The next-generation Industrial and Tactile Internet of Things (IoT) will support smart Cyber-Physical Systems and Industry 4.0, including Smart Cities, and Industrial Automation. It will also support bandwidth-intensive applications, ie Data-Centers, Cloud Computing and the Metaverse. This paper explores ultra-high reliability and throughput in a “Software-Defined Deterministic Internet of Things”.  Multiple “Software-Defined Deterministic Wide Area Networks” (SDD-WANs) are introduced into layer 3, using simple “Deterministic Packet Switches” (D-switches).  All complex functions are removed from layer-3 hardware, and are migrated into the SDN control-plane. The resulting D-switches can be fabricated on a single \emph{Integrated Circuit}, ie FPGA. To maximize reliability, mission-critical data is routed over multiple paths. A simple Forward Error Correcting (FEC) code transmits coded data over additional paths, to tolerate edge failures.  This architecture offers many benefits: (a) “Ultra-High Reliability” is achieved, while reducing bandwidth costs; (b) “Ultra-High Throughput” is achieved, to support Data- Centers, Cloud Computing and the Metaverse; (c) The “bare-metal” D-switches use FPGAs to dramatically lower costs, with potential cost-savings reaching $1-2 Trillion (USD) over  2025…2030.  (d) The lower costs address the IEEE’s “Digital Divide”, and can potentially improve Internet access for much of the world. (e) The  Software-Defined-Networking (SDN) control-plane integrates Post-Quantum-Cryptography (PQC) and Artificial Intelligence (AI), and achieves ultra-secure Quantum-Safe communications, where each nation can achieve unprecedented protection of its critical infrastructure from external cyber-attackers.

This paper presents a novel approach for detecting faults in photovoltaic (PV) cells. The proposed method combines the power of You Only Look Once version 8 (YOLOv8) and Particle Swarm Optimization (PSO) architecture. Unlike existing methods, the proposed model leverages PSO to optimize the parameters of YOLOv8, enhancing detection accuracy. To evaluate the efficacy of the proposed approach, two experimental cases are conducted, one with a 70% training set and the other with an 80% training set. The PV system data is used as input for the model, and YOLOv8 is utilized to extract necessary features before detecting fault cells from the data. We use PSO algorithm to optimize the model’s parameters to achieve the best detection accuracy. The experimental results demonstrate that the proposed approach achieves the highest mean Average Precision (mAP) of 94% at an intersection over union (IoU) threshold of 0.5, outperforming existing fault detection methods in terms of accuracy and robustness. Moreover, by leveraging the power of YOLOv8 and PSO, the approach offers a promising solution for reliable and efficient fault detection in PV systems, thus making it a practical solution to enhance the system’s performance and reduce maintenance expenses.

Internet of Video Things (IoVT) brings much higher requirements on the transmission and computing capabilities of wireless networks than traditional Internet of Things (IoT). Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been considered as two promising technologies to satisfy these requirements. However, successive interference cancellation (SIC) and grouping operations in NOMA as well as delay sensitive IoVT video tasks with different priorities make it challenging to achieve the optimal performance in NOMA-assisted IoVT with MEC. To address this issue, we formulate a joint optimization problem where both NOMA operations and MEC offloading are involved, with the goal to minimize the weighted average total delay. To tackle such intractable problem, we proposed a graph theory-based optimization framework, then decompose and transform the problem into finding \emph{negative} loops in the weighted directed graph. Specifically, we design a priority-based SIC decoding mechanism and propose convex optimization-based power allocation and computing resource allocation algorithms to calculate the adjacency matrix. Then, two negative loop searching algorithms are adopted to obtain the device association and grouping strategies. Simulation results demonstrate that compared with existing algorithms, the proposed algorithm reduces the weighted average total delay by up to 92.43% as well as improves the transmission rate of IoVT devices by up to 79.1%.

Data is collected through an Android application for the smartphone sensors. The data label is the mode of the users annotated through the same application.

Fast Advanced Prediction of water-filled Structure using Transient Electromagnetic Method based on Deep Learning

The development of quantum computers represents a breakthrough in the evolution of computing. Their graceful processing capacity will help to solve some problems impossible until now because the algorithms that calculate their solution require too much amount of memory or processing time.  In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets.  In this work we analyze the Variational Quantum Eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. We compare the results with three other classical algorithms for the same problem, running one equivalent condition.  By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work explores the backtesting of quantum and classical computing algorithms for portfolio optimization and compares the results.  The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer.

Image analysis and compression We usually use Fourier transform, DCT transform and wavelet transform. The image transformation method based on weighted probability model is the new method described in this paper. We reasoned iterative formula of binary sequence-weighted probability model transformation versus inverse transformation, The encoding method of binary sequence transformation and inverse transformation are constructed. In the image transformation and inverse transformation experiment, yielded large number of duplicate values in the transformed data, which is suitable for the construction of a new image compression method. The inverse transform can reconstruct the image effectively, and has the obvious image edge annotation effect. This method can be used in image compression, image edge detection, image sharpening and intelligent recognition.

In this paper, a theoretical framework is proposed to study sensitivities of Machine Learning models using metric techniques. From this metric interpretation, a complete family of new quantitative metrics called  α-curves is extracted. These  α-curves provide information with greater depth on the importance of the input variables for a machine learning model than existing XAI methods in the literature.  We demonstrate the effectiveness of the  α-curves using synthetic and real datasets, comparing the results against other XAI methods for variable importance and validating the analysis results with the ground truth or literature information.

Age-related cognitive impairment is generally characterized by gradual memory loss and decision-making difficulties. The aim of this study is to investigate multi-level support and suggest relevant helping means for the elderly with mild cognitive impairment as well as their caregivers as the primary end-users. This work reports preliminary results on an intelligent assistive system achieved through the integration of the Internet of Things, augmented reality, and adaptive fuzzy decision-making methods. The proposed system operates in different modes, including automated and semi-automated modes. The former helps the user complete their daily life activities by showing augmented reality messages or making automatic changes; while the latter allows manual changes after the real-time assessment of the user’s cognitive state based on the augmented reality serious game score. We have also evaluated the accuracy of the serious game score with 37 elderly participants and compared it with users’ paper-based cognitive test results. We further noted that there is an acceptable correlation between the paper-based test and users’ serious game scores. Moreover, we observed that the system response in

The paper is formatted perfectly under the formatting template of IEEE Control Letters.

Speaker verification is a natural and effective biometric authentication method. This method may be more practical when trained using data from real, unconstrained scenarios.  However, although the speech quality from real scenarios varies because of various adverse factors, including extreme noise and the lack of identity information, most models treat all speech samples equally. To tackle this deficiency, we propose adaptive weight loss (AWL), a function that assigns different weights to samples in accordance with their quality and difficulty based on our finding that speech quality is positively correlated with the L2 norm of speaker embedding. AWL directs the model’s attention to high-quality difficult speech samples and ignores low-quality difficult samples as much as possible so that the model can learn well from speech data containing a lot of noise. Comprehensive experiments reveal that AWL significantly outperforms existing loss functions on both utilized public and noisy datasets.

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. The main disadvantage of calculations on quantum computers is their non-determinism. For optimization problems, of course, it is possible to get surprisingly good results but without a guarantee of the actual optimality of the result, i.e, an assurance that there exists no better solution which could potentially be found by the quantum machine. In this paper, we propose an novel approach that provides such a guarantee of optimality. We generate a solution that is optimal in the strict mathematical sense, without probabilistic considerations. To accomplish this, we use the D-Wave quantum machine working as a sampler implementing quantum annealing – an approach considered a hardware metaheuristic – to obtain upper and lower bounds on the value of the objective function of the problem under consideration. Then, we use the Branch and Bound scheme controlled by quantum annealing. This allows us to obtain very quickly – in constant time – the boundaries of the considered subproblems. Our approach is a combination of calculations realized on a quantum machine controlled by procedures implemented on a classical computer, allowing us to generate optimal solutions to the NP-hard problems of task scheduling on a single machine with a total weighted tardiness criterion.  The proposed quantum algorithm shows a significant advantage over the classical CPU approach both in terms of calculation time and the number of generated solution tree nodes.

We propose a new methodology for large-scale subjective quality assessment of compressed still images in the high fidelity range. Combining two different assessment protocols, one based on pairwise comparisons, the other on absolute opinions, it is designed to assess this range of qualities not well-covered by previous methodologies. The methodology was applied to create the Cloudinary Image Dataset ’22 (CID22), consisting of 22,153 annotated images (with scores based on 1.4 million opinions), originating from 250 pristine images compressed using JPEG, JPEG 2000, JPEG XL, HEIC, WebP, and AVIF at high fidelity settings. Using this data, we evaluate various image encoders and objective metrics.

As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. How- ever, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user- centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile- based security levels that can minimize the probability of a critical error given a user’s risk preferences. We evaluate our customized uncertainty quantification with four different proba- bilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty

Testbeds play an essential role in the development of real-life molecular communication applications and experimental validation of communication channel models. Although some testbed concepts have been published in recent years, very few setups are inherently suitable for biomedical applications. Furthermore, systematic experimental data of a wide parameter field for molecular communication is scarce and often difficult to generate. In this work, a biocompatible testbed for molecular communication with magnetic nanoparticles is used to investigate a series of transmission channel parameters. The observed results are discussed in the context of a laminar flow channel. All experimental data regarding the parameter studies as well as an additional data set for a large binary transmission sequence is provided as a supplement to this publication. The data is available on a public server to allow for further use by other researchers.

To achieve high efficiency and dynamics in electric drive applications, it is necessary to have accurate torque control. This is typically accomplished through a current regulator that is fed by references generated by various open-loop control strategies, in order to obtain the desired torque. As an alternative, this work presents a  model predictive torque control. Starting from the torque reference, the algorithm generates optimal voltage references to the inverter-fed synchronous motor drive, while working at maximum efficiency and considering the motor current limit. This feature is achieved by combining two different norms in the cost function. According to the paradigm of the more autonomous drives, an important feature is that the algorithm requires only knowledge of the motor model. This means that a tuning procedure for control weight is no longer required as analytically discussed in this work. Experimental validation of the proposed technique are performed on a test rig featuring an anisotropic permanent magnet motor in different dynamic operation, including flipping from the motor nominal working point to the generator one.

The increase in extreme weather events induced  by climate change, and their impact on power systems, has  created a need for tools that can assess and improve system resilience. To meet this need, the R&D team of the Energy  System Consulting segment of Tractebel Engineering GmbH has developed a novel tool called reXplan. ReXplan is a Python  library for resilient electrical system planning under extreme  hazard events, such as windstorms, earthquakes, floods,  wildfire, etc. It is designed to help power system operators and  planners make better-informed decisions to create more  resilient and secure power grids. This paper provides an overview of reXplan’s main features,  architecture, methodology of analysis and metrics. ReXplan is capable of modeling both spatiotemporal  extreme events and electrical power systems. It leverages technologies and techniques such as Julia/JuMP package and sequential Monte Carlo analysis with multivariate stratified  sampling to achieve high accuracy in the results while reducing  computational load. By quantifying resiliency metrics,  comparing different planning strategies, and validating technical solutions, reXplan can help reduce the risks of severe  outages in the grid. The software can be easily integrated into common data  science environments and is available as a Python library. For  computationally intensive tasks, such as optimal power flow,  reXplan is exploiting the fast speed of Julia programming  language, using PowerModels.jl as backend.

\cite{2024}Ocean colour spectral observations play a significant contribution in mapping the earth marine resources through measurements with its inverted geo-physical/bio-physical parameters.The retrieval of parameters from the basic sensor measurements highly depend on atmospheric scattering and absorption of light energy by its constituents. Hence the quantitative applications using these datasets directly affected by the uncertainty in radiative transfer modeling towards atmospheric scattering and absorption and associated sensor degradation with time. Here authors presented an automation of radiometric calibration approach for ocean colour monitor of Oceansat-II (Jan 2017 - Dec 2017) dataset through top of the atmosphere radiance simulation, a non-linear optimization technique. This algorithm also provides an alternative approach of calibrating the sensor vicariously through reduced dependency of systematic congruent in-situ measurements. Kavaratti in Lakshadweep, India is already a well-known site for calibrating the ocean colour sensors. The OCM cloud free images over this calibration site are utilized to perform its radiometric assessment for the year 2017 using radiative transfer model coupled with bio-optical model where the synchronous, relevant model inputs are simulated.