Sensors最新文献 - Book学术

A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration. 防止医疗网络被入侵的深度加密框架。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217089

Arjun Singh, Vijay Shankar Sharma, Shakila Basheer, Chiranji Lal Chowdhary

{"title":"A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration.","authors":"Arjun Singh, Vijay Shankar Sharma, Shakila Basheer, Chiranji Lal Chowdhary","doi":"10.3390/s24217089","DOIUrl":"10.3390/s24217089","url":null,"abstract":"Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user's identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model. 基于 sEMG 和 CNN-TL 融合模型的下肢运动识别。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217087

Zhiwei Zhou, Qing Tao, Na Su, Jingxuan Liu, Qingzheng Chen, Bowen Li

{"title":"Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.","authors":"Zhiwei Zhou, Qing Tao, Na Su, Jingxuan Liu, Qingzheng Chen, Bowen Li","doi":"10.3390/s24217087","DOIUrl":"10.3390/s24217087","url":null,"abstract":"To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining these advanced techniques, significant improvements in movement classification were achieved. Firstly, sEMG data were collected from 20 subjects as they performed four distinct gait movements: walking upstairs, walking downstairs, walking on a level surface, and squatting. Subsequently, the gathered sEMG data underwent preprocessing, with features extracted from both the time domain and frequency domain. These features were then used as inputs for the machine learning recognition model. Finally, based on the preprocessed sEMG data, the CNN-TL lower limb action recognition model was constructed. The performance of CNN-TL was then compared with that of the CNN, LSTM, and SVM models. The results demonstrated that the accuracy of the CNN-TL model in lower limb action recognition was 3.76%, 5.92%, and 14.92% higher than that of the CNN-LSTM, CNN, and SVM models, respectively, thereby proving its superior classification performance. An effective scheme for improving lower limb motor function in rehabilitation and assistance devices was thus provided.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting. TEA-GCN：用于交通流量预测的变换器增强型自适应图卷积网络。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217086

Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang

引用次数: 0

ARMNet: A Network for Image Dimensional Emotion Prediction Based on Affective Region Extraction and Multi-Channel Fusion. ARMNet：基于情感区域提取和多通道融合的图像维度情感预测网络。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217099

Jingjing Zhang, Jiaying Sun, Chunxiao Wang, Zui Tao, Fuxiao Zhang

{"title":"ARMNet: A Network for Image Dimensional Emotion Prediction Based on Affective Region Extraction and Multi-Channel Fusion.","authors":"Jingjing Zhang, Jiaying Sun, Chunxiao Wang, Zui Tao, Fuxiao Zhang","doi":"10.3390/s24217099","DOIUrl":"10.3390/s24217099","url":null,"abstract":"Compared with discrete emotion space, image emotion analysis based on dimensional emotion space can more accurately represent fine-grained emotion. Meanwhile, this high-precision representation of emotion requires dimensional emotion prediction methods to sense and capture emotional information in images as accurately and richly as possible. However, the existing methods mainly focus on emotion recognition by extracting the emotional regions where salient objects are located while ignoring the joint influence of objects and background on emotion. Furthermore, in the existing literature, when fusing multi-level features, no consideration has been given to the varying contributions of features from different levels to emotional analysis, which makes it difficult to distinguish valuable and useless features and cannot improve the utilization of effective features. This paper proposes an image emotion prediction network named ARMNet. In ARMNet, a unified affective region extraction method that integrates eye fixation detection and attention detection is proposed to enhance the combined influence of objects and backgrounds. Additionally, the multi-level features are fused with the consideration of their different contributions through an improved channel attention mechanism. In comparison to the existing methods, experiments conducted on the CGnA10766 dataset demonstrate that the performance of valence and arousal, as measured by Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), has improved by 4.74%, 3.53%, 3.62%, 1.93%, 6.29%, and 7.23%, respectively. Furthermore, the interpretability of the network is enhanced through the visualization of attention weights corresponding to emotional regions within the images.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental and Numerical Studies of the Temperature Field in a Dielectrophoretic Cell Separation Device Subject to Joule Heating. 受焦耳热影响的压电泳细胞分离装置中温度场的实验和数值研究。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217098

Yoshinori Seki, Shigeru Tada

{"title":"Experimental and Numerical Studies of the Temperature Field in a Dielectrophoretic Cell Separation Device Subject to Joule Heating.","authors":"Yoshinori Seki, Shigeru Tada","doi":"10.3390/s24217098","DOIUrl":"10.3390/s24217098","url":null,"abstract":"Technologies for rapid and high-throughput separation of rare cells from large populations of other types of cells have recently attracted much attention in the field of bioengineering. Among the various cell separation technologies proposed in the past, dielectrophoresis has shown particular promise because of its preciseness of manipulation and noninvasiveness to cells. However, one drawback of dielectrophoresis devices is that their application of high voltage generates Joule heat that exposes the cells within the device to high temperatures. To further explore this problem, this study investigated the temperature field in a previously developed cell separation device in detail. The temperature rise at the bottom of the microfluidic channel in the device was measured using a micro-LIF method. Moreover, the thermofluidic behavior of the cell separation device was numerically investigated by adopting a heat generation model that takes the electric-field-dependent heat generation term into account in the energy equation. Under the operating conditions of the previously developed cell separation device, the experimentally obtained temperature rise in the device was approximately 20 °C, and the numerical simulation results generally agreed well. Next, parametric calculations were performed with changes in the flow rate of the cell sample solution and the solution conductivity, and a temperature increase of more than 40 °C was predicted. The results demonstrated that an increase in temperature within the cell separation device may have a significant impact on the physiological functions of the cells, depending on the operating conditions of the device.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation. 基于人工智能的脑磁共振成像应用的边缘计算：实时分类和分割的关键评估

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217091

Khuhed Memon, Norashikin Yahya, Mohd Zuki Yusoff, Rabani Remli, Aida-Widure Mustapha Mohd Mustapha, Hilwati Hashim, Syed Saad Azhar Ali, Shahabuddin Siddiqui

{"title":"Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.","authors":"Khuhed Memon, Norashikin Yahya, Mohd Zuki Yusoff, Rabani Remli, Aida-Widure Mustapha Mohd Mustapha, Hilwati Hashim, Syed Saad Azhar Ali, Shahabuddin Siddiqui","doi":"10.3390/s24217091","DOIUrl":"10.3390/s24217091","url":null,"abstract":"Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost-benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Passive and Active Exoskeleton Solutions: Sensors, Actuators, Applications, and Recent Trends. 被动和主动外骨骼解决方案：传感器、执行器、应用和最新趋势。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217095

D M G Preethichandra, Lasitha Piyathilaka, Jung-Hoon Sul, Umer Izhar, Rohan Samarasinghe, Sanura Dunu Arachchige, Liyanage C de Silva

引用次数: 0

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations. 利用边缘计算和低空平台站优化农业物联网（IoT）。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217094

Deshan Yang, Jingwen Wu, Yixin He

{"title":"Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations.","authors":"Deshan Yang, Jingwen Wu, Yixin He","doi":"10.3390/s24217094","DOIUrl":"https://doi.org/10.3390/s24217094","url":null,"abstract":"Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Factuality by Contrastive Decoding with Factual and Hallucination Prompts. 用事实和幻觉提示进行对比解码，提高事实性。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217097

Bojie Lv, Ao Feng, Chenlong Xie

引用次数: 0

A Review of Visual Estimation Research on Live Pig Weight. 活猪体重目测研究综述。

IF 3.4 3区综合性期刊

Sensors Pub Date : 2024-11-04 DOI: 10.3390/s24217093

Zhaoyang Wang, Qifeng Li, Qinyang Yu, Wentai Qian, Ronghua Gao, Rong Wang, Tonghui Wu, Xuwen Li

引用次数: 0