IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-09-05 DOI: 10.1002/dac.70253

Felicia Engmann, Kofi Sarpong Adu-Manu, Jamal-Deen Abdulai, Ferdinand Apietu Katsriku

{"title":"Optimizing Packet Size for Enhanced Performance in Wireless Sensor Networks for Environmental Monitoring Applications","authors":"Felicia Engmann, Kofi Sarpong Adu-Manu, Jamal-Deen Abdulai, Ferdinand Apietu Katsriku","doi":"10.1002/dac.70253","DOIUrl":"https://doi.org/10.1002/dac.70253","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless sensor networks (WSNs) are widely used in environmental monitoring applications (EMAs) for water quality, air quality, and structural health monitoring. However, the performance of WSNs in EMAs depends on various network parameters such as packet size, data rate, and bit error rate. Packet size affects network reliability, which is evident in performance metrics, such as energy consumption, delay, and throughput. In this study, we investigated a method used to measure the effect of packet size on the performance of an IEEE 802.11 network in typical EMAs. The NS-3 network simulator compares the packet sizes (32, 64, 128, 512, and 1024 bytes) for different network densities (100, 120, 140, 160, 180, and 200 nodes, respectively) in a 100 × 100 m square area. This paper provides a comprehensive performance analysis of WSN applications for different node densities for performance metrics such as energy consumption, latency, and packet delivery ratios. The study also found intermediate packet sizes of 64 and 128 bytes, proving improved network performance for the chosen performance metrics. The results were validated against results from related studies on WSN implementation in smart grids and other underwater communications. We report that the optimum average end-to-end delay achieved in our simulations was 10.88 s for the 128-byte packet size, whereas the optimum packet delivery ratio was 0.178 for the 64-byte channel. The remaining energy on the channel was 27.9% for 64 bytes, which is better than other channels with 25% energy remaining.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 15","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An evolutionary algorithm for multimodal multi-objective traveling salesman problems 多模态多目标旅行商问题的一种进化算法

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-09-05 DOI: 10.1016/j.eswa.2025.129540

Caitong Yue , Jiankang Song , Mengnan Liu , Ying Bi , Weifeng Guo , Hongyu Lin , Jing Liang

{"title":"An evolutionary algorithm for multimodal multi-objective traveling salesman problems","authors":"Caitong Yue , Jiankang Song , Mengnan Liu , Ying Bi , Weifeng Guo , Hongyu Lin , Jing Liang","doi":"10.1016/j.eswa.2025.129540","DOIUrl":"10.1016/j.eswa.2025.129540","url":null,"abstract":"<div><div>The multi-objective Traveling Salesman Problem (TSP) extends the classical TSP by simultaneously optimizing multiple conflicting objectives, such as minimizing travel cost and maximizing route diversity. While existing multi-objective TSP algorithms focus on convergence and diversity in the objective space, they often overlook the multimodal nature of the problem–where structurally distinct tours may map to similar objective values. This gap leads to the loss of high-quality solutions that could offer decision-makers valuable alternatives. Therefore, it is essential to study traveling salesman problems with multimodal and multi-objective characteristics. To address this gap, this paper conducts an in-depth study of the Traveling Salesman Problem with multimodal and multi-objective characteristics (MMTSP) and proposes an algorithm called MMTSP_DS. This algorithm combines the Spearman distance with a diversity measurement criterion based on shared edges in the decision space, allowing for a more accurate assessment of the similarity between different tour solutions. Additionally, the algorithm incorporates the concept of a special crowding distance into the environmental selection process to ensure that the population maintains diversity in both the decision space and the objective space in a balanced manner. Finally, a series of experiments are conducted to systematically compare the proposed MMTSP_DS algorithm with state-of-the-art algorithms designed for multimodal multi-objective TSP. The experimental results verify that MMTSP_DS significantly improves population diversity and optimization performance, demonstrating its considerable advantages.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"297 ","pages":"Article 129540"},"PeriodicalIF":7.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pixel-Level Semantics Boosted Fine-Grained Bird Image Classification 像素级语义增强细粒度鸟类图像分类

IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-09-05 DOI: 10.1016/j.engappai.2025.112089

Haoxiang Ma , Yongjian Deng , Bochen Xie , Jian Liu , Hai Liu , Youfu Li , Zhen Yang

{"title":"Pixel-Level Semantics Boosted Fine-Grained Bird Image Classification","authors":"Haoxiang Ma , Yongjian Deng , Bochen Xie , Jian Liu , Hai Liu , Youfu Li , Zhen Yang","doi":"10.1016/j.engappai.2025.112089","DOIUrl":"10.1016/j.engappai.2025.112089","url":null,"abstract":"<div><div>Fine-grained bird image classification (FBIC) is crucial for endangered bird conservation and biodiversity research. However, existing methods often struggle to capture detailed features and manage the interference caused by complex backgrounds. To address these challenges, we propose a novel Pixel-Level Semantic Boosted Fine-Grained Bird Image Classification (PFIC) framework, which enhances fine-grained bird image classification by incorporating pixel-level semantic information. PFIC consists of two core components: the Grouped Detail Enhancement (GDE) module and the Background–Foreground Enhancement (BFE) strategy. GDE integrates multi-level pixel-level semantic information, derived from a segmentation feature extractor, into classification features via two submodules: grouped aggregation and detail enhancement. This approach enhances the model’s ability to capture fine-grained details. BFE augments training samples by restricting background ranges and applying random shifts to foreground objects, thereby improving the model’s capability to recognize foreground objects in complex environments. Experimental results demonstrate that our method achieves state-of-the-art performance on the CUB-200-2011 and NABirds datasets. Additionally, further experiments on the Stanford Cars dataset validate the framework’s potential for generalization to other fine-grained image classification tasks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"161 ","pages":"Article 112089"},"PeriodicalIF":8.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Tutorial on Discriminative Clustering and Mutual Information 判别聚类与互信息教程

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-05 DOI: 10.1145/3748255

Louis Ohl, Pierre-Alexandre Mattei, Frederic Precioso

{"title":"A Tutorial on Discriminative Clustering and Mutual Information","authors":"Louis Ohl, Pierre-Alexandre Mattei, Frederic Precioso","doi":"10.1145/3748255","DOIUrl":"https://doi.org/10.1145/3748255","url":null,"abstract":"To cluster data is to separate samples into distinctive groups that should ideally have some cohesive properties. Today, numerous clustering algorithms exist, and their differences lie essentially in what can be perceived as “cohesive properties”. Therefore, hypotheses on the nature of clusters must be set: they can be either generative or discriminative. As the last decade witnessed the impressive growth of deep clustering methods that involve neural networks to handle high-dimensional data often in a discriminative manner; we concentrate mainly on the discriminative hypotheses. In this paper, our aim is to provide an accessible historical perspective on the evolution of discriminative clustering methods and notably how the nature of assumptions of the discriminative models changed over time: from decision boundaries to invariance critics. We notably highlight how mutual information has been a historical cornerstone of the progress of (deep) discriminative clustering methods. We also show some known limitations of mutual information and how discriminative clustering methods tried to circumvent those. We then discuss the challenges that discriminative clustering faces with respect to the selection of the number of clusters. Finally, we showcase these techniques using the dedicated Python package, GemClus , that we have developed for discriminative clustering.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"304 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cover 3 覆盖3

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-05 DOI: 10.1109/mcom.2025.11152674

引用次数: 0

IoT World Forum 物联网世界论坛

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-05 DOI: 10.1109/mcom.2025.11152688

引用次数: 0

IEEE ComSoc Training IEEE ComSoc培训

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-05 DOI: 10.1109/mcom.2025.11152703

引用次数: 0

Generative AI-Enabled Integrated Sensing and Communication 生成式人工智能集成传感与通信

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-05 DOI: 10.1109/mcom.001.2400746

Jifa Zhang, Min Sheng, Junyu Liu, Nan Zhao, Xianbin Wang

引用次数: 0

A Framework for Energy Efficiency Optimization in HMA-Assisted MU-MIMO Systems hma辅助MU-MIMO系统的能效优化框架

IF 8.3 2区计算机科学

IEEE Transactions on Communications Pub Date : 2025-09-05 DOI: 10.1109/tcomm.2025.3606661

Xin Ju, Chengwen Xing, Heng Liu, Shiqi Gong, Nan Zhao, Dusit Niyato

引用次数: 0

2025 Conference Calendar 2025年会议日历

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-05 DOI: 10.1109/mcom.2025.11152705

引用次数: 0

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