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Microcontrollers to be used in harsh environmental conditions, e.g., at high temperatures or radiation exposition, need to be fabricated in robust technology nodes in order to operate reliably. However, these nodes are considerably larger than cutting-edge semiconductor technologies and provide less speed, drastically reducing system performance. In order to achieve low silicon area costs, low power consumption and reasonable performance, the processor architecture organization itself is a major influential design point. Parameters like data path width, instruction execution paradigm, code density, memory requirements, advanced control flow mechanisms etc., may have large effects on the design constraints. Application characteristics, like exploitable data parallelism and required arithmetic operations, have to be considered in order to use the implemented processor resources efficiently. In this paper, a design space exploration of five different architectures with MIPS- or ARM-compatible instruction set architectures, as well as transport-triggered instruction execution is presented. Using a 0.18 μm SOI CMOS technology for high temperature and an exemplary case study from the fields of communication, i.e., powerline communication encoder, the influence of architectural parameters on performance and hardware efficiency is compared. For this application, a transport-triggered architecture configuration has an 8.5× higher performance and 2.4× higher computational energy efficiency at a 1.6× larger total silicon area than an off-the-shelf ARM Cortex-M0 embedded processor, showing the considerable range of design trade-offs for different architectures.

In this paper, a novel population-based technique called dung beetle optimizer (DBO) algorithm is presented, which is inspired by the ball-rolling, dancing, foraging, stealing, and reproduction behaviors of dung beetles. The newly proposed DBO algorithm takes into account both the global exploration and the local exploitation, thereby having the characteristics of the fast convergence rate and the satisfactory solution accuracy. A series of well-known mathematical test functions (including both 23 benchmark functions and 29 CEC-BC-2017 test functions) are employed to evaluate the search capability of the DBO algorithm. From the simulation results, it is observed that the DBO algorithm presents substantially competitive performance with the state-of-the-art optimization approaches in terms of the convergence rate, solution accuracy, and stability. In addition, the Wilcoxon signed-rank test and the Friedman test are used to evaluate the experimental results of the algorithms, which proves the superiority of the DBO algorithm against other currently popular optimization techniques. In order to further illustrate the practical application potential, the DBO algorithm is successfully applied in three engineering design problems. The experimental results demonstrate that the proposed DBO algorithm can effectively deal with real-world application problems.

Inline assembly code is common in system software to interact with the underlying hardware platforms. The safety and correctness of the assembly code is crucial to guarantee the safety of the whole system. In this paper, we propose a practical Hoare-style program logic for verifying SPARC (Scalable Processor Architecture) assembly code. The logic supports modular reasoning about the main features of SPARCv8 ISA (instruction set architecture), including delayed control transfers, delayed writes to special registers, and register windows. It also supports relational reasoning for refinement verification. We have applied it to verify that there is a contextual refinement between a context switch routine in SPARCv8 and a switch primitive. The program logic and its soundness proof have been mechanized in Coq.

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm’s performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the problem. To evaluate the proposed algorithm, 23 standard functions and CEC2019 functions were used and compared with different types of optimization algorithms. Moreover, using the statistical test T-test and the execution time to solve the problem have been discussed. Finally, it has been tested using four machine learning and data mining problems, and the results obtained from all the analysis signifies the excellent performance of this algorithm against all kinds of problems compared to other optimizers. This algorithm has performed better than the compared algorithms in 27 benchmarks out of 33 benchmarks and has obtained better results in solving the clustering problem in 7 data sets out of 10 data sets. Furthermore, the results obtained in the problems of community detection and feature selection and MLP were superior. The source codes of the PO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/157231-puma-optimizer-po .

Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

The Internet of Things (IoT) is a paradigm that connects objects to the Internet as a whole and enables them to work together to achieve common objectives, such as innovative home automation. Potential attackers see the scattered and open IoT service structure as an appealing target for cyber-attacks. So, security cannot be dealt with independently. Security must be designed and built-in to every layer of the IoT system. IoT security concerns not only network and data security but also human health and life attacks. Therefore, the development of the loT system to provide security through resistance to attacks is a de facto requirement to make the loT safe and operational. Protecting these things is very important for system security. Plus, it is important to integrate the Intrusion Detection System (IDS) with IoT systems. IDS intends to track and analyze network traffic from different resources and detect malicious activities. It is a significant part of cybersecurity technology. In short, IDS is a process used to detect malicious activities against victims by several methods. Besides, the method of Systematic Literature Review (SLR) is used to classify, review, and incorporate results from all similar research that answers one or more IDS research topics and perform a detailed empirical research analysis on IDS techniques. Furthermore, depending on the detection technique, we classify IDS approaches in IoT as signature-based, anomaly-based, specification-based, and hybrid. Also, for the IDS approaches, the authors give a parametric comparison. The benefits and drawbacks of the chosen mechanisms are then addressed. Eventually, there is an analysis of open problems as well as potential trend directions.

In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.