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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Road Perception for Autonomous Driving: Pothole Detection in Complex Environments Based on Improved YOLOv8

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IEEE Access 2025年 13卷 80914-80929页

作者： Kong, Siyuan Meng, Qiao Li, Xin Wang, Zhijie Liu, Xin Li, Bingyu Qinghai University School of Computer Technology and Application Qinghai Xining810016 China Intelligent Computing and Application Laboratory of Qinghai Province Qinghai Xining810016 China

Road surface pothole detection is crucial for ensuring the driving safety and path planning of autonomous vehicles. However, existing detection methods are often affected by variations in lighting, weather conditions, and complex environments, resulting in lower detection precision and recall rates. To address this, this paper proposes an innovative improved algorithm, which is based on the YOLOv8 model, and introduces the MSF-HFEB module in the innovative design. By cleverly blending the strengths of Convolutional Neural Network (CNN) and the Transformer architecture, the algorithm accomplishes multi-scale feature extraction and fusion for both local and global aspects of road potholes. This design significantly improves the robustness of the algorithm under different lighting and complex environmental conditions. Additionally, the algorithm incorporates the LSKA attention mechanism and an integrated MHSA_CGLU module. By utilizing large-scale convolutional kernels for weighted feature screening, it further enhances the capability of multi-scale feature extraction and the richness of nonlinear feature representation. Experiments on public datasets have validated the effectiveness of our approach: compared to the original YOLOv8 model, the detection precision and recall rate of our algorithm have been improved by 9.8% and 11.6% respectively, and the F1 score has reached 84.9%. The outcomes of this research not only enhance the driving safety and comfort of autonomous vehicles in complex environments but also offer robust technical support for obstacle avoidance and path planning in self-driving vehicles. © 2013 IEEE.

关键词： Feature Selection

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Heap Memory Vulnerability Detection and Protection in Embedded Real-Time Operating Systems 24

Heap Memory Vulnerability Detection and Protection in Embedd...

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8th International Conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhang, Jinyuan Yao, Rui Zhang, Yang Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9798400710094

In application software development, memory defects are difficult to detect. Traditional memory defect detection tools generally face issues of high performance overhead and excessive memory consumption, which limits their applicability in embedded operating systems. To address this, a method called EHD (Embedded Heap Detect) is proposed. This method combines memory defect detection technology with the memory management mechanisms of embedded operating systems to effectively and quickly detect heap memory vulnerabilities, selecting appropriate times for detection to reduce the number of detections and lower performance overhead. © 2024 Copyright held by the owner/author(s).

关键词： Dynamic Detection

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UniTrans:Unified Parameter-Efficient Transfer Learning and Multimodal Alignment for Large Multimodal Foundation Model

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computers, Materials & Continua 2025年第4期83卷 219-238页

作者： Jiakang Sun Ke Chen Xinyang He Xu Liu Ke Li Cheng Peng Chengdu Institute of Computer Application Chinese Academy of SciencesChengdu610213China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing101499China

With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource ***,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task *** paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple *** leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter *** further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable *** evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable ***,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.

关键词： Parameter-efficient transfer learning multimodal alignment image captioning image-text retrieval visual question answering

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Platform-specific code generation method for the tyche embedded operating system based on AADL models 3

Platform-specific code generation method for the tyche embed...

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3rd International Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Chen, Li Jia, Zhangtao An, Heng Li, Haoyu Beijing Computer Technology and Application Research Institute Beijing China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The Tyche Embedded Operating System is a highly customized real-time embedded operating system, widely applied in fields such as aerospace, medical devices, and industrial automation. Renowned for its robust support for real-time tasks and efficient resource management, this system has become a core technology in embedded system development. The Architecture Analysis and Design Language (AADL), as a stringent standardized modeling language, possesses powerful expressiveness and scalability. This paper proposes a method for generating platform-specific code for the Tyche Embedded Operating System based on AADL models, which involves describing complex systems through AADL modeling and generating safety-critical software suitable for the Tyche Embedded Operating System. Firstly, the paper elaborates on the key aspects of the AADL standard and adheres to these aspects for modeling safety-critical systems. Secondly, an intermediate layer is introduced, outlining the mapping rules from the AADL common subset to the intermediate code, generating intermediate code for each component within the system architecture, and ultimately compiling it into C code applicable to the Tyche platform. Finally, a prototype tool for code generation is provided, and the effectiveness of the proposed method is validated through a case study of a temperature control system. © 2025 SPIE.

关键词： Program compilers

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Designing blockchain based secure autonomous vehicular internet of things (IoT) architecture with efficient smart contracts

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International Journal of Information technology (Singapore) 2025年第2期17卷 1207-1223页

作者： Hemani Singh, Dayashankar Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology UP Gorakhpur India

The future of autonomous vehicles is exciting and holds significant potential for transforming transportation. The future is bright, but these vehicles require a high degree of connectivity complex software to function effectively. This connectivity, however, can also create a pathway for cyberattacks. If a hacker gains access to a connected vehicle, they may be able to control its systems, steal data, or cause it to malfunction. Additionally, the software systems might have flaws or vulnerabilities that intruders could take advantage of. The paper describe how blockchain technology can help to create a tamper-proof and transparent system that enables secure sharing of data between the nodes of autonomous vehicular (AV) network. A smart contract is utilized to establish predefined rules and conditions for data sharing and access. The paper proposes a efficient approach for securing the information of AVs and improving the safety and reliability of AV networks. In this proposed work, smart contracts are implemented using solidity language and deployed a blockchain using Ganache, and truffle development framework to create a test system while MATLAB is utilized for analysis. And the system configurations are Intel Core i5 CPU (2.30 GHz and 8 GB RAM). © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Autonomous vehicles Blockchain Fan-Vercauteren Smart contracts

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An Interactive Framework for Personalized Navigation Based on Metacosmic Cultural Tourism and Large Model Fine-tuning

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IEEE Access 2025年 13卷 81450-81461页

作者： Guo, Hang Liu, Zhiqiang Tang, Chuanqian Zhang, Xiaodan Qinghai University School of Computer Technology and Application Xining China Qinghai University Qinghai Provincial Intelligent Computing and Application Laboratory Xining China

With the wide application of Large Language models (LLMs) and the rapid growth of metaverse tourism demand, the digital tour and personalized interaction of historical sites have become the key to improving users’ digital travel experience. Creating an environment where users can access rich cultural information and enjoy personalized, immersive experiences is a crucial issue in the field of digital cultural travel. To this end, we propose a tourism information multimodal generation personalized question-answering interactive framework TIGMI based on LLM fine-tuning, which aims to provide a richer and more in-depth experience for virtual Tours of historical monuments. Taking Qutan Temple as an example, the framework combines LLM, retrieval augmented generation (RAG), and auto-prompting engineering techniques to retrieve accurate information related to the historical monument from external knowledge bases and seamlessly integrates it into the generated content. This integration mechanism ensures the accuracy and relevance of the generated answers. Through TIGMI’s LLM-driven command interaction mechanism in the 3D digital scene of Qutan Temple, users are able to interact with the building and scene environment in a personalized and real-time manner, successfully integrating historical and cultural information with modern digital technology. This integration significantly enhances the naturalness of interaction and personalizes the user experience, thereby improving user immersion and information acquisition efficiency. Evaluation results show that TIGMI excels in question-answering and multimodal interactions, significantly enhancing the depth and breadth of services provided by the personalized virtual tour. We conclude by addressing the limitations of TIGMI and briefly discuss how future research will focus on further improving the accuracy and user satisfaction of the generated content to adapt to the dynamically changing tourism environment. © 2013 IEEE.

关键词： Leisure

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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Deep semantic network for hyperspectral image classification using enhanced bifurcation and light self-Gaussian attention mechanism

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Wan, Xiaoqing Chen, Feng He, Yupeng Hengyang Normal University College of Computer Science and Technology Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification due to their strong feature extraction capabilities. Nevertheless, CNN-based classification methods face challenges in capturing deep semantic features effectively, and as the number of layers increases, computational expenses escalate significantly. In addition, spatial locations and spectral bands in HSI data have varying discriminative power. To address these challenges, we propose a novel approach, DSNet, which integrates a multiway attention mechanism with a CNN-based deep semantic network to improve HSI classification. First, we extract shallow abstract features using a module that combines 3D and 2D convolution layers, followed by transformation through a Gaussian-weighted feature tokenizer. Second, we introduce an enhanced bifurcation attention mechanism with dynamically adjustable perceptual ranges to capture spatial semantic information from both horizontal and vertical perspectives. Third, a convolutional multilayer perceptron module is employed to extract high-level feature representations in both spatial and spectral dimensions, which are then refined by a light self-Gaussian attention module. Finally, a linear layer is used to determine the sample labels. Experiments conducted on three publicly available hyperspectral datasets illustrate that the proposed DSNet achieves superior performance compared with several contemporary state-of-the-art methods, even when trained with limited samples. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Convolutional neural networks

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