signals are monitored by a MATLAB script, which will then guide the motors wired to the AtMega1284P Microcontroller over the Serial Interface to move in a particular direction. The system is cost effective and thus can be used by patients spread over a large economy range. We have implement a Smart mobile application to paramount our project and thereby we justify our project name “The Smart Mobility For Physically Challenged”

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs are one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results for a deterministic example and a stochastic case study are presented and discussed.

Before diode rectifier (DR) technology for connecting offshore wind farms (OWFs) to HVdc is deployed, in-depth studies are needed to assess the actual capabilities of DR-connected OWFs to contribute to the secure operation of the networks linked to them. This study assesses the capability of such an OWF to provide communication-less frequency support (CLFS) to an onshore ac network. It is shown that the HVdc link’s offshore terminal direct voltage can be estimated from measurements at the OWF’s point of connection with the DR platform. Two different methods are proposed for implementing CLFS in the OWF active power controls. In Method 1, the estimated offshore terminal direct voltage is used for estimating the onshore frequency deviation. In Method 2, the actual offshore terminal direct voltage measurement is used instead. Unique features of the provision of CLFS from OWFs connected to HVdc via DRs are highlighted, and the dynamic and static performance of the CLFS control scheme is compared to that of the communication-based frequency support scheme. To assess the impact of parameter estimation errors on the provision of CLFS, a parametric sensitivity study is presented as well, and recommendations are given to increase accuracy. The manuscript has been accepted for publication in IEEE Transactions on Sustainable Energy.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder.

Safety-critical power conversion systems play a major role in the paradigm shift towards more electric aircraft (MEA) architectures. This paper reviews the electrical machines and their power electronic systems that are currently competing in the application of integrated starter-generators (S/Gs) in MEA power systems. Motivated by the strict requirements of sufficient electrical starting capability, super-high power density and ultra-high reliability, additional considerations on the overall system design are necessary, including the power electronic converters (PECs) and integrated thermal designs. These aspects are discussed not only in the light of their many benefits but also of the challenges introduced by the continuous advancements and emerging innovations in the power conversion technology. In achieving the MEA goals and capitalize on all potential benefits, optimization-based design approaches will be necessary, where the aggregation of electric machines, PECs and the aircraft grid is considered as an integrated system to be optimized. This review highlights the importance of these aspects and offers a view on future perspectives and open issues.

This paper investigates and implements a procedure for parameter identification of salient pole synchronous machines that is based on previous knowledge about the equipment and can be used for condition monitoring, online assessment of the electrical power grid, and adaptive control. It uses a Kalman filter to handle noise and correct deviations in measurements caused by uncertainty of instruments or effects not included in the model. Then it applies a recursive least squares algorithm to identify parameters from the synchronous machine model. Despite being affected by saturation effects, the proposed procedure estimates 8 out of 13 parameters from the machine model with minor deviations from data sheet values and is largely insensitive to noise and load conditions. Submitted to IEEE IECON 2019.

In this paper, the concept and recent development of exploiting frequency diverse array (FDA) and its variants for the physical-layer wireless security have been revisited and carefully examined. Following rigorous analytical derivation and illustrative simulations, the authors argue that the investigations performed in some recent works did not reveal one critical issue facing the real-world applications, and system models established and used before were based on an unrealistic assumption, i.e. that the legitimate and eavesdropping users at different ranges sample the signal waveforms at the same time instant. This misunderstanding results in conclusions that are misleading. The authors aim to take the first step to divert research efforts and rectify the previous problematic analyses. The authors prove that the FDA cannot secure a free-space wireless transmission in range domain, because the previously claimed ‘secure reception region’ propagates in range domain as time elapses.

This paper presents an improved selective harmonics elimination technique for PV assisted single phase grid-tied pulse width modulated (PWM) voltage source inverter (VSI). The switching angles are determined offline through numerical techniques and stored in microcontroller memory as a function of modulation index md for online application. For multiple solution, the solution which leads to lower change of switching angles (α) from the previous md, is considered for storing in the processor memory. This leads to less no. of sections for the processor when a piecewise mixed model is considered for storing the entire switching angle curve. This technique is well suited for limiting voltage THD in two level grid connected VSI with L-C filter. The verification of theoretical concept is done in laboratory prototype of PV (500 W) connected to grid-tied PWM inverter. The control environment is realized in embedded FPGA interfaced national instrument hardware.