Search

Patients now want a contemporary, advanced healthcare system that is faster and more individualized and that can keep up with their changing needs. An edge computing environment, in conjunction with 5G speeds and contemporary computing techniques, is the solution for the latency and energy efficiency criteria to be satisfied for a real-time collection and analysis of health data. The feature of optimum computing approaches, including encryption, authentication, and classification that are employed on the devices deployed in an edge-computing architecture, has been ignored by previous healthcare systems, which have concentrated on novel fog architecture and sensor kinds. To avoid this problem in this paper, an Optimized Deep Recurrent Neural Network (O-DRNN) model is used with a multitier secured architecture. Initially, the data obtained from the patient are sent to the healthcare server in edge computing and the processed data are stored in the cloud using the Elliptic Curve Key Agreement Scheme (ECKAS) security model. The data is pre-processed and optimal features are selected using the Particle Swarm Optimization (PSO) algorithm. O-DRNN algorithm hyper-parameters are optimized using Bayesian optimization for better diagnosis. The proposed work offers superior outcomes in terms of accuracy and encryption latency while using computational cloud services. [ABSTRACT FROM AUTHOR]

Data deduplication can solve the problem of resource wastage caused by duplicated data. However, due to the limited resources of Internet of Things (IoT) devices, applying data deduplication to IoT scenarios is challenging. Existing data deduplication frameworks for the IoT are prone to inefficiency or trust crises due to the random allocation of edge computing nodes. Furthermore, side-channel attacks remain a risk. In addition, after IoT devices store data in the cloud through data deduplication, they cannot share their data efficiently. In this paper, we propose a secure and efficient data deduplication framework for the IoT based on edge computing and blockchain technologies. In this scheme, we propose a model based on parallel use of three-layer and two-layer architectures and introduce the RAndom REsponse (RARE) scheme to resist side-channel attacks. We also design a label tree to realise one-to-many data-sharing, which improves efficiency and meets the needs of the IoT. In addition, we use blockchain to resist collusion attacks. Experiments were conducted to demonstrate that our framework has advantages over similar schemes in terms of communication cost, security and efficiency. [ABSTRACT FROM AUTHOR]

Misalignment, imbalance, induced vibrations, and noise in rotating machines must be identified early on using condition monitoring and signal processing techniques. If it is not detected early, the machine’s reliability will suffer, potentially resulting in a catastrophic failure of the machine components. In this study, a web application for real-time fault detection is designed and built using a novel approach of edge computing and IoT. Vibration signature analysis are used to determine the severity of faults in machine rotating components and to provide an early warning even when the maintenance crew is located in a remote location. The vibration spectrum analysis results are successfully obtained and verified using the vibration-metre tool VibXpert-II. The purpose of this research is to improve real-time condition monitoring of rotating systems like bearings and induction motors and make it available on an online platform for predictive maintenance. Vibration signatures provide more accurate information regarding the type and location of rotor faults than current signatures. [ABSTRACT FROM AUTHOR]

Large amounts of processing resources are required for the sensed raw big data processing during the data generation process. Furthermore, as sensed data are typically privacy sensitive, blockchain technology can be used to ensure the privacy concerns. This study examines a multiuser mobile offloading network that consists of a cloud server located remotely and an edge node. We formulate the offloading problem as the joint optimization of task offloading decision making of all users, the computation resource allocation among the edge executing applications, and the radio resource assignment among all the remote-processing applications. The goal is to minimize the maximum weighted cost of all users. When compared to other benchmark approaches, the simulation results show that the proposed algorithm achieves optimal results in terms of both energy consumption and delay as a result of collaboration. Finally the resource allocation and optimal offloading strategy with 93% efficiency is obtained. [ABSTRACT FROM AUTHOR]

In this work, we construct a data-driven model to address the computing performance problem of the moving particle semi-implicit method by combining the physics intuition of the method with a machine-learning algorithm. A fully connected artificial neural network is implemented to solve the pressure Poisson equation, which is reformulated as a regression problem. We design context-based feature vectors for particle-based on the Poisson equation. The neural network maintains the original particle method's accuracy and stability, while drastically accelerates the pressure calculation. It is very suitable for GPU parallelization, edge computing scenarios and real-time simulations. [ABSTRACT FROM AUTHOR]

Emergency communication networks play a vital role in disaster monitoring, transmission, and application during disaster emergency response (DER), however, the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources. This weakness directly affects the quality of service (QoS) of the geospatial edge service (GES) chains involved in emergency monitoring. Existing research predominantly addresses service compositions in stable environments, neglecting the aggregation of efficient and robust GES chains in emergency communication networks. This study proposes an evolutionary particle swarm optimization (EPSO)-based emergency monitoring GES chain in an emergency communication network. It includes a GES chain model of emergency environment monitoring for tailing areas, as well as the designs of the particle chromosome encoding method, fitness evaluation model, and particle chromosome swarm update operators of the EPSO-based GES chain. Finally, the study conducts emergency environment monitoring experiments for tailing areas using the proposed method. Experiments results demonstrate that the proposed method significantly enhances the efficiency, stability, and reliability of emergency monitoring GES chains in the emergency communication network. This is crucial to providing fast and reliable services for DER during natural disasters. [ABSTRACT FROM AUTHOR]

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms. [ABSTRACT FROM AUTHOR]

The article deals with the creation of an intelligent architecture of the Internet of Things transport environment based on software-defined network (SDN) and blockchain for detecting threats and attacks. The transport environment is created for the monitoring system of critical events in the road transport infrastructure. Blockchain technology is used to authenticate network nodes, store sensor data in a distributed ledger. The network packet clustering method based on a fuzzy neural network is used to detect packets with possible malicious content. The intelligent SDN architecture is a hierarchy of four layers with six levels and includes: a) edge computing layer (sensor nodes and routers level, SDN switches data level), b) fog computing layer (zone server level, control level in SDN controllers), c) a cloud computing layer with data center servers, d) a layer for presenting monitoring results on user devices and applications. Detection of threats and attacks is implemented by validating network nodes and analyzing header fields of IP packets and TCP segments. The intrusion detection system includes a parser and analyzer of data packets, a module for filtering traffic by type, port numbers and other characteristics of packets, a module for synthesizing digital signatures of trusted nodes and their validation, a module for analyzing and clustering packets based on fuzzy logic and a neural network, modules for logging procedures. The probability function of packets belonging to clusters is tuned through deep learning of a five-layer neural network. The conclusion about belonging and degree of similarity with malicious packages is formed using the fuzzy logic apparatus. To train the neural network, the previously synthesized rules of the flow tables and the identified signs of atypical data packets are used. The functionality and effectiveness of the SDN architecture with an intrusion detection system is validated by simulating procedures in the NS3 Simulator system, evaluating authenticity, latency, throughput, response time, and accuracy in detecting atypical data packets. [ABSTRACT FROM AUTHOR]

Introduction: The metaverse is a new digital world powered by cutting-edge technologies that offers tremendous potential for healthcare. The metaverse is anticipated to experience enormous growth and adoption with a rising need for digital healthcare over the next decade. This article offers a comprehensive analysis of the integration of metaverse technologies into healthcare. Areas covered: This article delves into the multifaceted realm of metaverse technologies within healthcare, offering insights into its diverse contexts. Central to its exploration are the significant contributions, such as examining the current landscape of metaverse technologies in healthcare, offering a nuanced understanding of their applications. The article meticulously explores the array of tools and platforms available for surgical procedures, highlighting their efficacy and it also delves into how these technologies illuminate the evolving landscape of healthcare delivery. Through thorough analysis, this article elucidates the transformative potential of metaverse technologies in revolutionizing healthcare practices. Expert opinion: The integration of metaverse technologies in healthcare signifies a transformative shift in service delivery. The metaverse, driven by advanced technology, promises substantial changes, enhances medical education, patient care, and research through immersive experiences. This article aims to explore its various contexts heralding a new era in healthcare. [ABSTRACT FROM AUTHOR]

Accurate prediction of spatial-temporal extreme wind gust is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Deep-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the prediction accuracy only employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial-temporal fidelity. Furthermore, the Recurrent Neural Networks (RNN)-based approach previous employed exhibit low efficiency in terms of model convergence and on the aspect of practical engineering purposes. This study firstly proposed the wind gust prediction net (WGPNet), using residual learning with attention modulations to predict the instantaneous spatial-temporal wind gust in the West Pacific region with great potential wind-energy. And a public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method under different criteria. The overall RMSE of predicted gust fields obtained by the proposed method dropped to 0.18 m/s. Comprehensive discussions with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind gust prediction along timeline (the PCC equals to 0.98). [ABSTRACT FROM AUTHOR]

The detailed, high-resolution images captured by drones pose challenges to target detection algorithms with complex scenes and small-sized targets. Moreover, targets in unmanned aerial vehicle images are usually affected by factors such as viewing perspective, occlusion, and light, which increase the difficulty of target detection. In response to the above issues, we propose an improved SDMSEAFYOLOv8 for target detection based on YOLOv8, combined with a Bidirectional Feature Pyramid Network, to improve the sensing ability of the model for multiscale targets. A Space-to-depth layer replaces the traditional strided convolution layer to enhance the extraction of fine-grained information and small-sized target features. A Multi-Separated and Enhancement Attention module enhances the feature learning ability of the occluded target region, thus reducing missed and false detections. Four detection heads are employed for tiny target detection, each responsible for different size ranges, so as to improve the accuracy and robustness of small target detection. The conventional non-maximum suppression algorithm is improved, so as to reduce the problem of missed detections under a densely occluded scene by setting the attenuation function to adjust the confidence of the treated box based on the overlap between it and the highest-scoring box. Experiments demonstrate that the accuracy of SDMSEAF-YOLOv8 exceeds that of state-of-the-art models on the VisDrone2019-DET-val dataset, with a mAP of 42.9% at 640-pixel resolution, 14.8% over the baseline YOLOv8-x algorithm model, and 6.0% over the known state-of-the-art Fine-Grained Target Focusing Network model and with twice as fast detection. [ABSTRACT FROM AUTHOR]

Elliptic Curve Cryptography (ECC) is a promising cryptosystem as it presents the ideal choice for implementing public-key cryptography in resource-limited devices, such as those found in envisioned Internet of Things applications, e.g. wireless sensors. Elliptic Curve ElGamal (ECEG) is a renowned member of the ECC family. However, it requires encoding the message before encryption and decoding it after decryption. To better improve the processing time and to avoid sharing a lookup table, we propose, in this document, a new method that exchanges encoding parameters and authenticates endpoints at the same time. So, our new protocol ensures the authentication of communication parties and integrity of ephemeral encoding parameters. The robustness of our solution is proven by the Scyther tool. Experimental tests have been carried out, and the performance of the new methods is provided and compared with RSA and related work. Security and performance analysis show that our new scheme has achieved improved functionality and higher security while maintaining low computational. [ABSTRACT FROM AUTHOR]

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness. [ABSTRACT FROM AUTHOR]

Production managers seek cost-effective methods to meet the basic requirements of a modern manufacturing system, as several conventional machine tools in most manufacturing industries face abandonment or require upgrades due to challenges related to precision and accuracy. This study presents an innovative mechatronic approach that combines three key machine tool parameters: speed, pressure and alignment modules. It integrates them into conventional machine tools as an integrity upgrading enabler through real-time monitoring. A dynamic programming model was used to obtain the integrity and a generated Python software named INTEGMAC, connected seamlessly with Integrity Evaluator 1.0 hardware to analyze real-time attributes of machine tools. INTEGMAC recommends remedial actions, where necessary, to enhance machine tool integrity and was successfully tested on a Boxford 11-30 Industrial Lathe, which improved the electric motor and spindle speeds, offset and angular misalignment, hydraulic and pneumatic pressure readings to meet the manufacturer's benchmark specification. These collective modifications resulted in a significant increase in total integrity from 78.24% to 95.78%. Similar comparable results were obtained from follow-up tests to validate the performance of the developed system. This solution is currently applied in the machine tool industry and can also be extended to other industrial machines. [ABSTRACT FROM AUTHOR]

The growing of the photovoltaic (PV) panel's installation in the world and the intermittent nature of the climate conditions highlights the importance of power forecasting for smart grid integration. This work aims to study and implement existing Deep Learning (DL) methods used for PV power and electrical load forecasting. We then developed a novel hybrid model made of Feed-Forward Neural Network (FFNN), Long Short Term Memory (LSTM) and Multi-Objective Particle Swarm Optimization (MOPSO). In this work, electrical load forecasting is long-term and will consider smart meter data, socio-economic and demographic data. PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala. The second one is made of climate data collected at the climate management center in Douala. The performances of the models are evaluated using different error metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and regression (R). The proposed hybrid model gives a RMSE, MAE and R of 1.15, 0.75 and 0.999 respectively. The results obtained show that the novel deep learning model is effective in the both electrical load prediction and PV power forecasting and outperforms other models such as FFNN, Recurrent Neural Network (RNN), Decision Tree (DT), Gated Recurrent Unit (GRU) and eXtreme Gradient Boosting (XGBoost). [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) are gaining momentum due to the advantages it possesses in congestion-free traffic, reduced probability of accidents, and economical transit thus reducing the transit time, saving human life, and helping the growth of the economy. Autonomous vehicles (AV) are an important part of ITS as these are the actual actuators of the ITS. In AVs, the perception system is particularly important as this provides vital information to the motion and planning system. Any error in perception of the environment by the AV will lead to the failure of the entire ITS. This article provides a thorough review of the recent technological advancements in perception systems. In this work, we have considered peer-reviewed journals and conference proceedings on AV from 2019 onwards. The primary focus is on motion prediction models, object detection, localization, sensor data fusion, sensors, autonomous driving (AD), communication technology and AI. This article also discusses the various challenges faced by perception systems in AD and how communication technology and Artificial Intelligence (AI) can help the perception system to overcome the existing challenges. [ABSTRACT FROM AUTHOR]

Effective message dissemination in vehicular ad hoc networks requires proper management of broadcast storms for effective bandwidth usage, fast and reliable delivery of messages. Studies suggest clustering of vehicles as a means of solving broadcast storm problems due to reduction of nodes that rebroadcast emergency messages. However, clustering in an urban environment faces challenges in terms of bandwidth utilization and high End-to-End (E2E) delay due to high density of vehicles, and the number of intersections and buildings. Several cluster-based dissemination schemes have been proposed however, their model evaluations lack typical urban features, like intersections and buildings, which can adversely affect their actual performances. Thus, it is still not clear which scheme is truly the best for real-world urban scenarios. To address this challenge, a fair and thorough performance evaluation of the state-of-the-art schemes in a detailed urban setup, specifically at crossroads with buildings nearby, is urgently needed. Results deduce that, in terms of E2E delay, the Time-Barrier Emergency Message Dissemination Scheme (TBEMDS) outperforms Effective Emergency Message Dissemination Scheme (EEMDS) and Position-based Emergency Message Dissemination Scheme (PBEMDS) by 75% and 50% respectively at low traffic density. However, it is outperformed by PBEMDS by around 9% in high density. For Packet Delivery Ratio (PDR), from low to high density, EEMDS has the least percentage decrease in PDR with a 10% and 40% higher than TBEMDS and PBEMDS respectively, making it the most robust scheme in maintaining PDR as traffic density. [ABSTRACT FROM AUTHOR]

Human following is a very useful task in the robotics industry. With modern compact-sized robots, there is a demand for further investigated computer-vision solutions that can perform effectively on them. A well-known deep learning model along this line of thought is the MobileNet-Ssd, an object detection model renowned for its resource-constrained usage. Available in popular frameworks like TensorFlow and PyTorch, this model can be of great use in deployments on robotic applications. This research attempts to investigate the MobileNet-Ssd model in order to evaluate its suitability for stand-alone object detection on a Raspberry Pi. To determine the effect of input size on the model, the model's performance has been investigated with speed in frames-per-second across different input sizes on both CPU and GPU-powered devices. To evaluate the model's effectiveness in the human following task, a Raspberry Pi-based robot was designed leveraging the tracking-by-detection approach with TensorFlow-Lite. Furthermore, the model's performance was evaluated using PyTorch while the model's inputs were adjusted, and the results were compared to those of other state-of-the-art models. The investigation revealed that, despite its modest speeds, the model outperforms other noteworthy models in PyTorch and is an ideal choice when working with Raspberry Pi using TensorFlow-Lite. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) brings a set of challenges that need to be settled to enable the expected industrial revolution. Cyber-Physical Systems (CPS) and Autonomous Robots are two of mainstream I4.0 technologies and can be combined with other Enabling Technologies to overcome main I4.0 challenges. This Systematic Literature Review (SLR) aimed to understand how these Enabling Technologies were combined with CPS and Autonomous Robots to achieve fully featured I4.0 solutions. The 5C is a CPS architecture which was used as a measurement to CPS compliance. After filtering and processing 15,144 papers from six different academic search engines, it was found that full 5C compliant solutions are rare to find; that not all enabling technologies were integrated; and most of existing research focused in implementing practical solutions, but comparison to conventional approaches is scarce. We conclude that there is a need for further research to accomplish full 5C I4.0. [ABSTRACT FROM AUTHOR]

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems. [ABSTRACT FROM AUTHOR]

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed. [ABSTRACT FROM AUTHOR]

With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed. [ABSTRACT FROM AUTHOR]

The continuing prominence of machine learning has led to an increased focus on enhancing the inference performance of edge devices to reduce latency and improve efficiency. Two widely adopted strategies for accelerating computational performance are quantisation and the utilisation of AI hardware accelerators. Each type of accelerator or inference engine offers distinct advantages, with accelerators primarily designed to optimise neural network operations. In this paper, we present an innovative method for integrating TVM's quantisation flow with the MediaTek Neuropilot AI accelerator. We outline the process of converting the TVM relay intermediate-representation quantised neural network dialect model to a tensor-oriented quantisation format, with the aim of harnessing the full potential of both TVM and MediaTek NeuroPilot. This integration enables more efficient neural network inference while preserving the accuracy of the results. We assessed the effectiveness of our proposed integration by conducting a series of experiments and comparing the performance of our approach with that of TVM equipped with an autotuning mechanism. The findings indicate that our approach substantially outperforms TVM in both floating-point model inference and quantised model inference, with inference speedups of up to 11× and up to 70×, respectively. These results underscore the potential of our approach in accelerating AI performance across a diverse range of applications and edge devices. Moreover, a key contribution of our work is providing a valuable practical method for other hardware companies interested in integrating TVM with their own accelerators to achieve performance gains. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) provides an overview of the Internet of Things (IoT) and the Internet of Everything (IoE). Generally, it connects the different items, Vehicles, and Environments, which transfers data between the network. The various challenges associated with IoV are routing security and data transmission. It is a different form of the traditional intelligent transportation system, and thus, many researchers have studied routing protocols and their simulation tools. Finding all Such methodologies and their development are unavailable at a single source. Focusing on this objective, our research explores routing protocols within the Internet of Vehicles (IoV) context. Our study comprehensively reviews diverse routing algorithms and their associated assessment methodologies. To systematically categorise these protocols, we employ a multi-tiered taxonomy. Firstly, we classify them into three main groups based on their transmission strategies: unicast, geo-cast, and broadcast. Secondly, we categorise them into four classes: topology-based, position-based, map-based, and path-based. Thirdly, we organise them according to their dimensions, differentiating between 1-D, 2-D, and 3-D approaches. Finally, we classify these protocols based on their applicability to homogeneous or heterogeneous network environments. The combination of classical routing protocols with the emerging heterogeneous network paradigm is of particular interest in our research, representing a compelling area for future exploration. By presenting this extensive framework, we aim to inspire researchers in the IoV field to develop innovative and efficient routing protocols and technologies. [ABSTRACT FROM AUTHOR]

While population aging has sharply increased the demand for nursing staff, it has also increased the workload of nursing staff. Although some nursing homes use robots to perform part of the work, such robots are the type of robots that perform set tasks. The requirements in actual application scenarios often change, so robots that perform set tasks cannot effectively reduce the workload of nursing staff. In order to provide practical help to nursing staff in nursing homes, we innovatively combine the LightGBM algorithm with the machine learning interpretation framework SHAP (Shapley Additive exPlanations) and use comprehensive data analysis methods to propose a service demand prediction model Fidan (Forecast service demand model). This model analyzes and predicts the demand for elderly services in nursing homes based on relevant health management data (including physiological and sleep data), ward round data, and nursing service data collected by IoT devices. We optimise the model parameters based on Grid Search during the training process. The experimental results show that the Fidan model has an accuracy rate of 86.61% in predicting the demand for elderly services. [ABSTRACT FROM AUTHOR]

This paper proposes a particle-based nonlinear elastic object simulation technique for virtual surgery. Particle-based techniques are used to model and simulate nonlinear elastic objects, such as the skin and internal organs. This enables the simulation to consider various factors, such as location, direction, and depth, when making incisions in the organs. However, the issue with this method is that it can only simulate precisely cut tissue during incision. Objects with elasticity, such as tissue, require the generation of complex debris during incision. This paper proposes a particle-based elastic object simulation technique to model the debris from torn tissue when making an incision in the organs. It can predict where the tissue will tear based on the maximum shear stress (MSS) theory and Tresca's yield criterion when the force applied to the tissue exceeds the maximum stress. Newly generated particles at the predicted location are remeshed with nearby particles. We verified the superiority of our proposed method over traditional particle-based methods by accurately representing more complex debris and comparing the results of incisions in the same area of the body. This allows various incision types, such as stab wounds and lacerations, to be simulated. [ABSTRACT FROM AUTHOR]

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa. The modality semantic gap between recipes and food images (text and image modalities) is the main challenge. Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components. 2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features. This paper proposes a novel method based on Latent Component Weight Factors and Global Information (LCWF-GI) to learn the robust recipe and image representations for food-oriented cross-modal retrieval. This proposed method integrates the textual embeddings of different recipe components into a compact embedding to represent the recipes with the latent component-specific weight factors. A transformer encoder is utilised to capture the intra-modality interactions and the importance of different extracted image features for enhanced image representations. Finally, the bi-directional triplet loss is further used to perform retrieval learning. Experimental results on the Recipe 1M dataset show that our LCWF-GI method achieves competent improvements. [ABSTRACT FROM AUTHOR]

Third-party APIs have been widely used to develop various applications. As the number of third-party APIs grows, it becomes increasingly challenging to quickly find suitable APIs that meet users' requirements. Inspired by recommender systems, API recommendation methods have been proposed to address this issue. However, previous API recommendation methods are insufficient in utilising the high-order interactions between users and APIs, and thus have limited performance. Based on the model of lightweight graph convolutional neural network, this paper proposes an effective API recommendation method by exploiting both low-order and high-order interactions between users and APIs. It first learns the embedding of users and APIs from the user-API interaction graph, and then adopts a weighted summation operator to aggregate the embeddings learned from different propagation layers for API recommendation. Extensive experiments are conducted on a real dataset with 160,309 API users and 21,031 Web APIs, and the results show that our method has significantly better precision and recall than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In contemporary society people constantly are facing situations that influence appearance of serious diseases. For the development of intelligent decision support systems and services in medical and health domains, it is necessary to collect huge amount of patients' complex data. Patient's multimodal data must be properly prepared for intelligent processing and obtained results should be presented in a friendly way to the physicians/caregivers to recommend tailored actions that will improve patients' quality of life. Advanced artificial intelligence approaches like machine/deep learning, federated learning, explainable artificial intelligence open new paths for more quality use of medical and health data in future. In this paper, we will focus on presentation of a part of a novel Open AI Architecture for cancer patients that is devoted to intelligent medical data management. Essential activities are data collection, proper design and preparation of data to be used for training machine learning predictive models. Another key aspect is oriented towards intelligent interpretation and visualisation of results about patient's quality of life obtained from machine learning models. The Architecture has been developed as a part of complex project in which 15 institutions from 8 European countries have been participated. [ABSTRACT FROM AUTHOR]

The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. Although the combination of IoMT and federated learning (FL) can achieve no direct access to the original data of participants, FL still can't resist inference attacks against model parameters and the single point of failure of the central server. In addition, malicious clients can disguise as benign participants to launch poisoning attacks, which seriously compromises the accuracy of the global model. In this paper, we design a new privacy protection framework (BFG) for decentralized FL using blockchain, differential privacy and Generative Adversarial Network. The framework can effectively avoid a single point of failure and resist inference attacks. In particular, it can limit the success rate of poisoning attacks to less than 26%. Moreover, the framework alleviates the storage pressure of the blockchain, achieves a balance between privacy budget and global model accuracy, and can effectively resist the negative impact of node withdrawal. Simulation experiments on image datasets show that the BFG framework has a better combined performance in terms of accuracy, robustness and privacy preservation. [ABSTRACT FROM AUTHOR]

The development of 5G communication, big data technology and intelligent transportation system has driven the rapid development of vehicle network (VANET). However, with the rapid development of vehicle networks, it will be invaded by various security problems, such as privacy disclosure, session key security, forward security, etc. To eliminate the security threats faced by the vehicle network and ensure privacy and secure communication between vehicles and roadside units(RSU), it is necessary to introduce a secure, reliable, and efficient authentication key agreement scheme. Therefore, based on the principle of challenge authentication handshake protocol, we propose a novel security authentication and key agreement scheme for the vehicle networks. The scheme meets mutual authentication, and is the session key of communication protocol between vehicle and roadside unit to resist common attacks. In addition, compared with other schemes, our scheme has the advantage that we can flexibly set time keys to constrain vehicle behaviour and achieve dynamic vehicle management. We also give a formal security proof of the scheme under the random oracle model (ROM). Finally, the efficiency of the protocol is evaluated, and the computing and communication overhead of our scheme is about 35% lower than that of the existing scheme. So, our scheme is more practical. [ABSTRACT FROM AUTHOR]

Microservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of computing microservice systems, while solving the problem of latency in computing the best combination of services with the maximum QoS objective function value, this paper proposes a microservice combination approach based on the QoS model and a CGWO algorithm for optimisation computation for this model. The experimental results verify that the error rate of the method is only 0.528% on the non-functional combination optimisation problem, and the computational efficiency of the algorithm increases by 97.29% when the complexity of the problem search space increases, while CGWO improves 65.97% and 81.25% respectively in the accuracy of optimisation compared to the prototype of the algorithm (GWO), and has a stable optimisation performance, aspect. It proves that the research in this paper has a high advantage in automatically searching for the best QoS for the microservice combination problem. [ABSTRACT FROM AUTHOR]

Offloading data to cloud storage has changed the way people live and work, which reduces data management costs and increases productivity. People can use their devices to download data from cloud storage regardless of location and time. It means that data can be shared across domains through cloud storage. However, offloading data to cloud storage brings convenience as well as various challenges, such as how to solve the performance bottleneck of cloud storage and how to achieve secure data sharing in cloud storage. Blockchain has played an important role in secure data sharing based on its tamper-evident nature. In this paper, a blockchain-assisted data sharing scheme is proposed which implements the detection of deduplicated data and completes the ciphertext combination after deduplication. In order to verify the legitimacy of the ciphertext, blockchain is used to assist in verifying the ciphertext based on its tamper-evident nature. Furthermore, partial ciphertext parameters are calculated at free time to improve server operation efficiency. Finally, we simulated all algorithm of the proposed scheme on the PBC library and the results show that it achieves a balance between usability and performance. [ABSTRACT FROM AUTHOR]

Failures in urban areas' solid waste management lead to clandestine garbage dumping and pollution. This affects sanitation and public human hygiene, deteriorates quality of life, and contributes to deprivation. This study aimed to test a combination of machine learning, high-resolution earth observation and GIS data to detect diverse categories of residual waste on the streets, such as sacks and construction debris. We conceptualised five different classes of solid waste from image interpretation: "Sure", "Half-sure", "Not-sure", "Dispersed", and "Non-garbage". We tested a combination of k-means-based segmentation and supervised random forest to investigate the capabilities of automatic classification of these waste classes. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class "Sure". Moreover, a building extraction using an EfficientNet deep-learning-based semantic segmentation allowed masking the rooftops. This improved the accuracy of the classes "Sure" and "Non-garbage". The systematic evaluation of all parameters considered in this model provides a robust and reliable method of solid waste detection for decision-makers. These results highlight areas where insufficient waste management affects the citizens of a given city. The best segmentation using simple linear iterative clustering (SLIC) was achieved with the parameter values 8,000 segments and 0.3 compactness. The following supervised classification of the segmented images using Random Forest yielded an average overall accuracy of 80.18%. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class "Sure". The average reflectance values of the classes "Sure" and "non-Garbage" overlapped. Removing the building rooftops from the orthotiles reduced the overlap of the classes mentioned above. This allowed better identification of the class "Sure". Moreover, rooftop removal helped improve the accuracy of the classifier, from 59.51% to 90.18% to 71.53% to 95.76% in study areas with and without rooftops, respectively. [ABSTRACT FROM AUTHOR]

Mobile devices, including phones, tablets, and smartwatches, have revolutionized the way we compute and have seamlessly integrated into education systems. These versatile devices store vast amounts of valuable personal data due to their rich user interactions and advanced sensor capabilities. By harnessing the potential of this data through model training, we can greatly enhance the functionality and effectiveness of smart applications, providing educators with invaluable insights for making informed decisions. However, it is crucial to acknowledge the significant risks and responsibilities associated with handling such sensitive information. One notable breakthrough in this field is distributed machine learning, which enables improved accuracy and scalability by employing a multi-node system. This approach is particularly advantageous for processing larger input data sizes, allowing for enhanced performance and reduced errors. Moreover, it facilitates assisting individuals in making well-informed choices and effectively analyzing extensive datasets. This work introduces an advanced distributed intelligent model that leverages fully distributed machine learning techniques. Through a consensus mechanism and the exchange of gradients, we ensure the utmost integrity of private data pertaining to sports activities, education, training, and the health of preschoolers. The robust privacy and security features of this model make it an ideal solution for preschool organizations and educational institutions seeking to harness the power of machine learning while upholding the strictest standards of data privacy and security. [ABSTRACT FROM AUTHOR]

As part of the national strategy to enhance fitness, mass sports development is continuously being promoted and facilitated. As the living standards of Chinese residents continue to improve, the demand for health for people of all ages is increasing. Basketball, a widely played and popular sport, has flourished in recent years and has gained the love of people from different age groups. As a vital sport in the national fitness strategic plan, basketball has laid a strong foundation for the progress and development of the national fitness strategic plan in China. However, the active development of basketball in the context of national fitness has also raised the bar for the level of competence in basketball, particularly in terms of professional theoretical knowledge, professional skills, and physical quality training for basketball referees. Thus, this paper aims to combine the advancements in fog computing and deep learning to further enhance the training of basketball referees. [ABSTRACT FROM AUTHOR]

Under the mobile computing environment, a large number of convenient mobile terminals and extensive mobile network application services have been produced. The technology has been used in college sports teaching to optimize the management of sports teaching. In this context, this paper studies the application of the association rule algorithm and cluster analysis in basketball teaching, which will effectively promote the practice and application of the association rule algorithm and cluster analysis in college PE teaching in China. This paper studies basketball teaching in colleges and universities based on the applied explainable association rule algorithm and cluster analysis. It is concluded that the p value of positioning shooting in the basic basketball technology test between the experimental class and the control class before the experiment is 0.883, which is greater than 0.06, indicating that there is no significant difference between the experimental class and the control class before the experiment. The p value of round-trip straight dribble in the whole field is 0.735, which is greater than 0.07, indicating that there is no significant difference between the experimental class and the control class before the experiment. This teaching mode plays a significant role in cultivating beginners' learning interest and enthusiasm, preliminarily mastering movement skills and establishing solid technical concepts. With the complex emotional experience of the association rule algorithm, it is of great significance for teachers to grasp students' various emotional experiences in learning, cultivate students' team consciousness by cluster analysis, guide and dredge their negative emotions, and develop students' sense of cooperation, team spirit and democratic spirit, personal responsibility and personality. [ABSTRACT FROM AUTHOR]

This research paper aims to enhance the operation of visual communication systems by incorporating computer image processing technology. The study investigates the potential applications of visual communication systems by utilizing imaging equipment to capture distorted fringe images of height-modulated objects from different angles. Phase information closely associated with the object is then extracted from the images and utilized to obtain the object's three-dimensional surface topography following calibration. The paper applies multi-frequency phase-shift profilometry and Fourier transforms profilometry to construct further a visual communication system based on computer graphics processing technology. The article contributes to visual communication systems by proposing a novel approach that incorporates computer image processing technology. This approach enhances the performance of visual communication systems, resulting in a more effective means of communication. The study's experimental analysis demonstrates computer image processing technology's superiority and ability to enhance the visual communication system's application. The study's key findings indicate that computer image processing technology can effectively enhance the effectiveness of visual communication systems. The incorporation of imaging equipment and phase extraction methods leads to the production of accurate three-dimensional surface topography data. Furthermore, multi-frequency phase-shift profilometry and Fourier transform profilometry aid in constructing a visual communication system based on computer graphics processing technology, which leads to an overall enhancement of the visual communication system's application. [ABSTRACT FROM AUTHOR]

Recently, with the vigorous development of the Internet of Things (IoT) technology, all kinds of intelligent home appliances in the market are constantly innovating. The public requirements for residential safety and convenience are also increasing. Meanwhile, with the improvement of indigenous medical technology and quality of life, people's average lifespan is gradually increasing. However, countries around the world are facing the problem of aging societies. Hand gesture recognition is gaining popularity in the fields of gesture control, robotics, or medical applications. Therefore, how to create a convenient and smart control system of home appliances for the elderly or the disabled has become the objective of this study. It aims to use Google MediaPipe to develop a hand tracking system, which detected 21 key points of a hand through the camera lens of a mobile device and used a vector formula to calculate the angle of the intersection of two lines based on four key points. After the angle of bending finger is obtained, users' hand gesture can be recognized. Our experiments have confirmed that the recognition precision and recall values of hand gesture for numbers 0–9 reached 98.80% and 97.67%, respectively; and the recognition results were used to control home appliances through the low-cost IoT-Enabled system. [ABSTRACT FROM AUTHOR]

This study contributes an efficient method for assessing the structural integrity of concrete bridges by collecting images of surface cracks and defects with drones. The use of drones to gather crack samples reduces human error and is appropriate in adverse working conditions and short-duration examinations. This work introduces a new POI (Points of Interest) flying algorithm for typical flight paths, based on the drone flying criteria of safe viewing distance and the POI concept. In this research, the flight controller of the drone was combined with a Raspberry Pi4 to build and alter a typical flight path suitable for the mission, with the addition of interactive features that control flight mode switching, separation, and automatic return to the original orbit to meet expertly specified POIs. In other words, as drones approach a region requiring image capture, they automatically move to a safe distance to photograph cracks and then return to their predetermined orbit. An experiment at the Cau May bridge demonstrates that the path-planning algorithms created on an embedded computer can successfully coordinate with the flight controller switching three times to hover and zoom in on fractures during the inspection. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation. The researchers conducted a thorough survey by reviewing numerous papers and articles from respected sources such as IEEE, ACM, and Springer. Their focus centered on the latest AI and ML breakthroughs in cybersecurity, while also exploring current challenges and open research questions. The results indicate that integrating AI and ML into cybersecurity systems shows great potential for future research and development. Intrusion detection and response, malware detection, and network security are among the most promising applications identified. According to the survey, 45% of organizations have already implemented AI and ML in their cybersecurity systems, while an additional 35% plan to do so. However, 20% of organizations believe that it is not yet the right time for adopting these technologies. Overall, this paper serves as a reliable reference for researchers and practitioners in the field of cybersecurity, offering a comprehensive overview of the use of AI and ML. It not only highlights the potential applications but also addresses the challenges and research gaps. Additionally, the paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

Safety on road networks is the utmost important factor to consider for public well-being and transportation efficiency. This study introduces a new approach that combines Getis-Ord spatial statistics and crash rate analysis to identify significant road traffic accidents (RTAs) characterized by hotspots on road segments in Addis Ababa's road network. The study's results visually portray the crash locations, associating them with the underlying road network, which demonstrates a notable concentration of accident hotspots, between the years from 2014 to 2019 on Addis Ababa's roads network. The RTAs spatial analysis resulted in the identification of hotspots on 33 road segments, 3 intersections, and 10 roundabouts/squares. Among the identified hotspots, the road segment recognized as Djibouti Street, extending from Bole Edna Mall to the 22 "mazoriya" roundabouts, stands out as the most significant accident hotspot. It exhibits an average of 37.5 crashes per kilometer per year, encompassing a road segment length of 1141 m. Using both methods in this study is crucial for validating findings by identifying high-crash segments and enhancing their reliability and hotspot accuracy. This unique validation method links each traffic accident's spatial data with the road network using both crash rate and spatial statistical analysis, effectively pinpointing accident hotspots. Given the limited resources, this approach enhances awareness of accident-prone locations, enabling the prioritization of safety measures to improve road safety. It effectively addresses spatial analysis gaps related to RTAs in Ethiopia and holds practical significance by identifying and prioritizing safety measures aimed at reducing accidents within Addis Ababa's road network. [ABSTRACT FROM AUTHOR]

China's government has pledged to attain net-zero emissions by 2050 and aims to create the world's most resilient and forward-looking border by 2025. It has outlined a high-level vision for digital trade and a freeport plan and guarantees to implement new free trade agreements, develop infrastructure, and equalise the economy. Therefore, this study explores the dynamic impact of digital trade and financial development on ecological sustainability from 2000-Q1 to 2020-Q4. We apply the Bootstrap ARDL model for empirical analysis and found that digital trade in goods and services, financial development, and green innovation are conducive to long-term environmental sustainability. Similar results are also observed in the short run; however, the influence of short-run parameters is relatively lower. Moreover, the error correction term endorses convergence towards stable equilibrium with a 32.7% quarterly adjustment rate. Granger causality test report uni-direction casualty in all variables, except green innovation and carbon emissions. These findings recommend an inclusive policy for promoting digital trade, financial integration, and green innovation in China. [ABSTRACT FROM AUTHOR]

Crack detection is one of the crucial aspects of bridge evaluation and maintenance. Several existing image-based methods require capturing the bridge surface and extracting crack features to detect the crack. However, in some positions such as the space under the bridge and piers, it is difficult to capture crack images. This paper aims to apply a method to detect cracks on the bridge surface by using a drone that can capture images in challenging positions. The video recorded from the drone will be automatically identified the cracks by employing the deep learning method. Deep learning is designed for training and testing the dataset with 51.000 images, each image sized 244 × 244. The deep learning method shows the feasibility of detecting the cracks in the transport facility. This is supported by the high accuracy of the experimental results of 95.19%. In addition, the tool can assign an ID containing information to each crack from video so that these cracks can then be mounted on a 3D map of the bridge for research on crack development over time in the task of assessing the health of bridges. [ABSTRACT FROM AUTHOR]

With the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become the third most important data source, after vector maps and imagery. Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science, spatial cognition, and smart cities. However, how to acquire high-quality three-dimensional (3D) geospatial information from point clouds has become a scientific frontier, for which there is an urgent demand in the fields of surveying and mapping, as well as geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the-art of point cloud intelligence, with regard to acquisition equipment, intelligent processing, scientific research, and engineering applications. For this purpose, we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection, as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds. These projects were conducted at the Institute for Photogrammetry, the University of Stuttgart, which was initially headed by the late Prof. Ackermann. Finally, the development prospects of point cloud intelligence are summarized. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present mechanisms and algorithms implemented for improving Bitcoin cryptocurrency efficiency and security and to examine the block propagation times from a selected period before and after SegWit was introduced. In this paper, Segregated Witness Implementation issues were verified based both on the simulation and real data from the Bitcoin network. Based on the block propagation times calculated in the simulator, as well as bitcoin network real data, the efficiency and safety of Bitcoin have been analysed and validated. [ABSTRACT FROM AUTHOR]