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IET International Conference on engineering Technologies and Applications (ICETA 2024)

作者： Ching-Ta Lu Yu-Han Weng Yun-En Wu Yi-Ching Wei Department of Communications Engineering Feng Chia University Taichung City Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung City Taiwan

ISBN: (数字)9781837242252

People need to pay more attention to planting and caring for plants. Orchids are an essential potted flower item in the global flower market as they are a highly economical flower production crop. Despite the increasing demand for orchids in the global market, the production of orchids has remained the same, which is attributed to the difficulty in caring for orchids and the high cost of human efforts. Therefore, this study chooses orchids as the target. We realize an orchid care system that can give users precise advice on fertilizer and pesticide application through AI assistance. The automatic light chasing system allows orchids to chase the light when the light is weak and avoid the light when the sun is intense; the humidity detection gives orchids an automatic humidity environment; and the wireless photography and identification allows farms to be identified and managed automatically. The application of this system can also be extended to other species of plants. Accordingly, the proposed system is scalable.

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Mask-CTC-based Encoder Pre-training for Streaming End-to-End Speech Recognition

arXiv

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arXiv 2023年

作者： Zhao, Huaibo Higuchi, Yosuke Kida, Yusuke Ogawa, Tetsuji Kobayashi, Tetsunori Department of Communications and Computer Engineering Waseda University Tokyo Japan LINE Corporation Tokyo Japan

Achieving high accuracy with low latency has always been a challenge in streaming end-to-end automatic speech recognition (ASR) systems. By attending to more future contexts, a streaming ASR model achieves higher accuracy but results in larger latency, which hurts the streaming performance. In the Mask-CTC framework, an encoder network is trained to learn the feature representation that anticipates long-term contexts, which is desirable for streaming ASR. Mask-CTC-based encoder pre-training has been shown beneficial in achieving low latency and high accuracy for triggered attention-based ASR. However, the effectiveness of this method has not been demonstrated for various model architectures, nor has it been verified that the encoder has the expected look-ahead capability to reduce latency. This study, therefore, examines the effectiveness of Mask-CTC-based pre-training for models with different architectures, such as Transformer-Transducer and contextual block streaming ASR. We also discuss the effect of the proposed pre-training method on obtaining accurate output spike timings, which contributes to the latency reduction in streaming ASR. © 2023, CC BY.

关键词： Speech recognition

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Sparse Channel Feedback for Energy-Efficient mmWave Massive MIMO Systems

Sparse Channel Feedback for Energy-Efficient mmWave Massive ...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Seungnyun Kim Jiao Wu Jihoon Moon Byonghyo Shim Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Korea

mmWave massive multi-input multi-output (MIMO) systems with large-scale antenna array have potential to increase the spectral efficiency by orders of magnitude. One well-known drawback of this systems is that the power consumption of base station (BS) is considerable. To improve the energy efficiency and throughput, it is crucial that the BS acquires accurate downlink channel state information (CSI). However, this task is difficult since the CSI feedback overhead scales linearly with the number of antennas. In this paper, we propose an approach to maximize the energy efficiency of mmWave massive MIMO systems using a sparse channel feedback. Key idea of the proposed scheme is to choose a small number paths among the whole propagation paths in the angular domain channel and then exploit the channel information of chosen paths in the data precoding. From the performance analysis and the numerical results, we demonstrate that the proposed scheme achieves considerable energy efficiency improvement and feedback overhead reduction over the conventional CSI feedback-based schemes.

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SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

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IEEE communications Society Conference on Sensor and Ad Hoc communications and Networks (SECON)

作者： Yinnan Zhou Hao Zhou Luowei Li Siming Chen Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn't be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions.

关键词： Location awareness Performance evaluation Accuracy Line-of-sight propagation Switches Mobile handsets Sensors Indoor environment Wireless fidelity Antennas

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Mask-CTC-Based Encoder Pre-Training for Streaming End-to-End Speech Recognition

Mask-CTC-Based Encoder Pre-Training for Streaming End-to-End...

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European Signal Processing Conference (EUSIPCO)

作者： Huaibo Zhao Yosuke Higuchi Yusuke Kida Tetsuji Ogawa Tetsunori Kobayashi Department of Communications and Computer Engineering Waseda University Tokyo Japan LINE Corporation Tokyo Japan

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Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein

Multimodal biometric authentication based on deep fusion of ...

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作者： El-Rahiem, Basma Abd El-Samie, Fathi E. Abd Amin, Mohamed Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

Biometric identification depends on the statistical analysis of the unique physical and behavioral characteristics of individuals. However, a unimodal biometric system is susceptible to different attacks such as spoof attacks. To overcome these limitations, we propose a multimodal biometric authentication system based on deep fusion of electrocardiogram (ECG) and finger vein. The proposed system has three main components, which are biometric pre-processing, deep feature extraction, and authentication. During the pre-processing, normalization and filtering techniques are adapted for each biometric. In the feature extraction process, the features are extracted using a proposed deep Convolutional Neural Network (CNN) model. Then, the authentication process is performed on the extracted features using five well-known machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN). In addition, to represent the deep features in a low-dimensional feature space and speed up the authentication task, we adopt Multi-Canonical Correlation Analysis (MCCA). We combine the two biometric systems based on ECG and finger vein into a single multimodal biometric system using feature and score fusion. The performance of the proposed system is tested on two finger vein (TW finger vein and VeinPolyU finger vein) databases and two ECG (MWM-HIT and ECG-ID) databases. Experimental results reveal improvement in terms of authentication performance with Equal Error Rates (EERs) of 0.12% and 1.40% using feature fusion and score fusion, respectively. Furthermore, the authentication with the proposed multimodal system using MCCA feature fusion with a KNN classifier shows an increase of accuracy by an average of 10% compared with those of other machine learning algorithms. Therefore, the proposed biometric system is effective in performing secure authentication and assisting the stakeholders in making

关键词： Authentication

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Design and Development of a Semi-Automated Kitchen Waste Composter

Design and Development of a Semi-Automated Kitchen Waste Com...

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International Conference on computer and Communication engineering, ICCCE

作者： Moshe Coleen N. Adique Aaron Jimson L. Mandap Maria Leonora C. Guico Jan Kevin A. Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Waste generation is a significant challenge exacerbated by factors such as population growth, industrialization, and urbanization, particularly in densely populated areas like Metro Manila. Consequently, effective waste management becomes increasingly crucial. This study focused on the development of a waste management device designed to address waste generation at its source, specifically targeting the residential sector. The researchers have created a semi-automated kitchen waste composter to facilitate the conversion of kitchen waste into valuable fertilizers, even for individuals with limited composting knowledge. The system incorporates a sensor network that monitors key parameters of the composting process, including temperature and moisture levels. An actuator network ensures that these parameters remain within optimal ranges. Additionally, an image processing algorithm has been implemented to detect compost maturity. By implementing this waste management device, households in urban areas can actively contribute to waste reduction efforts. It empowers individuals to participate in composting without requiring extensive expertise. This technology represents a promising solution to mitigate waste generation and promote sustainable practices in residential settings, ultimately contributing to a cleaner and more environmentally friendly urban environment.

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Sustainability Analysis of Residential Photovoltaic Systems: A Life Cycle Assessment Approach

Sustainability Analysis of Residential Photovoltaic Systems:...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Lorenzo Gabriel C. Caballa King Harold A. Recto Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Renewable energy has emerged as a prominent topic in dialogues about energy production sustainability in the Philippines. This is mostly attributed to the excessive dependence of the country on imported petroleum and fossil fuels, which are well recognized for their detrimental effects on the environment. Solar energy is often acknowledged as a very accessible kind of renewable energy, especially in tropical nations like the Philippines, owing to the country's abundant sun irradiation levels throughout the year. Despite the significant progress made in solar technology and its effective implementation in residential environments, some limits continue because of constraints associated with photovoltaic technology, economic feasibility, and environmental implications. The use of the life-cycle assessment (LCA) methodology offers a structured framework for the examination and evaluation of the sustainability aspects pertaining to residential PV systems. This research does this by investigating PV systems throughout their lifecycle, starting with the selection of materials, manufacturing process, operation, and maintenance, and concluding with the environmental impacts at the end-of-life phase. Subsequently, this paper will also discuss the policy implications arising from the study results. The research aims to provide valuable insights into PV residential systems in the Philippines and aspires to contribute to policy-relevant literature in this field.

关键词： Life cycle assessment Photovoltaic systems Renewable energy sources Solar energy Production Recycling Sustainable development

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IoT Intelligence Empowered by End-Edge-Cloud Orchestration

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China communications 2022年第7期19卷 152-156页

作者： Yaoxue Zhang Feng Lyu Peng Yang Wen Wu Jie Gao Department of Computer Science and Technology at Tsinghua University China School of Computer Science and Engineering Central South UniversityChangshaChina School of Electronic Information and Communications HUST Frontier Research Center Peng Cheng LaboratoryShenzhenChina 渥太华卡尔顿大学信息技术学院

To support intelligent Internet of Things(IoT)applications,such as autonomous driving,smart city surveillance,and virtual reality(VR)/augmented reality(AR),cloud services are expected to be pushed to the proximity of IoT devices for quality *** instance,to facilitate safe autonomous driving,the service delay of most vehicular applications is required to be within milliseconds,and any information delay may result in dangerous on-road conditions.

关键词： IoT delay driving

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Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression

Wet and Dry Cough Classification System Using Support Vector...

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International Conference on computer and Communication engineering, ICCCE

作者： P.Co Sean Andrei Claudine Anne J. Madamba Maria Leonora C. Guico Jan Kevin A. Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to extract and analyze the Energy Envelope Peaks, Crest Factors, Zero-Crossings, and Formant Frequencies 1-4 from a dataset of 870 cough samples. The analysis of 347 wet cough sound samples and 523 dry cough samples reveals distinctive characteristics. Wet coughs exhibit a higher number of peaks and zero-crossings, while dry coughs display a slightly higher crest factor on average. Moreover, the F1 and F2 formant frequencies are higher in wet coughs, whereas the F3 and F4 formant frequencies are higher in dry coughs. To classify the cough types, both Support Vector Machine (SVM) and Logistic Regression Method (LRM) classifiers are trained using the identified features. The SVM classifier achieves an average accuracy of 71.26%, sensitivity of 72.73%, specificity of 70.87%, and F1-score of 67.94% during testing. Similarly, the LRM classifier achieves an accuracy of 71.26%, sensitivity of 70.59%, specificity of 71.55%, and F1-score of 68.45%. Such automated classification systems have the potential to aid in the early detection and monitoring of respiratory diseases in enclosed spaces.

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