Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of {\it microservices} that are mapped to edge computing and communication devices ({\it fog nodes}). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

This paper presents a residential, round-trip, mixed ownership, electric car-sharing scheme with an optimized charging schedule, expressed as a mixed integer linear programming model. Representing the given travel demand as a graph, a mixed (shared and owned cars) ownership fleet configuration, with different trip fulfillment possibilities, is determined using maximal cliques. Next, a car-to-trip allocation model for feasible trip plans, with considerations for equal car utilization and charging price, is used to generate charging profiles for the fleet. The model and the outputs can be variously integrated into existing energy system modeling frameworks. A case study, using data from a test building with a residential car-sharing fleet in Munich, demonstrates the potential of the proposed models to derive meaningful operational insights for fleet and energy system planners. 92% of the recorded trips at the test building could have been feasibly accomplished with only 60% of the available cars.

The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel’s coherence time. This work acknowledges the fact that the CSI consists of  deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, espectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.

Microfinance emerges as a pivotal catalyst within India's micro, medium, and small-scale industries. A notable exemplar is the SIDBI-PRAYAAS initiative, under the auspices of SIDBI, strategically engineered to empower the lower echelons of the financial pyramid. The latest study unveils a compelling triumph, spotlighting an impressive 6.02% Compound Annual Growth Rate (CAGR) surge in the commercial ventures of beneficiaries who availed themselves of this pioneering scheme. In this context, the infusion of digital mechanisms, including B-POS and B-Bank, has admirably streamlined the application, disbursement, and meticulous execution of loan reimbursements. The efficacy of these interventions reverberates triumphantly. Notably, the synergy of the Patanjali Ayurved Limited collaboration has further amplified the scheme's resonance, as eager borrowers harness its potential to nurture the expansion of Gramin Arogya Kendra. Considering these advancements, the current case study stands as a compelling testament to the efficacy of the PRAYAAS schemes, seamlessly interwoven with the digital dexterity of contemporary banking paradigms. This holistic approach underscores not only the scheme's operational prowess but also engenders profound customer gratification, thus forging a harmonious synthesis of financial inclusion and technological ingenuity.

We study object detection and image classification models and observe that their respective architectures are vulnerable to image distortions such as noise, compression, blur or snow. We propose alleviating this problem by training the models with antibodies generated using Artificial Immune Systems (AIS) from original training samples (antigens). These antibodies are AIS-distorted antigens at the pixel level through cycles of “select, clone, mutate, select” until an affinity to the antigen is achieved. We then add the antibodies to the antigens, train the models, validate and test them under 15 distortions, and show that our data augmentation approach (AISbod) significantly improved their accuracy without altering their architecture or inference speed. For example, YOLOv4 improves by 3.90% on average over all 15 distortions, 4.06% under snow, and 28.11%  under impulse noise. Our simulations show that compared to related defence methods, our method performs better under distortions, is more consistent across datasets and object detection models, and also under clean samples. Moreover, we show that our approach to image classification significantly improves accuracy under distortions.

In this work, we utilize symmetric Fourier features to improve the accuracy of deep learning-augmented MOSFET current-voltage model. We first apply the Fourier analysis on the cost function and discuss the spectral bias of neural network. The benefits of augmenting the neural network with symmetric Fourier features, designed to satisfy the Gummel symmetry, are then discussed. Using TCAD-simulated FinFET as our dataset, our improved model, augmented with 8 Fourier features, achieves outstanding 3-sigma errors of 0.15% for IV characteristics, 4.14% for output conductance, and 0.33% for transconductance. This represents a cost reduction of 13 times compared to the scenario without the Fourier features, highlighting the benefits gained from their inclusion.

Human Abnormal Behavior Detection is needed in several important sectors, especially in the field of public safety. Video Surveillance Systems are a trigger in detecting strange human behavior. However, this abnormal behavior data is rare and requires more costs to obtain, so the unsupervised learning method is used to study normal human behavior patterns only. There have been several previous studies that have provided significant results using this technique. There have been several previous survey studies related to human abnormal behavior detection, but they did not focus on unsupervised learning methods and the scope was too broad. In this paper, I present a survey regarding human abnormal behavior detection in more depth conceptually and classify it into two large parts, such as reconstruction-based detection and generative-based detection. This paper also offers a more extensive comparison with several popular datasets that are often used in previous research. Finally, I also shared several challenges and open research issues that emerged from our survey regarding future directions for future development.

With the development of novel display technologies such as 3-D graphical applications, virtual and augmented reality, there are new demands for more efficient and natural multi-degree-of-freedom (DOF) interaction devices. However, traditional rigid touchscreens provide only the 2-D position of the touch point as input information, which cannot meet the needs of high DOF interactions, and many of the existing interaction technologies suffer from the problems of low resolution and lack of information. At the same time, non-rigid interaction interfaces provide deformability for input and have been shown to extend the richness of input vocabulary. This article proposes a new non-rigid input device, OneTip, for single-fingertip human-computer interaction with 6-DOF. In terms of design and manufacture, OneTip employs the bio-inspired design of Skin-On interfaces to mimic the sensitivity of human skin and provide 6-DOF interaction capabilities. In terms of sensing, OneTip uses the visuotactile sensing technique based on the marker displacement method to achieve high-resolution and multi-modal measurements. We propose a novel fingertip pose estimation method based on incipient slip detection, a non-learning algorithm that does not require registration and priori information. Experiments show that OneTip had good 6-D pose estimation accuracy, with RMSEs of translation and rotation not exceeding 0.1mm and 2.6°, respectively, within the linear interval. Extensive experiments were also conducted to explore the application of OneTip in typical virtual manipulation tasks and the possibility of combining it with other interaction devices. This work is intended to serve as a reference for other researchers exploring innovative interaction techniques.

Cryptography has become an essential tool in information security, preserving data confidentiality, integrity, and availability. However, despite rigorous analysis, cryptographic algorithms may still be susceptible to attack when used on real-world devices. Side-channel attacks (SCAs) are physical attacks that target cryptographic equipment through quantifiable phenomena such as power consumption, operational times, and EM radiation. These attacks are considered to be a significant threat to cryptography since they compromise the integrity of the algorithm by obtaining the internal cryptographic key of a device by seeing its physical implementation. The literature on SCAs has focused on real-world devices, yet with the growing popularity of sophisticated devices like smartphones, fresh approaches to SCAs are necessary. One such approach is electromagnetic side-channel analysis (EM-SCA), which gathers information by listening to electromagnetic (EM) radiation. EM-SCA has been demonstrated to recover sensitive data like encryption keys and has the potential to identify malicious software, retrieve data, and identify program activity. This study aims to evaluate how well EM-SCA compromises encryption under various application scenarios, as well as examine the role of EM-SCA in digital forensics and law enforcement. Regarding this, addressing the susceptibility of encryption algorithms to EM-SCA approaches can provide digital forensic investigators with the tools they desire to overcome the challenges posed by strong encryption, allowing them to continue playing a crucial role in law enforcement and the justice system. Furthermore, this paper seeks to define the current state of EM-SCA in terms of attacking encryption, the encryption algorithms and encrypted devices that are most vulnerable and resistant to EM-SCA, and the most promising EM-SCA on encryption approaches. This study will provide a comprehensive analysis of EM-SCA in the context of law enforcement and digital forensics and point towards potential directions for further research.

Symbolic planning algorithms and large language models have different strengths and weaknesses for story generation, suggesting hybrid models might leverage advantages from both. Others have proposed using a language model in combination with a partial order planning style algorithm to avoid the need for a handwritten symbolic domain of actions. This paper offers a complementary approach. We use a state space planning algorithm to plan coherent multi-agent stories in symbolic domains, with a language model acting as a guide to estimate which events are worth exploring first. We evaluate an initial implementation of this method on a set of benchmark problems and find that the LLM's guidance is helpful to the planner in most domains.

The operating temperature of lithium-ion battery (LIB) cells significantly influences their degradation behavior. In indirect liquid cooling systems, temperature variations within a Battery Electric Vehicle (BEV) LIB module are inevitable due to the increasing downstream temperature of the cooling medium as it absorbs heat. This leads to reduced temperature differentials between the cooling medium and the LIB cells. As a result, LIB cells located further along the flow path experience higher average temperatures than those at the front. Typically, a maximum average cell temperature difference of 5 K within LIB modules is considered acceptable. However, results from a conventional cooling system indicate that, when fast charging is exclusively used, this can lead to a 15.5 % difference in the total ampere-hours passed before the End-of-Life (EOL) is reached for the front and back LIB cells. To address this issue, a switchable thermal management system for the traction battery is proposed. In this concept, the coolant inlet flow direction is reversed after every fast charge. Compared to conventional systems, this flow direction switch can increase the total ampere-hours passed by 7.2 %, leading to a corresponding 7.2 % increase in vehicle range until the first cell reaches EOL. The concepts are evaluated using a validated 1D electro-thermal-aging model. Additionally, a 3D electro-thermal Computational Fluid Dynamics (CFD) model of a module section, including a wavy-mini-channel cooling design, is employed. Together, these approaches ensure that the 1D model accurately captures the thermal behavior of the LIB module cells.

Fuzzy-rough sets (FRS) encapsulate the related but distinct concepts of vagueness (for fuzzy sets) and indiscernibility (for rough sets), both of which occur as a result of uncertainty in data, information or knowledge. The application of FRS in feature selection (FS) has employed the dependency degree to guide the FS process with much success. Whilst promising, most existing fuzzy-rough feature selection (FRFS) approaches are only conducted at the level of individual features, considering the inclusion/exclusion of individual features with regard to a candidate feature subset. In this case, the insight of meaningful information about certain inherent feature structure, such as the correlation between features or the collaborative contribution to a common decision may be ignored. To address this issue, an exclusive lasso assisted two-stage fuzzy-rough FS (EL-TSFRFS) method is presented in this paper. First, regarding discernibility, all features are divided into distinct groups using k-means clustering and the exclusive lasso regularization is utilised to select the representative features in each cluster, with such selected features sorted in descending order within the cluster. Second, a feature grouping-based FRFS algorithm is implemented to further determine the final discriminating feature subset. Comparative experimental results show that the reduct gained by the proposed approach generally outperforms those attained by alternative implementations of FRFS, in terms of both the size of the selected feature subset and the subsequent classification accuracy using the feature subset. Moreover, this is the first work to apply exclusive lasso to fuzzy-rough feature selection.

Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others' behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents' actions without knowledge of their rewards. The agents independently pursue their own rewards without any explicit motivation for teaching one another. We propose a free energy-based social bandit learning algorithm over policy space, where the social agent evaluates others' expertise levels without resorting to any oracle or social norms. Accordingly, the social agent integrates its direct experiences in the environment and others' estimated policies. The theoretical convergence of our algorithm to the optimal policy is proven. Empirical evaluations validate the superiority of our social learning method over alternative approaches in diverse scenarios. Our algorithm strategically identifies the relevant agents, even in the presence of random or sub-optimal agents, and skillfully exploiters their behavioral information. Extra to societies including expert agents, in the presence of relevant but non-expert agents, our algorithm significantly enhances individual learning performance, where most related methods fail. Importantly, it maintains logarithmic regret.

In this manuscript, one of the higher educational institutions located at Lat. 13.62ON; Long. 79.29OE; Alt. 226 M is considered and the roof architectures and  orientations of seven sheds being utilized as six workshops (WS) and one central stores (CS) are studied. NASA-SSE satellite synthetic data is used for resource assessment and PV System 7.3 software is used to estimate the annual GHI at the concerned site. Physical inspection and measurement survey has been carried out to exactly obtain the shed orientation, azimuth angle and roof dimensions including slope angle. Based on these data, gross capacity of solar PV collectors that can be installed on the roof of sheds and corresponding energy yield is estimated. Different cases have been exercised to create real-time feasibility for erecting solar PV system on the roofs of the concerned sheds. The obtained results are so intensive that for the WS and CS sheds, the daily normalized production in kWh/kWp/Day and energy yield is more on the south facing roofs (CS & WS) followed by west (WS), east (WS) and north facing roofs (WS & CS). In comparison to the vertical installation, the specific production in kWh/kWp/Annum is more with the horizontal installation of PV modules on either side of the exposed roofs for WS and less for CS. The total maximum energy that can be generated on the roofs of total seven sheds is about 9,69,566 kWh/Annum which may reduce about 824.12 Tons of CO2 emissions per annum. It is an appreciable figure and could pave a path for establishing green electricity. This research has been carried out to inspire the enthusiastic higher educational institutions to think for installing considerable capacity of solar PV system on the roofs of their workshop sheds and contribute for establishing green electricity.  In this manuscript, one of the higher educational institutions located at Lat. 13.62ON; Long. 79.29OE; Alt. 226 M is considered and the roof architectures and  orientations of seven sheds being utilized as six workshops (WS) and one central stores (CS) are studied. NASA-SSE satellite synthetic data is used for resource assessment and PV System 7.3 software is used to estimate the annual GHI at the concerned site. Physical inspection and measurement survey has been carried out to exactly obtain the shed orientation, azimuth angle and roof dimensions including slope angle. Based on these data, gross capacity of solar PV collectors that can be installed on the roof of sheds and corresponding energy yield is estimated. Different cases have been exercised to create real-time feasibility for erecting solar PV system on the roofs of the concerned sheds. The obtained results are so intensive that for the WS and CS sheds, the daily normalized production in kWh/kWp/Day and energy yield is more on the south facing roofs (CS & WS) followed by west (WS), east (WS) and north facing roofs (WS & CS). In comparison to the vertical installation, the specific production in kWh/kWp/Annum is more with the horizontal installation of PV modules on either side of the exposed roofs for WS and less for CS. The total maximum energy that can be generated on the roofs of total seven sheds is about 9,69,566 kWh/Annum which may reduce about 824.12 Tons of CO2 emissions per annum. It is an appreciable figure and could pave a path for establishing green electricity. This research has been carried out to inspire the enthusiastic higher educational institutions to think for installing considerable capacity of solar PV system on the roofs of their workshop sheds and contribute for establishing green electricity.

Facing the climate crisis, more and more renewable inverter-based resources (IBRs), such as wind energy conversion systems (WSs), replace grid-connected synchronous machines (SMs). SMs inherently provide balanced and sinusoidal grid voltages but also power system inertia, whereas standard grid-following (GFL) control of IBRs leads to decreasing power system inertia. This paper proposes grid-forming (GFM) control for doubly-fed induction machine (DFIM) based wind energy conversion systems (WSs) based on the virtual synchronous machine (VSM) concept with the following extensions: (i) Maximum power power tracking (MPPT) compensation for accurate inertia emulation, (ii) feedforward torque control (FTC) for fast power reference tracking, (iii) de-rated operation or reference power point tracking (RPPT) to provide additional power reserves, (iv) dynamic droop power saturation control to avoid excessive power overshoots (as available power reserves are taken into account), and (v) grid voltage control utilizing reactive power from both, DFIM stator and rotor-side back-to-back inverter. The modeling and control of the DFIM-based WSs is shown in detail and the grid model is based on the IEEE 9-bus test system. Comprehensive simulation results show that the GFM control enables unlimited power penetration of IBRs and that the grid frequency dynamics of VSM-based power systems can be interpreted and handled analogously to SM-based power systems. During normal operation, compared to existing VSM control without FTC, the proposed VSM control with FTC increases the wind energy yield, i. e. typical MPPT or RPPT performance is achieved, similar to the performance of GFL control. For high power penetration of IBRs, the proposed VSM control enables stable operation due to its inertia emulation or GFM capability, whereas GFL control tends to instability. During faults and system splits, the VSM provides higher power system damping than a real SM due to (i) virtual/internal damping that can be adapted by software (hence almost independently from the hardware, whereas the response of SM damper windings depends on the physical machine design and cannot be altered during operation), and due to (ii) faster droop control which adapts the virtual turbine power without the mechanical delays that dominate real turbine dynamics.

The multichannel SAR system is an advanced imaging technique that enables achieving high resolution and wide swath simultaneously. However, the fundamental hindrance in the multichannel SAR system is the failure of reception by the multiantenna system due to being in hostile working conditions. As a result of this, the received multichannel SAR data often suffer from the presence of missing samples in the azimuth domain. Besides this problem, the multichannel SAR system also has to deal with the azimuth ambiguities to obtain high azimuth resolution and wide swath. This kind of received multichannel SAR data consisting of missing azimuth samples and azimuth ambiguities cannot be processed with the conventional multichannel SAR algorithms. Therefore, we propose a novel multichannel reconstruction method to address both challenges, enabling imaging of wide swath width with high resolution from the received incomplete multichannel SAR data. Our method uses the minimum variance distortionless response (MVDR) algorithm to reconstruct an unambiguous wide azimuth spectrum. In the end, we have provided simulation results that validate the efficiency of the proposed reconstruction method. Index Terms-Synthetic aperture radar (SAR), minimum variance distortionless response (MVDR) algorithm, azimuth ambiguities, high-resolution wide-swath (HRWS), and azimuth sampling frequency.

(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)In this work, we combine microwave dielectric spectroscopy and machine learning techniques to assess the authenticity of extra virgin olive oil (EVOO). We investigate four common adulterants of EVOO, namely soybean oil, corn oil, sunflower oil, and canola oil. We use a low-cost spiral shaped microwave resonator-based sensor operating at 546.8 MHz to detect changes in the complex permittivity of oil samples. A vector network analyzer (VNA) is used to extract complex scattering parameters S11 and S21 that serves as inputs for two artificial neural network models. The first model, using only the real and imaginary parts of S21, achieves an overall accuracy of 95.8% in predicting the applied adulterant in test samples. In contrast, the second model, incorporating the real and imaginary parts of both S11 and S21, attains a 100% accuracy for test samples. Additionally, we investigate the relationship between the measured |S21| (in dB) and the adulteration level, expressed as the percentile value of the volume of adulterant per volume of the sample (mL/mL). For each adulterant, a calibration equation is developed using partial least squares regression (PLSR) to predict adulteration levels up to 50%. The maximum root mean square error (RMSE) is 2.1% for canola oil adulteration prediction. PLSR yields RMSE values of 0.9% for soybean oil, 1.1% for corn oil, and 1.0% for sunflower oil adulteration. This methodology offers both qualitative and quantitative analyses of EVOO, capable of identifying adulterations as low as 5% with a simple, portable, and practical system.

Dengue fever (DF) is a mosquito-borne communicable disease, mostly manifest during South-East Asian monsoon. Approximately 2-5% people die due to severe hemorrhagic Dengue fever and, if left untreated, the number rises up to 20%. Although it is self-limiting, some cases, referred here as Doubtful Cases’ (DfC), may abruptly turn ‘fatal’ often causing mortality in the affected population. Diagnostically apprehending such ‘fatal’ cases is a key medical challenge as clinically it is nearly impossible to predict those. This article outlines a predictive model of dengue infection through a Deep Learning (DL) approach leading to deliver a predictive tool that can accurately preempt clinical ‘morbidity’ patterns. This tool is developed and validated on a set of prototype synthetic Dengue data and ratified against independent opinions from (10) clinicians to predict and grade the infection severity of such DfC case to ca 99% accuracy. The DL approach can be generically extended to other epidemic forms with DfCs that further extends the remit of this study.