检索结果-内蒙古大学图书馆

International Conference on Innovations in Science, engineering and Technology (ICISET)

作者： Md. Humayun Kabir Md Saiful Islam Md Jiabul Hoque Dept. of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Dept. of Computer and Communication Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Traffic congestion in smart cities is a growing concern. This paper explores the application of Fuzzy Logic in Intelligent Transportation systems (ITS) to address this challenge. Fuzzy logic can handle imprecise and dynamic traffic data and offers advantages over traditional fixed-time signal control. The research proposes a Fuzzy-based ITS framework for dynamically traffic signal control. This framework utilizes real-time sensor traffic data to adjust signal timings, dynamically prioritizing congested areas. The system also incorporates emergency vehicle detection using siren sensors. This ensures priority for emergency vehicles by granting them the right of way. This paper discusses the design of the fuzzy inference system, including the selection of fuzzy variables and membership functions. The effectiveness of the proposed system is evaluated through simulations, demonstrating its potential to reduce traffic congestion and improve traffic flow in smart cities.

关键词： Fuzzy logic Transpiration Technological innovation Uncertainty Smart cities Computational modeling Vehicle detection Transportation Vehicle dynamics Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Gamification of RF Data Acquisition for Classification of Natural Human Gestures

Gamification of RF Data Acquisition for Classification of Na...

引用

IEEE National Conference on Radar

作者： Emre Kurtoğlu Kenneth DeHaan Caroline Kobek Pezzarossi Darrin J. Griffin Chris Crawford Sevgi Z. Gurbuz Dept. of Electrical and Computer Engineering The University of Alabama Dept. of American Sign Language Gallaudet University Dept. of Psychology Gallaudet University Dept. of Communication Studies The University of Alabama Dept. of Computer Science The University of Alabama

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

In recent years, there have been significant developments in radio frequency (RF) sensor technology used in human-computer interaction (HCI) applications, specifically in areas like gesture recognition and more broadly, human activity recognition. Although extensive research has been conducted on these subjects, most experiments involve controlled settings where participants are instructed on how to perform specific movements. However, when such experiments are conducted on sign language recognition they lack capturing dialectal and background-related diversities. In this work, we explore the differences in RF datasets acquired under controlled experimental settings and in free form environments where users were not constrained by the experimental instructions and limitations. We show that directed (i.e., controlled) data acquisition approaches result in over-optimistic performances which do not perform well on naturally acquired data samples in a real-world use case. We evaluate different approaches on generating synthetic samples from directed dataset, but show that such methods do not offer much benefit over collecting natural data. Therefore, we propose an interactive data acquisition paradigm through gamification. We show that the proposed approach enables the recognition of American Sign Language (ASL) in real-world settings by achieving 69% accuracy on 29 words.

关键词： Radio frequency Human computer interaction Sign language Data acquisition Transforms Radar Predictive models

来源：评论

学校读者我要写书评

暂无评论

Machine Vision Approach Using Multi Features for Detection of Oil Palm Stem Disease 1

Machine Vision Approach Using Multi Features for Detection o...

引用

1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Septiarini, Anindita Hamdani, Hamdani Syaifudin, Encik Akhmad Setyaningsih, Emy Nurcahyono, Damar Hadiwijaya, Noor Alam Mulawarman University Dept. of Informatics Samarinda Indonesia Mulawarman University Faculty of Agriculture Samarinda Indonesia Institut Sains Teknologi Akprind Department of Computer System Engineering Yogyakarta Indonesia Politeknik Negeri Samarinda Dept. of Information Technology Samarinda Indonesia

ISBN: (纸本)9798350306484

Indonesia is a significant palm oil producer, widely cultivated due to its status as a prominent vegetable oil-producing plant. Palm oil is a highly sought-after crop in agriculture due to its profitability and the demand for substantial quantities of high-quality oil. Early diagnosis of oil palm diseases is crucial for prompt prevention and eradication measures essential for maintaining high-quality palm oil production. Hence, there is a requirement for a machine vision-based method to classify diseases on plant stems. The proposed method consists of several main processes: pre-processing, feature extraction, and classification. Several machine learning algorithms and feature extraction using color and texture features were performed to develop a machine vision for detecting oil palm stem disease. It applied cross-validation with a K-fold value of 10. Performance evaluation was carried out using three parameters: precision, recall, and accuracy. The Linear Discriminant Analysis (LDA) method achieves the optimum results in testing scenarios combining color features based on HSV color space and textures features, achieving an accuracy value of 90.50%. © 2023 IEEE.

关键词： Palm oil

来源：评论

学校读者我要写书评

暂无评论

Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI Applications

引用

Remote Sensing in Earth systems Sciences 2025年第2期8卷 435-443页

作者： Kumar, M. Prema Krishnammal, N. Gupta, Manu Begum, Mairaj Unisa Sultana, Sameera Degala, Divya Priya Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of CSE-AIML MALLAREDDY UNIVERSITY Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Globally, chronic food insecurity persists and is made worse by shocks brought on by climate change, like floods and droughts. In order to guarantee prompt assistance delivery, humanitarian programming prioritizes predicting levels of food insecurity and identifying homes that are at risk. Using complicated and diverse data, machine learning (ML) models have been widely used to promote food security. This research proposes a novel technique in sustainable agriculture-based food security analysis and satellite data cyberattack detection using machine learning models. Here, the input is collected based on satellite data, and its cyber detection has been analyzed using a convolutional spatio adversarial encoder neural network. Then, from this satellite data, the food security analysis data has been collected and processed for noise removal and normalization. Processed data has been classified using the fuzzy Q-Boltzmann ResNet transfer neural network. Experimental analysis is carried out for the food security analysis dataset in terms of data integrity, detection accuracy, average precision, F-1 score, and recall. The proposed approach yielded 97% detection accuracy, data integrity of 98%, 96% average precision, 93% recall, and 94% F-1 score. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cyberattack detection Food security analysis Machine learning models Satellite data Sustainable agriculture

来源：评论

学校读者我要写书评

暂无评论

Implementation of parallel AES Processing with CAM-based Massive-parallel SIMD Matrix Core 5

Implementation of parallel AES Processing with CAM-based Mas...

引用

5th World Symposium on communication engineering, WSCE 2022

作者： Kageyama, Kyosuke Arai, Sota Hamano, Hajime Kong, Xiangbo Koide, Tetsushi Kumaki, Takeshi Electronic and Communication Engineering Kindai Univ. Dept. of Electrical Osaka Japan Ritsumeikan Univ. Dept. of Electronic and Computer Engineering Shiga Japan Hiroshima Univ. Hiroshima Japan

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

Recently, several multimedia applications, such as digital image compression, digital video compression, and digital audio processing, are executed on the mobile devices. The processing core in the mobile device requires high performance, versatility, and programmability. Generally, multimedia applications consist of repeated arithmetic operation and table-lookup coding operation. To have high performance, versatility, and programmability by improving the processing speed of the both operations on a processing core, Content Addressable Memory-based massive-parallel SIMD matrix core (CAMX) is proposed. The role of CAMX is an accelerator for mobile CPU core. CAMX has highly parallel processing capability and is configured by two CAM modules which are used in fast table-lookup coding operation. This paper has shown the result of AES encryption. The number of AES encryption total clock cycles are 1,362,699, and also the detail number of clock cycles is confirmed: SubBytes is 1,312,160, ShiftRows and MixColumns is 17,161 in total, AddRoundKey is 2,519. Furthermore, CAMX can process AES encryption in 83.17 clock cycles / bytes, and it is confirmed to be about 50 % performance improvement than existing mobile processor. © 2022 IEEE.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification

Time-Stretched Imaging Flow Cytometry and Photonic Neuromorp...

引用

2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Tsirigotis, Aris Deligiannidis, Stavros Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. 83200 Karlovasi Samos Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

ISBN: (纸本)9798350345995

In this work, we investigate the compression capabilities of a photonic neuromorphic accelerator relying on an optical spectrum slicing technique [1], following a high-flow 1D imaging cytometry setup, able to image 62000 particles/sec. The utilized image scheme relies on a technique, where spatial information is transfered to the spectrum and subsequently through dispersion to the temporal domain. Therefore, it can replace high speed spectrometers with a single photodetector [2]. On the other hand, the elevated image acquisition rate, trigger the generation of a staggering amount of data, which can cause a processing backlog at the digital backend. In this context, we explore, for the first time, the use of a photonic neuromorphic pre-processing unit, following the cytometer that can reduce the number of trainable parameters at a lightweight digital backend by a factor of 2.5, while at the same time, it can project incoming data to a high dimensional space boosting accuracy. © 2023 IEEE.

关键词： Photonics

来源：评论

学校读者我要写书评

暂无评论

Support Vector Regression Based Traffic Prediction Machine Learning Model*

Support Vector Regression Based Traffic Prediction Machine L...

引用

International Conference on Computation system and Information Technology for Sustainable Solutions (CSITSS)

作者： Ansh Srivastava Mrigaannkaa Singh Somesh Nandi Dept. of Computer Science and Engineering RV College of Engineering Bengaluru India Dept. of Electronics and Communication RV College of Engineering Bengaluru India Dept. of AI/ML RV College of Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues.

关键词： Support vector machines Training Computational modeling Urban areas Machine learning Predictive models Vectors Vehicle dynamics Optimization Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

引用

2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Deligiannidis, Stavros Tsirigotis, Aris Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach. © 2023 IEEE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle communication

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle...

引用

2023 IEEE International Conference on communications, Information, Electronic and Energy systems, CIEES 2023

作者： Sharma, Preeti Singh, Prabhakar Tiwari, Rakesh N. Kumar, Pradeep Galgotias University Physics Division School of Basic Sciences Greater Noida India Greater Noida Institute of Technology Dept. of Applied Science Greater Noida India Madanapalle Institute of Technology & Science Dept. of Electronics and Communication Engineering Madanapalle India University of KwaZulu-Natal Dept. of Electrical Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350336917

This research paper reports a very thin 2-port multiple-input multiple-output (MIMO) antenna with a small footprint of 14× 10× 0.58mm3. The proposed design works between the frequencies of 8.73 to 15.86 GHz. A modified hexagonal patch is used along with the defected ground plane to obtain wide bandwidth. A conventional decoupling strip technique is utilized to improve the isolation between the ports and it is found >19dB across the whole operating band. In addition to that, 'H'-shaped slot has been incorporated into the grounding plane to enhance impedance matching. The realized gain and radiation patterns of the designed antenna are calculated and found quite acceptable. Furthermore, the diversity parameters of the antenna are computed with envelope correlation coefficient (ECC) 9.97dB across the entire operating frequency range. Finally, the results of MIMO antenna are compared with the calculated data for the purpose of practical validation. © 2023 IEEE.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Exploring the EmoBone Dataset with Bi-Directional LSTM and Attention for Emotion Recognition via Bone Conducted Speech

Exploring the EmoBone Dataset with Bi-Directional LSTM and A...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Md. Sarwar Hosain Yosuke Sugiura Mousumi Haque M. Shahidur Rahman Tetsuya Shimamura Dept. of Information and Computer Sciences Saitama University Saitama Japan Dept. of Information and Communication Engineering University of Rajshahi Rajshahi Bangladesh Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research investigation explores the use of deep learning for emotion recognition using bone-conducted (BC) speech. It focuses on the EmoBone dataset, which is the first dataset specially created for this purpose. Bone-conducted speech offers several advantages, such as improved speech quality in noisy environments, reduced interference from ambient sounds, and enhanced user privacy, as the sound is directly transmitted to the auditory system through bone conduction. Although deep learning is commonly employed for voice emotion recognition, these methods frequently encounter challenges, including the degradation problem and information loss in the deeper stages of deep neural networks. To resolve these issues, we propose implementing a bi-directional long short-term memory (BiLSTM) network that utilizes BC speech and incorporates attention mechanisms. This network will effectively utilize information and mitigate the adverse impacts caused by degradation. However, the attention mechanism significantly improved the performance of the model, resulting in an accuracy gain of approximately 91.45%. The confusion matrices demonstrate that the attention mechanism enhances recognition accuracy for all emotional categories. This study exhibits the capability of enhancing emotion recognition in BC speech applications by integrating BiLSTM with attention.

关键词： Degradation Deep learning Emotion recognition Attention mechanisms Accuracy Transfer learning Bidirectional long short term memory Speech recognition Speech enhancement Bones

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：