Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development.

This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.

Fabrication technology and structural engineering states-of-art have led to a growing use of slender structures, making them more susceptible to static and dynamic actions that may lead to some sort of damage. In this context, regular inspections and evaluations are necessary to detect and predict structural damage and establish maintenance actions able to guarantee structural safety and durability with minimal cost. However, these procedures are traditionally quite time-consuming and costly, and techniques allowing a more effective damage detection are necessary. This paper assesses the potential of Artificial Neural Network (ANN) models in the prediction of damage localization in structural members, as function of their dynamic properties – the three first natural frequencies are used. Based on 64 numerical examples from damaged (mostly) and undamaged steel channel beams, an ANN-based analytical model is proposed as a highly accurate and efficient damage localization estimator. The proposed model yielded maximum errors of 0.2 and 0.7 % concerning 64 numerical and 3 experimental data points, respectively. Due to the high-quality of results, authors’ next step is the application of similar approaches to entire structures, based on much larger datasets.

According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean Vtest / VANN =1.00) the Eurocode 2 provisions (mean VE,EC / VR,c =1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures.

Comparing experimental results on the shear capacity of steel fiber-reinforced concrete (SFRC) beams without mild steel stirrups, to the ones predicted by current design equations and other available formulations, still shows significant differences. In this paper we propose the use of artificial intelligence to estimate the shear capacity of these members. A database of 430 test results reported in the literature is used to develop an artificial neural network-based formula that predicts the shear capacity of SFRC beams without shear reinforcement. The proposed model yields maximum and mean relative errors of 0.0% for the 430 data points, which represents a better prediction (mean Vtest / VANN = 1.00 with a coefficient of variation of 1× 10-15) than the existing expressions, where the best model yields a mean value of Vtest / Vpred = 1.01 and a coefficient of variation of 27%.

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.

Massive multiple-input multiple-output (MIMO) systems operating in the centimeter-wave (cmWave) and millimeter-wave (mmWave) region offer huge spectral efficiencies, which enable to satisfy the urgent need for higher data rates in mobile communication networks. However, the proper design of those massive MIMO systems first requires a deep understanding of the underlying wireless propagation channel. Therefore, we present a fully-digital MIMO measurement system operating around 28 GHz. The system enables to take fast subsequent snapshots of the complex MIMO channel matrix. Based on this method we statistically analyze the time-dependent channel behavior, the achievable signal quality and spectral efficiency, as well as the channel eigenvalue profile. Furthermore, the presented calibration approach for the receiver enables an estimation of the dominant absolute angle of arrival (AoA) and allows us to draw conclusions about the line-of-sight (LOS) dominance of the scenario. In total, 159 uplink communication measurements over 20 seconds are conducted in three different small cell site scenarios to investigate the wireless propagation behavior. The measurements reveal the existence of several spatial propagation paths between the mobile transmitter and the base station. Furthermore, an insight into their likelihood in different propagation scenarios is also given.

Integrated Kerr micro-combs are a powerful source for generating multiple wavelength channels to achieve photonic radio frequency (RF) and microwave signal processing, particularly in the context of transversal filters. They offer competitive advantages including a compact device footprint, high versatility, large numbers of wavelengths, and wide Nyquist bands. Here, we review recent progress on Kerr micro-comb-based photonic RF and microwave high bandwidth temporal signal processing, including integral and fractional Hilbert transforms, differentiators as well as integrators. The strong potential of optical micro-combs for RF photonic applications in terms of functions and integrability is also discussed.

The continuous development of the Internet of Things (IoT) calls for innovative solutions and technologies to realize the IoT vision efficiently. One of the rising connectivity technologies that can potentially benefit the IoT deployment is Optical Wireless Communication (OWC) technology. In OWC, light beams from light sources are used to modulate information. The receiver demodulates the received light and processes the signal. The broad unlicensed spectrum of the OWC technology, along with its potential high bit-rate and increased physical link security, motivated researchers to consider OWC for IoT solutions. In this paper, we survey the existing literature related to using OWC technology in the IoT domain. We present the background and preliminaries of the IoT and OWC domains to understand how the OWC fits in the IoT architecture. Then we perform a comprehensive survey of literature related to the use of OWC technology in IoT applications. We highlight and summarize the major papers and experiments in the literature to provide researchers a jump-start to tap into the domain of OWC in IoT using the systemic and detailed survey presented in this paper.

In this paper, a laser-powered aerial mobile edge computing (MEC) architecture is proposed, where a high-altitude platform (HAP) integrated with an MEC server transfers laser energy to charge aerial user equipments (AUEs) for offloading their computation tasks to the HAP. Particularly, we identify a new privacy vulnerability caused by the transmission of wireless power transfer (WPT) signaling in the presence of a malicious smart attacker (SA). To address this vulnerability, the interaction between the HAP and the SA in their allocation of tile grids as charging points to the AUEs in laser-enabled WPT is formulated as a Colonel Blotto game (CBG), which models the competition of two players for limited resources over multiple battlefields for a finite time horizon. Moreover, the utility function that each player receives over a battlefield is developed by identifying the tradeoff between privacy protection level and energy consumption of each AUE. We further obtain the mixed-strategy Nash equilibrium for the modified CBG with asymmetric players. Simulation results are presented to show the effectiveness of this game framework.

Sub-Saharan Africa requires affordable, reliable, and sustainable electricity to boost its economic, social, and human development. The main challenge posed to the region's electricity sector is the large investment gap needed to finance new power projects. The employment of new and innovative financing options is required to bridge this investment gap. Independent power projects have become one of the fastest-growing sources of new finance in the region. However, their development is constrained by the limited availability of debt finance for project implementation. The limited capital and bureaucratic burden of traditional financial institutions coupled with the high risks in the region ensures that the debt finance required by independent power projects is raised only after an arduous voyage and at high interest rates. We address these challenges by proposing a novel decentralized finance instrument, a blockchain special purpose vehicle that streamlines the processes in the financial layer of a traditional special purpose vehicle -- finance mobilization, revenue collection, and revenue disbursal. Specifically, the proposed decentralized finance instrument facilitates the mobilization of finance for the special purpose vehicle from a location-independent crowd, revenue collection from the electricity offtaker in a risk-mitigated manner, and disbursal of eventual project revenues to investors.

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.

This research is on performance analysis of networks and no data was used.

Blockchain technology has disclosed unprecedented opportunities in the healthcare sector by unlocking the true value of interoperability. Specifically, the striking features of blockchain technology, such as data provenance, transparency, decentralized transaction validation, and immutability can help to compensate for stringent data management issues (e.g., patient recruitment, persistent monitoring, data management, and data analytics and accurate reporting) in clinical trials (CTs). Although several research studies show that blockchain solutions help to improve patient retention, data integrity, privacy, and ensure CTs compliance with regulatory policies, a comprehensive survey on this topic is lacking. In this survey, we provide insights into the adoption of blockchain technology in CTs. We categorize and classify the literature by devising a meticulous taxonomy of the decentralized tasks of CT and practices based on indispensable parameters. Furthermore, we provide insights on works in progress towards deploying blockchain solutions in CTs. Finally, we identify and discuss several challenges that hinder the successful implementation of blockchain technologies in CTs.

Abstract- Deeply implanted biomedical devices (DIBDs) are a challenging application of wireless power transfer because of the requirement for miniaturisation whilst minimising patient exposure to tissue heating. This work proposes a capacitively-coupled conductive power transfer method for DIBDs, which allows for the safe transfer of power into the body whilst using minimum implant volume. The method uses parallel insulated capacitive electrodes to couple uniform current flow into the tissue and implants. Analytical analyses are presented, which result in a two-port network that describes circuit operation. The two-port network is further simplified for typical DIBD applications where coupling to the external electrodes is low. This results in a simple circuit model of power transfer for which the parameters are easily obtained by experimental measurements. The proposed circuit model has been validated using circuit coupled finite element analysis (COMSOL) and benchtop experiments using a tissue phantom. In addition, the safety aspect of the method has been evaluated via COMSOL simulation of the specific absorption rate (SAR) for various implanted receiver dimensions and implantation depths. Finally, a completed power supply, unaffected by the implantation depth, running at 6.78 MHz, delivering 10 mW deep into the body whilst meeting the IEEE C95.1 basic restriction is presented.

Food safety is an important issue in today’s world. Traditional agri-food production system doesn’t offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and origin of the outbreak. In recent years, blockchain based food production system has resolved this challenge, however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework, which digitizes the food production information, and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, and experimental evaluation to prove that.

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65\% and 91.304 \% of CNN and kNN respectively.