This paper presents a novel framework for real-time object detection in Wireless Mesh Networks (WMNs) using AI driven drones. The system integrates YOLOv5 for object detection, a hybrid greedy and reinforcement learning-based routing algorithm for efficient data communication, and a scalable multihop mesh topology. MATLAB-based simulations evaluate energy consumption, detection accuracy, and network connectivity, showcasing significant performance improvements. The results demonstrate the system's ability to detect objects accurately, maintain robust connectivity, and optimize energy consumption, even in dynamic environments. Future work includes expanding the system to integrate 5G and satellite networks and conducting field tests.

We propose an integrated behavior and motion planning framework for the automated lane-merging problem. The behavior planner combines search-based planning with game theory to model the interaction between vehicles and select multivehicle trajectories. Inspired by human drivers, we model the lane-merging problem as a gap selection process. To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions. In practice, however, the surrounding vehicles might deviate from the computed equilibrium trajectories. Thus, we introduce a branch model predictive control (BMPC) framework to account for the uncertain behavior modes of the surrounding vehicles. A tailored numerical solver is developed to enhance computational efficiency by leveraging the tree structure inherent in BMPC. Finally, we validate our proposed integrated planner using real traffic data and demonstrate its effectiveness in handling interactions in dense traffic scenarios.

Pulsatile physiological signals, characterized by periodic waveform traits, are essential for patient monitoring. However, artifacts considerably compromise signal fidelity, thereby rendering the signal cleansing process essential. Although previous studies have focused on algorithmic advancements, domainspecific factors in this field are yet to be comprehensively understood. Therefore, this study conducts an empirical investigation into four critical signal cleansing aspects in the medical field. First, we verified the performance enhancement of incorporating domain knowledge into cleansing techniques. Second, we emulated real-time cleansing requirements from clinical environments. Third, we explored the similarities and differences among three evaluation approaches: quantitative assessment of the cleansing performance, qualitative assessment of the same by clinical experts, and a cleansing effect assessment on downstream tasks. Finally, we evaluated whether the willingness to accept these techniques varied according to the knowledge incorporation mode and professional groups comprising clinicians and engineers. The results revealed that domain knowledge incorporation enhances cleansing performance, visually appealing cleansing results can mislead experts, and the willingness to accept cleansing techniques varies depending on the knowledge incorporation method, professional group, and experience level.

In developing economies, logistics inefficiencies impede supply chain operations, leading to increased costs and hampering economic growth. This article investigates the pivotal role of mobile technology in optimizing logistics and enhancing Supply Chain Management (SCM), with a specific focus on Uganda. It delves into the landscape of mobile technology utilization, addressing the benefits and associated risks within logistics operations, encompassing storage, transportation, and management. The evolving concept of SCM is discussed, emphasizing its strategic significance in improving efficiency and customer satisfaction. The article systematically examines the advantages of integrating mobile technology into logistics operations, emphasizing improved collaboration, enhanced supply chain performance, resilience to disruptions, and advancements in service quality and global competitiveness. Practical insights from Uganda illustrate the application of mobile technology in the local context. However, integration introduces risks, including cybersecurity vulnerabilities, technological dependencies, and integration challenges. The paper recommends proactive measures, such as robust security protocols and contingency plans, to mitigate risks and ensure effective integration. This article targets policymakers, industry professionals, and researchers. It provides valuable insights into strategically employing mobile technology for enhanced logistics operations and SCM in developing economies, emphasizing its transformative potential in fostering efficient, resilient, and competitive supply chain ecosystems.

Symmetric nonnegative matrix factorization (Sym-NMF) is an unsupervised method that seeks to approximate a symmetric matrix A, often a similarity or kernel matrix, by the product of low-rank factors. Most existing models deal with a factorization of the form A ≈ HH T , which may not yield a satisfactory approximation in cases where the matrix is indefinite. To address this limitation, this paper proposes a new SymNMF model based on the more general formulation A ≈ HSH T , that is more stable. To ensure a low-rank representation on the positive semi-definite cone, the LogDet divergence is incorporated as a regularization term. An alternative optimization algorithm is proposed to optimize the proposed objective. Experimental results on various synthetic and real-world datasets demonstrate that the proposed model outperforms or performs comparably to many state-of-the-art methods while maintaining superior computational efficiency.

Power delay profile (PDP) of a wireless channel has been widely studied using different approaches and measurements. In this paper we take a new approach by applying Friis transmission equation to derive the PDP in a Gaussian scattering scenario. Using this approach we notice that if the scattering spread is large or the distance between the base station and the mobile station is small, the PDP has a peak at a delay greater than the line of sight delay. The obtained PDP is able to explain the peak reported in measurements and is consistent with the delay probability density function. Further, the profile comes closer to the PDP reported in the literature when either the distance between the base station and mobile station is large or the scattering spread is small.

This article has been accepted for publication at IEEE EMBC. © 2024 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.   The efficient execution of surgical operations plays a crucial role in optimizing patient outcomes, evidencing the need for effective training methods to improve surgical skills. Medical training simulators, praised for objective, automated skill assessment, require instrumented sensors and relevant metrics for targeted feedback on important aspects of a surgical procedure. Traditional metrics that capture a single instance of force, such as peak or range, lack the characterization of the entire force profile and lose subtleties that may limit accurate evaluation of the skilled application of force, a valuable aspect of assessment in surgery. This study introduces novel force metrics inspired by motion smoothness-based measures, analyzed on an extensive dataset of 97 subjects suturing on an open vascular suturing simulator. We validated the effectiveness of these metrics by comparing the metric scores for subjects with different skill levels. Our findings highlight the value of these advanced force metrics as robust indicators of suturing performance, demonstrating their valuable potential for more accurate and objective skill assessment in surgical training.

5G cellular systems are slowly being deployed worldwide delivering the promised unprecedented levels of throughput and latency to hundreds of millions of users. At such scales security is crucial, and consequently, the 5G standard includes a new series of features to improve the security of its predecessors (i.e., 3G and 4G). In this work, we evaluate the security of currently deployed 5G commercial networks in Europe and North America. Specifically, by collecting 5G signaling traffic in the wild in several cities in Spain, Germany, France, Canada, and the USA, we i) fact-check which 5G security enhancements are implemented in current deployments, ii) provide a rich overview of the implementation status of each 5G security feature in a wide range of 5G commercial networks in Europe and North America and compare it with previous results in China, iii) analyze the implications of optional features not being deployed, and iv) discuss on the still remaining 4G-inherited vulnerabilities. Our findings indicate that the rollout of 5G security features in commercial networks is still a work in progress. On the one hand, many networks continue to rely on 4G for their core network operations, which hinders the deployment of new security features (e.g., SUCI) and, on the other hand, fully-fledged 5G deployments lack mandatory security measures such as GUTI reallocation after paging. Moreover, we find that some operators fail to provide proper temporary identifier randomization, in both 4G and 5G networks. Some of the obtained results are aligned with results previously reported from China [1] and keep the European and North American studied networks vulnerable to some 4G attacks, during their migration period from 4G to 5G. Conversely, networks deployed in North America exhibit stronger adherence to 5G security standards, with near-complete compliance observed, in contrast to deployments in China and Europe where compliance levels are comparatively lower.

3D object detection plays a crucial role in autonomous driving, with Bird’s Eye View (BEV) becoming increasingly popular for its rich contextual information, ease of multimodal fusion, and scalability. Despite its advantages, current BEV-based 3D detection methods still face significant challenges, including multi-modal fusion, communication bottlenecks, robustness under varying conditions, and safety concerns. This survey provides a systematic review of recent advancements in BEV perception, organizing the research based on these focal areas. It spans a broad range of perspectives, offering valuable insights for future perception research. Additionally, this survey explores the influence of emerging technologies, such as large language models and end-to-end frameworks on enhancing BEV perception capabilities, focusing on improving performance and robustness. Key future directions include: (1) advancing from isolated vehicle perception to V2X cooperative perception; (2) evolving from single-modal to integrated multi-modal fusion; (3) shifting from simulated environments to real-world applications; and (4) transitioning from hierarchical perception frameworks to interpretable, end-to-end large-scale models.

In today's scientific landscape, Directed Acyclic Graphs (DAGs) are pivotal for representing task dependencies in data-intensive applications. Traditionally, two dominant bottomup DAG scheduling approaches exist: one overlooks communication contention and the other fails to exploit parallelization for improving latency. This study distinguishes itself by advocating a top-down approach prioritizing latency or cost optimization in multi-tier environments to fulfill QoS and SLA requirements. Our strategy effectively mitigates bandwidth contention and facilitates parallel executions, leading to substantial completion time reductions. Our findings suggest that myopic knowledgebased scheduling, emphasizing latency or cost minimization, can yield benefits comparable to its look-ahead counterparts. Through latency-efficient and cost-efficient topological sorting, our wDAGSplit strategy introduces a two-stage partitioning and scheduling approach. Its simplicity and adaptability extend its usability to DAGs of any scale. Evaluated on over 100,000 real-world DAG applications, wDAGSplit demonstrates latency enhancements of up to 80x compared to Edge-only scenarios, 15x to Near-Edge-only, and 6x to Cloud-only. In terms of cost, our approach achieves enhancements of up to 60x compared to Edgeonly scenarios, 250x to NE-only, and 70x to Cloud-only. Moreover, for DAGs with 50 tasks, we achieve 5x reduced latency compared to previous approaches, along with a complexity reduction of up to 24 times.

Heart disease has been the leading cause of mortality globally. The necessity for quick access to trustworthy, dependable, and practical processes for early diagnosis and disease management pertains to numerous risk factors for heart disease. In the current global environment, detecting heart disease through early-onset manifestations is challenging. This has the potential to be fatal if not stopped in time. In isolated, semiurban, or rural locations without access to heart specialists, accurate risk prediction and analysis may be essential for the early detection of cardiac issues. Artificial Intelligence (AI) and robotics are currently used in medical research. This addresses the urgent need for better ways to find, diagnose, and treat heart disease. To close the gap between theory and reality, we offer a dataset on cardiovascular disease that has been carefully put together. The variables in the dataset are age, gender, subtypes, symptoms, risk factors, and result variables that can be either 1 or 0. The

Vision-based Transformers have emerged as a revolutionary architecture in the field of robotics, offering significant advancements in perception, decision-making, and control. This survey aims to provide a comprehensive review of the application of Vision-based Transformers in robotic systems. We explore the fundamental principles of Transformers, highlighting their ability to handle longrange dependencies and contextual understanding, which are critical for complex robotic tasks. The paper delves into various implementations of Vision-based Transformers across different robotic domains, including object detection, autonomous navigation, manipulation, and human-robot interaction. We also discuss the challenges and limitations associated with integrating Transformers into robotic systems, such as computational demands and real-time processing constraints. Furthermore, we present a comparative analysis of Vision-based Transformers with traditional convolutional neural networks and other state-of-the-art approaches, underscoring their unique advantages. Finally, the survey identifies promising research directions and potential future applications, aiming to guide and inspire further innovations in this rapidly evolving field.

In the past decades when capacitive loads were less common. As a result, electrical networks only experienced electrical wave distortions on inductive loads, such as industrial pumps, refrigeration units, injection compressors, and electric motors; phase angle issues in the power triangle accounted for the majority of these issues. The classic, adding power factor capacitors were seem to be capable of solving majority of such issues. [1-3]. However, harmonic distortions on capacitive loads were later produced by excessive use of computers, energy-saving light bulbs, mobile chargers, and routers; These distortions had a direct influence on transformer stations and significantly affected electrical network performance. At present times, the public and private electric power generation and distribution companies around the world have recently become aware of these problems and have imposed heavy fines on this type of due to the fact that it reduces the efficiency of state-level distribution stations while simultaneously undermining the principle of equitable power distribution.

This paper delves into the realm of large language models (LLMs), which are potent tools within the domain of artificial intelligence, facilitating comprehensive comprehension and utilization of human language by computers. LLMs, exemplified by GPT-3, exhibit remarkable proficiency across various linguistic tasks, ranging from writing to language translation and customer service. However, despite their efficacy, these models are not without drawbacks; they often manifest biases, possess opaque workings, and consume substantial energy resources. Tracing the evolution of LLMs from rudimentary models to sophisticated constructs capable of intricate language processing, this paper explores their learning mechanisms, diverse applications, and associated concerns, including the imperative of fairness and privacy preservation. Looking towards the future, it advocates for research endeavors aimed at enhancing the environmental sustainability, comprehensibility, and impartiality of LLMs. The overarching objective is to harness these models effectively and ethically, cognizant of their transformative potential across technological and societal landscapes. In addition, it should be noted that the author extensively reviewed and studied over 40 articles within a span of 2 months to comprehensively acquaint themselves with the subject matter, ensuring the most effective presentation of large language models (LLMs) to the interested audience.

This paper focuses on the concept of convolution neural network for binary classification of food items. Two case studies were primarily focused : (i) identifying "rotten" and "fresh " food items using pre-trained models-VGG, ResNet50 and Xception; (ii) binary classification of "half-filled milk carton" and "completely filled milk carton" with different combination of selftrained neural network layers :dense layer, convolution layer and layer size combinations. In our first case study of binary classification, it was found that VGG had a validation accuracy of 97.5% and Xception produced an "overfitting" tendency. For our second case study, the combination with layer size =125, convolution layer = 3 and dense layer=1 had produced the highest validation accuracy of approximately 97 % and was also able to produce the most accurate prediction with different testing samples. This AI model can be implemented in smart refrigerators to let consumers know the status of their food item more accurately as compared to GoogleNet and NasNetLarge prediction.

This paper proposes a hybrid deep actor-critic framework for the optimal operation of a phase-changing soft open point (PCSOP) in an unbalanced distribution network. The framework combines algorithmic features of off-policy reinforcement learning and imitation learning. The proposed method comprises a policy-guiding module based on the PCSOP physics and an adaptive dynamic experience replay buffer. The policy-guiding module facilitates the agent’s navigation of the complex action space of the PCSOP. The dynamic experience replay accelerates agent training by leveraging expert demonstrations. As part of the design process, the paper also proposes a data-driven linearization of operational power losses in PCSOPs to enhance the convergence of nonlinear AC optimal power flow calculations without compromising accuracy. The proposed framework was trained and tested on a modified three-phase IEEE-33 bus test feeder. Results demonstrate the superiority of our framework compared to three different methods, including the conventional nonlinear AC optimal power flow.

This letter considers a reconfigurable intelligent surface (RIS)-aided indoor visible light communication system, where a mirror array-based RIS is deployed to assist the communication from a light-emitting diode (LED) to multiple user terminals (UTs). We aim to maximize the sum-rate in an entire serving period by jointly optimizing the orientation of the RIS reflecting unit, the time fraction for the UT, and the transmit power at the LED, subject to the communication and illumination intensity requirements. To solve this high-dimensional non-convex problem, we transform it as a constrained Markov decision process. Then, a soft actor-critic (SAC)-based deep reinforcement learning algorithm is proposed with the goal of maximizing both the average reward and the expected policy entropy. Simulation results prove the effectiveness of the proposed SAC-based joint optimization design in improving the sum-rate and long-term average reward.

This paper proposes a new secure and privacy-preserving prediction-enhanced solution for reliable physical-to-virtual communications in human digital twin (HDT) systems. With such a prediction-enhanced connectivity (PeHDT) framework, the evolution of any virtual twin (VT) could be triggered in real-time or in advance using the expected state of its physical counterpart. This ensures the continuous maintenance of a true replica of each physical twin (PT), thus relieving the need for timely PT-VT synchronization while the VT-experienced delay is reduced to zero or close to zero. We adopted a secured federated multi-task learning technique to meet the security and privacy constraints of HDT and employed a single server discrete-time batch-service queue framework when characterizing the batching process to reduce the communication burden. Furthermore, we introduced a prediction verification framework to improve the performance of the proposed PeHDT framework. The resulting problem was formulated as a constrained Markov decision process and was solved by introducing a primary-dual deep deterministic policy gradient (DDPG) algorithm. Through a joint investigation of communication, batching and prediction verification schemes, the simulation results show that the proposed PeHDT framework can greatly reduce both the VT-experienced delay and the PT-VT communication time without compromising the specific requirements of HDT.