Wireless Personal Communications最新文献

In artificial intelligence, the large role is played by machine learning (ML) in a variety of applications. This article aims at providing a comprehensive survey on summarizing recent trends and advances in hardware accelerator design for machine learning based on various hardware platforms like ASIC, FPGA and GPU. In this article, we look at different architectures that allow NN executions in respect of computational units, network topologies, dataflow optimization and accelerators based on new technologies. The important features of the various strategies for enhancing acceleration performance are highlighted. The numerous current difficulties like fair comparison, as well as potential subjects and obstacles in this field has been examined. This study intends to provide readers with a fast overview of neural network compression and acceleration, a clear evaluation of different methods, and the confidence to get started in the right path.

To explore the feasibility of stellate ganglion block (SGB) for the treatment of brain injury and the inhibitory effect of neuronal apoptosis. SD rats were randomly divided into control group, model group (LPS 0.6 mg/kg), and SGB group (LPS 0.6 mg/kg + lidocaine 0.2 mL). The rats were sacrificed after SGB block for 2 h to separate the hippocampus. The pathological changes of hippocampal tissue were observed by HE staining, the apoptosis of hippocampal neurons was observed by TUNEL staining, the positive expression of B-cell lymphoma-2-Associated X (Bax) in hippocampal tissue was detected by immunohistochemistry, and the expression of Bax mRNA in hippocampal tissue was detected by qRT-PCR method. Results: Compared with the control group, the hippocampal tissue of the model group was severely damaged, and the apoptosis rate of hippocampal neurons, the positive rate of Bax protein, the expression of Bax mRNA, and the protein expressions of Baxwere significantly increased, the protein expression of Bcl-2 was significantly decreased (P < 0.05).Compared with the model group, the hippocampal tissue damage of the SGB group was significantly reduced, and the hippocampal neuron apoptosis rate, Bax protein positive rate, Bax mRNA expression and Bax protein expressions were significantly decreased, and the expression of Bcl-2 protein was significantly increased (P < 0.05). Conclusion: SGB can reduce the apoptosis of neurons in rats and improve brain injury by inhibiting the expression of Bax gene.

The Internet of Things (IoT) represents a network framework comprising identifiable entities that interact through the Internet. One of its applications is the smart home, where household devices can be remotely monitored and controlled. This has led to an increased demand for reliable security solutions in IoT systems. Security presents a significant challenge in IoT smart home devices and must be carefully considered. Unauthorized access to a smart home system, facilitated by means such as jamming or replay attacks, could pose risks by manipulating sensors and controls, potentially allowing unauthorized entry. This review paper concentrates specifically on the security and privacy aspects of IoT smart home access control devices. It begins with a concise overview of smart home security and privacy, then delves into various techniques within the smart home system taxonomy, such as authentication, access control, blockchain, and cryptography-based methods. Furthermore, the paper compares the advantages and disadvantages of these techniques. It also examines various types of attacks on smart home IoT access control systems and evaluates risk factors such as methodologies, attack frequency, severity, probability, and ranking. Finally, the paper discusses challenges, applications, conclusions, and future directions.

Increasing energy utilization is one of the main obstacles to reliable interaction in dynamic digital networks. We concentrate on improving the network’s topology by taking this difficulty into account. In this study, we build a hybrid star-mesh topology to minimize both the energy usage and latency of the network. AOMDV routes is used in hybrid network topologies to create various pathways in the distance from the point of origin and the goal. When a routing path loses its energy level, the best path is selected using ChOA from the available options. The system’s energy efficiency is improved by choosing the best route. According to mimicry findings, the suggested method performs better when thinking of supply number, energy consumption, delay, and throughput.

The power grid, manufacturing, and industrial automation are just a few Internet of Things (IoT) settings where publish/subscribe (p/s) systems are increasingly prevalent. These systems may handle a wide range of middleware and communication protocols, ensuring compatibility. The most well-liked publish/subscribe protocol is the Message Queue Telemetry Transport Protocol (MQTT), which uses an agent to transfer information between publishers and subscribers on certain subjects. MQTT can be quickly and simply deployed for IoT settings using a popular wireless MAC layer protocol like Long Range Wide Area Network (LoRaWAN), however, this has not been properly validated. MQTT can be readily set up in cloud environments to do research experiments. To provide an MQTT-based publication design that can handle the LoRaWAN proactive steps, the authors design and provide a simulation framework in NS-3 in this study. To do this, the authors make use of the LoRaWAN library from NS-3 and include connecting it with a middleman that links to numerous publications as well as clients. The authors support many topics at the broker while enabling unicast capabilities from the broker to LoRaWAN end devices. In other words, the proposed work activates the unicast capability from the middleman to LoRaWAN peripheral devices while handling multiple topics at the mediator. To illustrate the viability of our IoT architecture and evaluate its performance at scale, the authors performed several scenarios under it.

The data being produced by the devices connected to the Internet is ever growing. The large volume of data is also required to be processed and stored. Cloud computing provides data processing and storage services by using IT resources at gegraphically distributed locations. However, the cloud computing resources are usually far away from the sites where data is produced and processing is needed, which results in communication latencies that may not be acceptable for some appliations. This issue is addressed by Fog computing by brining data processing closer to the edge of the Internet. As fog infrastructures are significantly different from cloud in terms of scale, processing capacity, bandwidth etc.; more focused analysis is needed to explore avenues for performance improvement. In this paper, we analyze effects of resource distribution in small-scale fog computing infrastructure on its performance and resource utilization. We examine different topological arrangements of fog devices with varying resources. Our results show that the topological arrangement of fog nodes has a significant impact on the overall performance of fog computing infrastructure.

The Rabin Cryptosystem is a public-key cryptosystem known for providing security levels comparable to RSA but with reduced computational overhead. Despite these advantages, it has not been widely adopted for practical use due to its lack of a deterministic nature. This paper addresses this limitation by introducing a new Deterministic Rabin Cryptosystem (DRCS). The DRCS framework includes processes for key generation, encryption, and decryption, leveraging the concept of cubic congruence and the Chinese Remainder Theorem to ensure the decryption process is unambiguous and deterministic. This design not only retains the computational efficiency of the original Rabin Cryptosystem but also enhances its security. Our comparative analysis shows that the DRCS achieves similar performance to the traditional Rabin system in terms of computational overhead. While the encryption process in DRCS is less demanding, its decryption process is more complex, and overall, it maintains a polynomial time complexity. Furthermore, a detailed security analysis indicates that the DRCS is significantly harder to factorize compared to previous models, underscoring its improved security features.

Clustering is a main component for establishing WSN routing since it promotes scalability and increases network lifetime. Cluster heads (CHs) in a clustered WSN allocate their maximum energy to a different node. Despite the widespread use of WSNs, only a few applications require rapid data transport with less interruption. Many applications concentrate on throughput while paying little attention to latency. Thereby, there arises the need to develop a new secure-aware routing in WSN. This paper aims to propose a WSN cluster-based routing model that follows: (i) Cluster head selection (CHS) and (ii) Routing. Initially, the CHS is made optimal to ensure the appropriate clustering of nodes in the WSN. Moreover, the CHS is carried out by considering risk, delay, energy and distance. This parameter consideration ensures the energy efficiency and the security aspect while routing the data packets. For this optimal CHS, a new Remora Customized Shark Optimization (RCSO) is introduced. After clustering using the optimal CH, optimal routing takes place by concerning link quality (proposed evaluation), trust (proposed trust evaluation) and distance (inter-cluster and intra-cluster distance). The optimal routing is performed using a novel RCSO algorithm. At last, the performance of the suggested RCSO is analyzed over conventional approaches with regard to several factors. The residual energy using RCSO is maximum around 0.28 at the 2000th iteration when compared to conventional methods.

Different compression techniques necessitate the signal to be sparse or have a sparse representation in a suitable domain. Among these methods, compressed sensing is regarded as highly efficient. Sparse coding seeks to identify a sparse group of coefficients that effectively depicts a signal from a predetermined overcomplete dictionary. In the context of practical signal processing, quantization of sampled signals marks a crucial step in digitizing signals, and subsequently, realizing their high-efficiency transmission. In this study, we introduce a novel approach that integrates the challenges inherent in the approximation of sparse signals with coefficient quantization. In this study, a new method called Adaptive Quantized Iterative Thresholding is introduced, which utilizes Signal-to-Noise Ratio (SNR) to enhance classical iterative thresholding techniques for identifying sparse signal representations. Simulation outcomes demonstrate a notable enhancement in operational SNR performance when compared to Quantized Iterative Thresholding methods.

Neurological disease is one of the most common types of dementia that predominantly concerns the elderly. In clinical approaches, identifying its premature stages is complicated, and no biomarker is comprehended to be thorough in witnessing neurological disorders in their earlier stages. Deep learning approaches have attracted much attention in the scientific community using scanned images. They differ from simple machine learning (ML) algorithms in that they study the most favorable depiction of untreated images. Deep learning is helpful in the neuroimaging analysis of neurological diseases with subtle and dispersed changes because it can discover abstract and complicated patterns. The current study discusses a vital part of deep learning and looks at past work that has been used to switch between different ML algorithms that can predict neurological diseases. Convolution Neural Networks, Generative Adversarial Network, Recurrent Neural Network, Deep Belief Network, Auto Encoder, and other algorithms for Alzheimer’s illness prediction have been considered. Many publications on preprocessing methods, such as scaling, correction, stripping, and normalizing, have been evaluated.