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As the Internet of Things (IoT) continues to evolve, the demand for efficient hardware solutions is crucial. The paper explores the significance of hardware multiplication in IoT devices and discusses the challenges and opportunities in implementing dedicated hardware accelerators for multiplication operations. It specifically focuses on a highly efficient NxN-bit folded pipeline multiplier designed using advanced 22 nm CMOS technology and a modified hierarchical design approach. This multiplier achieves remarkable performance enhancements by reducing critical path delay through pipelining, resulting in a three-fold increase in operational speed compared to conventional multipliers. Additionally, it consumes only 0.2 mW of power and reduces chip area consumption by over 28 %. The paper emphasizes the importance of minimizing computational latency and power consumption to enable IoT devices to execute complex algorithms with improved speed and resource utilization. It also addresses trade-offs, such as area overhead and design flexibility, associated with hardware multiplication. The insights provided contribute to ongoing efforts to optimize hardware components for IoT applications, fostering the development of more capable and responsive IoT ecosystems. The study presents a rapid and energy-efficient alternative for multiplication in digital hardware, addressing the need for high-performance and energy-efficient devices in the context of the IoT. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

3D multi-object tracking (MOT) is pivotal for associating the trajectories of objects in autonomous driving. The integration of image and point cloud data in 3D MOT algorithms has emerged as a research hotspot, aiming to achieve both high precision and efficiency tracking. In this paper, we present Smart3DMOT, an novel method that leverages multi-modal detection data and incorporates a cascade matching strategy, intelligently utilizing motion and multi-modal appearance affinity for data association across frames. Our algorithm introduces two association models: the strong motion association model (SM-AM) and the multi-modal fusion feature association model (MMFF-AM), tailored to different tracking scenarios. The SM-AM robustly associates objects by enhancing motion similarity, while the MMFF-AM integrates multi-modal fusion features from images and point clouds to generate appearance similarity between objects and combine motion cues for joint reasoning. Finally, we propose a cascade tracking strategy with a "spatial tracking state confirmer" (STSC). The SM-AM and MMFF-AM models are performed staged matching facilitated by STSC. This intelligent implementation allows SM-AM or MMFF-AM to tackle instances with different tracking difficulties, maintaining the operational efficiency and tracking accuracy of trade-offs. Our approach demonstrates the effectiveness of the proposed modules through comprehensive ablation experiments and the results obtained in the KITTI and nuScenes benchmark show that our method attains state-of-the-art performance in 3D MOT. [ABSTRACT FROM AUTHOR]

Community detection is a key task in social network analysis, as it reveals the underlying structure and function of the network. Various global and local techniques exist for uncovering community structures in social networks wherein diffusion-based algorithms are proposed as novel methods for local community detection, particularly suited for large-scale networks. The efficacy of diffusion processes and initial detection is paramount in the successful identification of community structures within social networks. This effectiveness hinges significantly on the meticulous selection of the label diffuser core, which serves as the foundation for propagating labels through the network, and the precise labeling of boundary nodes. Addressing the constraints of current community detection algorithms, notably their time complexity and efficiency, this paper proposes a novel local community detection algorithm that combines core expansion with label diffusion, and deep embedding techniques. In the proposed method, a new centrality measure is introduced for appropriate core selection to facilitate precise label diffusion in the initial phase. Subsequently, a deep embedding technique is employed for updating labels of boundary and core nodes using the GraphSage embedding method. Finally, a rapid merging step is executed to amalgamate initially proximate communities into finalized community structures in large-scale social networks. We evaluate our algorithm on 14 real-world and 4 synthetic networks and show that it outperforms existing methods in terms of NMI, F-measure, ARI, and modularity. According to numerical results, the proposed method shows approximately 1.04 %, 1.03 %, and 1.12 % improvement in F-measure, NMI, and ARI measures respectively, compared to the second-best method, LBLD, in the networks with ground-truth. In addition, our method is able to accurately identify communities in large-scale networks such as Orkut, YouTube, and LiveJournal, where it ranks among the top-performing methods. Our approach exhibits the best performance in terms of ARI compared to other algorithms under comparison. [ABSTRACT FROM AUTHOR]

The Domain Name System (DNS) is a prime target for cyber attacks, necessitating the monitoring and analysis of DNS activities to detect malicious behaviors. This paper presents an innovative DNS behavioral profiling approach that addresses challenges posed by the dynamic landscape of cyber threats, encompassing issues like evasion tactics, content variability, discerning malicious intent, navigating URL obfuscation, low and slow tactics, and maintaining accuracy in the face of diverse normal behaviors, contributing to the advancement of robust threat detection. The framework leverages unique feature behaviors and correlations, incorporating a novel feature selection algorithm, pattern extraction methodology, and a robust neural network architecture for accurate profile construction. The research also includes the development of ALFlowLyzer, a custom application layer network flow analyzer, and introduces the BCCC-CIC-Bell-DNS-2024 dataset, addressing limitations in widely used public DNS datasets. Experimental results demonstrate the effectiveness of the proposed model in profiling various DNS activities. [Display omitted] • Introduction of a new graph-based feature selection algorithm. • Introduction of a behavior similarity calculation algorithm. • Introduction of DNS Flow as a vital component in DNS analysis. • Creation of a new and sophisticated behavioral profiling system through pattern extraction and neural network architecture. • Implementation of ALFlowLyzer, an application layer network flow analyzer with more than 120 DNS features. • Introduction of a new malicious DNS dataset, BCCC-CIC-Bell-DNS-2024, with a DNS flow-based approach. [ABSTRACT FROM AUTHOR]

The recent surge in electric vehicle (EV) adoption has presented various challenges, notably in the charging and discharging processes of EV batteries, each characterized by unique traits. While conventional charging stations remain popular, battery swap stations (BSS) offer a compelling alternative, addressing issues like prolonged waiting times and potential battery degradation from fast charging. BSS, with its extensive array of battery systems, ensures efficient services for EVs. However, meticulous planning for the charging and discharging operations is imperative for both BSS and the overall grid to guarantee optimal functionality. This paper proposes an efficient approach to enhance the efficiency of battery swapping and charging mechanisms (BSCM) for electric vehicles, leveraging the bat algorithm. The BSCM is conceived as a system that incorporates both the battery swapping mechanism (BSM) and the battery charging mechanism (BCM). The key contribution lies in designing an effective BSCM where the BSM functions as a manager, handling battery swapping requests from EV users, while the BCM acts as a supporter, interfacing with the grid to regulate battery charging and discharging power. To efficiently address the mixed-integer nonlinear program (MINLP) inherent in this system, a Bat algorithm is developed. The results clearly demonstrate the effectiveness of the proposed algorithm in efficiently addressing large-scale problems, producing solutions that closely approach optimality. It promptly achieves a substantial reduction in battery swapping energy by 30% and 24%, respectively, and significantly enhances charging station utilization by 25% and 21% compared to the LSTM-Based Rolling Horizon Approach and Bilevel Optimization Approach. Additionally, the algorithm showcases remarkable improvements in battery swapping performance, boasting a 25% and 19% enhancement, and noteworthy increases in charging station utilization by 20% and 17% compared to the aforementioned approaches. This enhancement in the energy exchange with grid and regulation contributes to the overall efficiency and sustainability of electric vehicle operations. [ABSTRACT FROM AUTHOR]

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these irrelevant features and redundant features may lead to local optimum. How to efficiently select the global optimal feature subset from high-dimensional data is a challenging task. To solve the problem, an adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection (ADSCO) is proposed in this paper. The contributions of this algorithm are as follows. Firstly, an adaptive dual strategy constrained optimization mechanism (ADSC) is developed. In the ADSC, the adaptive linear constraint strategy and the random constraint strategy are designed to search for the feature subsets that satisfy the constraints. This can reduce the search space of candidate solutions. Secondly, the dual population-constrained coevolutionary optimizer (CO) is developed to help the algorithm jump out of the local optimum and search for better optimal solutions to further improve the classification accuracy of ADSCO. Finally, the ablation experiments are executed to validate the effectiveness of the proposed ADSC and CO. The ADSCO algorithm is tested on 8 high-dimensional datasets and 8 low-dimensional datasets, and compared with seven typical population feature selection algorithms. Experimental results show that the proposed ADSCO algorithm is superior to the 7 comparison algorithms in terms of classification accuracy and feature subset size, demonstrating its strong competitiveness in tackling high-dimensional data feature selection. [ABSTRACT FROM AUTHOR]

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for detecting and defending against distributed denial-of-service (DDoS) and distributed reflective denial-of-service (DRDoS) flood attacks in software-defined networking (SDN) environments using Programming Protocol-Independent Packet Processors (P4). The proposed system introduces a hybrid intrusion statistical threshold algorithm (HISTA) that analyzes intrusion data statistics to identify potential malicious attacks. Upon detection, the system utilises P4 programmability to implement a custom defence mechanism on the P4 switch. A novel Uruk protocol header collaborates with HISTA for enhanced cross-layer attack detection and categorisation. The HISTA-Uruk mechanism selectively discards malicious packets while ensuring uninterrupted communication for legitimate packets, effectively preventing DDoS/DRDoS attacks. Extensive experiments validate the system's ability to efficiently detect and thwart various DDoS, DRDoS, and internal attacks, demonstrating its effectiveness in identifying threats and restoring impacted network connections across diverse attack scenarios. [Display omitted] • Proposed HISTA accurately detects network attacks using intrusion statistics. • Introducing Uruk protocol header, collaborating with HISTA in P4 SDNs. • Extensive experimentation validates HISTA Uruk mechanism efficacy. [ABSTRACT FROM AUTHOR]

This paper considers the high-dimensional table compression problem on balanced k -partite graph. The objective is to choose half of the vertices from each of the k -partite to maximize the total weight of edges connecting the chosen vertices. Our main contribution is a 0.8785-approximation algorithm for the high-dimensional table compression problem by introducing the α - i n d e p e n d e n t solutions through Lasserre semidefinite programming. This new algorithm improved two previous low-dimensional results, namely the 0.8731-approximation algorithm of Wu et al. for the one-dimensional case and the 0.6708-approximation of Xu and Du for the two-dimensional case. [Display omitted] • Present a 0.8785-approximation algorithm for high-dimensional table compression problem. • Obtain a group of independent optimal solutions through Lasserre hierarchy semidefinite programming. • The integer solutions to the original problem are obtained through deformed random hyperplane recognition. [ABSTRACT FROM AUTHOR]