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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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Batik Industrial Waste Monitoring and Control System Based on the Internet of Things 5

Batik Industrial Waste Monitoring and Control System Based o...

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5th International Conference on electrical, Electronics, Informatics, and Vocational Education, ICEELINVO 2022

作者： Ali, M. Firdaus, M.F. Electrical Engineering Education Department Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Electrical Study Program Vocational School Universitas Negeri Yogyakarta Indonesia

The aims of this research are to (1) Design and build a Batik Industrial Waste Monitoring and Control System Based on the Internet of Things (BIWMCS -IoT);(2) Test the performance of the BIWMCS-IoT system. The design and implementation of the BIWMCS-IoT system consist of five stages, namely: (1) Needs analysis, (2) Design, consisting of electronic planning, hardware, and software;(3) Development, including hardware and software development;(4) Testing, and (5) implementation. The BIWMCS-IoT system consists of a sensor unit to measure the parameters of water turbidity, temperature, Power of Hydrogen (pH), and the flow velocity of the wastewater then obtained by the microcontroller and the results are sent to the Wi-Fi module to adjust the solenoid valve opening and closing. Based on the testing of the system, has been shown that the BIWMCS-IoT system can work well according to the standards in the Minister of Indonesian Environment Regulation Number 05 of 2014. The reading accuracy of the batik waste monitoring system reaches 96.6% with the delivery time to the smartphone less than 30 seconds. This shows that this system can work well to monitor and control the liquid waste produced by the batik industry. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Impact of High-k Materials and Tube Numbers on CNFET Gate's Performances 5

Impact of High-k Materials and Tube Numbers on CNFET Gate's ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Yalung, Christofer Tantraviwat, Doldet Uatrongjit, Sermsak Yamwong, Wittawat Doctor of Philosophy Program in Nanoscience and Nanotechnology International Program/Interdisciplinary Center of Excellence in Materials Science and Technology Faculty of Science Chiang Mai University Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand Chachoengsao Thailand

ISBN: (纸本)9781665474672

Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and contact materials. In this study, the dielectric constant and the number of carbon nanotubes (CNTs) were emphasized to analyze the propagation delay and average power dissipation of basic logic gates. Zirconium dioxide (ZrO2), hafnium oxide (HfO2), and silicon dioxide (SiO2) were used as dielectric materials. In this experiment, the Stanford CNFET model was utilized, and variable factors such as the number of CNTs and the dielectric constant (Kox) accumulated on the nanotube gate's surface were diversified. The correlation coefficients of the delay, the average power, and the CNT numbers were also presented. Furthermore, the multi-objective genetic algorithm and the Rmethod were utilized to find the optimal Kox and the number of CNTs for minimizing the average dissipation power and the propagation delay of some logic gates designed with CNFET. For all the gates considered, the optimal number of CNTs is 4 while the dielectric constant is within the ranges of 19 to 24. © 2023 IEEE.

关键词： Carbon nanotubes

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study 3

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023

作者： Muharram, Arief Purnama Taufikulhakim, Farhan Hilmi Purwarianti, Ayu Universitas Indonesia Faculty of Medicine Jakarta Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Master Program of Informatics Bandung Indonesia

ISBN: (纸本)9798350302424

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development. © 2023 IEEE.

关键词： COVID-19

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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Deep Convolutional Neural Networks for Accurate Solar Module Classification in Thermal Images

Deep Convolutional Neural Networks for Accurate Solar Module...

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International electrical engineering Congress (iEECON)

作者： Chutinun Phungket Prasit Nangthin The Graduate Program Faculty of Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The rapid and continuous technological advancements in computer and internet technologies, combined with data management techniques, find applications in various domains. For example, they aid in predictive maintenance systems, evaluate the performance of solar modules, and differentiate efficiently performing solar modules from less efficient ones using thermal imagery. The accuracy of solar module classification significantly impacts the subsequent assessment. In this study, we employ advanced techniques, particularly deep learning through Convolutional Neural Networks (CNN), to classify thermal images of solar modules. The practical aspects of applying CNN to the dataset of thermal images are explored, including transfer learning from pretrained CNN architectures and custom CNN architecture training. Experiments are conducted with three prominent types of solar module conditions: normal, cracked, and dusty. The results reveal that the most effective approach involves learning and fine-tuning pre-trained architectures on detailed studies, achieving a classification accuracy of up to 94.5 % .

关键词： Training Photovoltaic cells Transfer learning Neural networks Computer architecture Thermal conductivity Thermal analysis

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Design of Radio over Fiber System with 16-QAM Modulation for 5G Fronthaul Network Implementation in Indonesia

Design of Radio over Fiber System with 16-QAM Modulation for...

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2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2022

作者： Effendi, Nabila Syadzwina Natali, Yus Apriono, Catur Universitas Indonesia Faculty of Engineering Department of Electrical Engineering Depok Indonesia Telecommunication Engineering Program Akademi Telkom Sandhy Putra Jakarta Jakarta Indonesia

ISBN: (数字)9781665451260

ISBN: (纸本)9781665451260

In 5G Network, Cloud Radio Access Network (C-RAN) plays a substantial role in escalating network performance efficiency. Nevertheless, this C-RAN concept's main challenge lies in the need for a fronthaul network to handle high capacity and low delay. The Radio over Fiber (RoF) has been a solution to satisfy the high capacity and high-speed transmission required by the 5G fronthaul network. Keeping the attenuation effect low to achieve the minimum BER by using the optical amplifier is necessary. This paper investigates RoF by considering amplifier placement and different bitrate with 16-QAM modulation for Indonesia's 5G Fronthaul Network Implementation. Optical amplifier placement scenarios are pre-amplifier and booster amplifier. The results show that the booster amplifier scheme can cover a maximum fronthaul transmission distance of 20 km. As a comparison, the pre-amplifier scheme can reach a transmission distance of up to 15 km. Moreover, increasing the bitrate from 1 Gbps to 2.5 Gbps causes the BER value to increase. This result shows that different optical amplifiers and the increase in bit rate will affect the obtained BER values and limit the transmission distance that the fronthaul network can achieve. © 2022 IEEE.

关键词： Radio-over-fiber

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Development of Color Detection Based on Camera Infrared Filter with Python Image Processing 22

Development of Color Detection Based on Camera Infrared Filt...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Leonardy, Joshu Aflah, Nawal Zuhdaniel Salim, Mohd Rashidi Bin Ibrahim, Mohd Haniff Bin Manap, Hadi Bin Tresna, Wildan Panji Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Bahru Malaysia The Republic of Indonesia Defense University Military Physics Study Program Bogor Indonesia University Malaysia Pahang Al Sultan Abdullah Faculty of Electrical & Electronic Engineering Technology Pekan Malaysia National Research and Inovation Agency Indonesia Research Center for Photonics South Tangerang Indonesia

ISBN: (纸本)9798331510077

This research explores the use of a modified infrared (IR) camera equipped with a high-pass filter to distinguish between natural objects (vegetation) and artificial objects (synthetic military uniforms) in an environment where vegetation makes objects difficult to detect. In military operations, especially guerrilla warfare in tropical forests, detecting enemies camouflaged in vegetation makes it difficult to distinguish targeted objects. Visible light cameras usually struggle to distinguish between synthetic and natural colors. This methodology involves the use of Python programming and various libraries, such as Computer Vision (CV), to process images captured by modified cameras. A graphical user interface (GUI) was developed to analyze color values, focusing on the red, green, and blue spectra. The results show that the modified IR camera can more effectively separate synthetic and natural object color peaks compared to a standard visible light camera, offering potential applications in military reconnaissance and sniper operations. This research demonstrates how image processing can improve object detection in complex environments. © 2024 IEEE.

关键词： High pass filters

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Speech Recognition for Intelligent System in Service Robots: A Review

Speech Recognition for Intelligent System in Service Robots:...

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International Conference on electrical engineering and Computer Science (ICECOS)

作者： Ratna Atika Suci Dwijayanti Bhakti Yudho Suprapto Department of Electrical Engineering Polytechnic of Sriwijaya Doctoral Program in Engineering Science Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Faculty of Engineering Palembang Universitas Sriwijaya Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Speech recognition and response system technology in service robot research continues to evolve alongside technical advances and the increasing demand for intelligent automation across industries. The development of machine learning, digital signal processing, and artificial neural networks has brought significant advancements to speech recognition systems. While speech recognition has been implemented in various systems, there is limited discussion on categorizing these systems by examining four aspects: speech recognition applications, datasets used, emotional speech recognition, and automatic speech recognition. In this study, a review of these four aspects is conducted, focusing on the algorithms and methods used, along with their advantages and disadvantages, particularly in the context of implementing them in service robots using the Indonesian language. This study involves a literature review of 100 research papers from the last five years. The review concludes that the use of the transformer method combined with other techniques and Indonesian language datasets is still very limited. Therefore, future research will focus on developing and implementing these methods in humanoid service robots.

关键词： Intelligent automation Industries Service robots Reviews Signal processing algorithms Humanoid robots Hidden Markov models Speech recognition Machine learning Transformers

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Optimal Resource Allocation for NOMA Wireless Networks

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Computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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