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EffiNet: An Efficient, Low-complexity Neural Network for Monocular Depth Estimation on Embedded Devices 7th

EffiNet: An Efficient, Low-Complexity Neural Network for Mo...

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Tu, Xiaohan Zhang, Chuanhao Zhuang, Haiyan Sang, Zhen Liu, Mengran Zhengzhou Police University Zhengzhou450053 China Tianjin Public Security Division Beijing Railway Public Security Bureau Tianjin300142 China

ISBN: (纸本)9789819624089

Monocular Depth Estimation (MDE) plays an important role in many fields. However, current MDE requires complex models that are computationally intensive and unsuitable for real-time applications. Some MDE methods also have a high memory access frequency (MAF), leading to substantial data transfer demands. this paper presents EffiNet, a novel network structure designed for MDE. We emphasize high performance and lightweight computing. EffiNet significantly reduces computational complexity while maintaining accurate depth estimation by utilizing MobileNetV2 as the backbone for the encoder. the newly designed decoding algorithm optimizes the model by implementing block processing strategies, efficient upsampling, and point convolution operations. the decoder enhances performance under various scene conditions while keeping computational costs low. this approach also minimizes MAF, thereby reducing data transmission requirements and improving execution speed and energy efficiency. Experimental results demonstrate that EffiNet achieves a reduction in inference time by at least 18.18% on Jetson Nano and 24.39% on Jetson TX2, and a maximum 96.07% reduction in computational complexity (FLOPs), compared to existing methods. Additionally, the accuracy index δ1 shows an improvement of 0.44% to 65.70%. these results validate the superior performance and suitability of resource-constrained mobile and edge computing devices. these advancements position EffiNet as an innovative solution in the field of MDE, capable of delivering high efficiency and reducing resource consumption. © the Author(s) 2025.

关键词： Convolutional neural networks

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Lightweight Garbage Detection Algorithm Based on Improved YOLOv5s 23

Lightweight Garbage Detection Algorithm Based on Improved YO...

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7th international conference on Big Data and Internet of things, BDIOT 2023

作者： Xiao, Zhenqi Yang, Guangxiang Wang, Xu School of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China

ISBN: (纸本)9798400708015

Garbage classification not only helps to avoid resource wastage and environmental pollution but also reduces the cost of waste management, which is of significant importance for environmental protection. In order to promote the development of garbage classification, a high-precision garbage detection model that can be deployed on embedded devices is highly necessary. However, there are several problems with garbage detection models currently deployed on embedded devices, such as large algorithm parameters, large model sizes, and high computational complexity. To address these issues, a lightweight detection algorithm, Distilled_YOLOv5s_ABCS, was proposed by combining YOLOv5s and MobileNetV3. To compensate for the detection accuracy loss caused by the lightweight design, we use the coordinate attention module to replace the squeeze-And-excitation module in MobileNetV3 and add it to the head network. In addition, the integrated module of self-Attention and convolution and spatial pyramid pooling fast module is added to the backbone network to improve feature extraction capabilities. the neck layer uses a weighted bidirectional feature pyramid network to enhance feature fusion. Finally, knowledge distillation is used to improve detection accuracy further. Experimental results show that compared to the original YOLOv5s algorithm, the Distilled_YOLOv5s_ABCS algorithm reduces the algorithm parameter quantity, computational complexity, and model size by 50.8%, 62.4%, and 50%, respectively, while slightly decreasing mean accuracy precision. Additionally, the image processing speed on the CPU increased by 21.3%. the mAP value of the Distilled_YOLOv5s_ABCS algorithm is 89.6%, which meets the requirements for deployment on embedded devices. © 2023 ACM.

关键词： Computational complexity

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University student's mental stress detection using machine learning 7

University student's mental stress detection using machine l...

引用

7th international conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Firoz, Mehedi Islam, Mohammad Monirul Shidujaman, Mohammad Islam, Ashraful Habib, Tarek Department of Computer Science and Engineering Daffodil International University Bangladesh Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Bangladesh

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

University students are especially susceptible to the negative effects of mental stress in today's environment, which is a serious issue overall. A great deal of pressure is now being placed on a period of life that was traditionally considered to be the most carefree. People in today's culture are exposed to increasingly high levels of mental stress, which has been related to a broad variety of health problems, such as depression, suicide, heart attacks, and strokes. Because of this, in order to primarily extract, for the purposes of this research, the mental stress ratings of university students, we applied a total of six distinct machine learning methods. the Decision Tree Classifier, the Random Forest Classifier, the SVC, the KNN Classifier, the Multinomial NB, and the K-Nearest Neighbors Regressor are only some of the machine learning algorithms that are available. this investigation's principal objective is to determine the percentage of students who are struggling to deal with emotional pressure in their lives. the dataset was put together by hand with paper and manual information obtained from a survey. Out of the six distinct classification strategies, the Decision Tree Classifier and the Random Forest Classifier both achieved a test result of 0.99, which is the maximum score that can be achieved. © 2023 SPIE. All rights reserved.

关键词： Machine learning

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Implementation of Russian Short Command Speech Recognition Based on Dynamic Time Warping (DTW) Technology

Implementation of Russian Short Command Speech Recognition B...

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Education, Network and Information Technology (ICENIT), international conference on

作者： Nan Zhang Shuang Zhang Weiwei Wang Yumin Xu School of International Education Heilongjiang University of Technology Jixi China

ISBN: (数字)9798350374919

ISBN: (纸本)9798350374926

For speech recognition tasks with limited training data, the dynamic time warping (DTW) algorithm is employed to recognize Russian speech. Initially, a method combining phonetic conversion and machine translation for speech recognition is investigated using cross-lingual annotated speech corpora. Subsequently, by focusing on the characteristics of Russian speech, dynamic threshold values centered around vowels are employed to achieve precise endpoint detection at the phoneme level, resulting in a 34.4% increase in recognition speed and a 14% improvement in accuracy. Furthermore, parameters reflecting both static features and dynamic changes of speech are extracted through comprehensive time-domain and frequency-domain analysis. Additionally, enhancements to the DTW algorithm, such as the introduction of global constraints and early abandonment strategies, are implemented to mitigate pathological matching and reduce computational complexity, leading to a 19.7% increase in speed and a 4.8% improvement in accuracy. Cross-validation on a five-fold dataset of Russian short command speech achieves a recognition accuracy of 74.9%.

关键词： Training Pathology Accuracy Heuristic algorithms Training data Speech recognition Speech enhancement Feature extraction Machine translation Computational complexity

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Spatial- Temporal- TES: Reanalysis Dataset based Short- Term Temperature Forecasting System 7

Spatial- Temporal- TES: Reanalysis Dataset based Short- Term...

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7th IEEE conference on Energy Internet and Energy System Integration, EI2 2023

作者： Akrami, Neda Li, Yue Dey, Prasanjit Dev, Soumyabrata Shiraz Iran School of Computer Science Technological University Dublin Ireland The Adapt Sfi Research Centre Ireland School of Computer Science University College Dublin Ireland Beijing-Dublin International College Beijing China

ISBN: (纸本)9798350345094

In this paper, we propose a Spatial-Temporal-TES system for short-term forecasting of temperature using reanalysis datasets. We use the ERA5 reanalysis dataset generated by European Center for Medium-Range Weather Forecast (ECMWF) over the 2015-2020 period and take advantage of Triple Exponential Smoothing (TES) algorithm which is one of the most well-known algorithms in time series prediction. the evaluation is done on an hourly value basis. Our experimental results show that the forecasting based on ERA5 exhibited good performance, and ensured a promising and reliable forecasting. Additionally, we compare our proposed approach to two other commonly used forecasting techniques: persistence forecasting and average forecasting. Our results reveal that our proposed method achieves a Root Mean Square Error of 0.57K and 1.01K for a forecasting interval of 12 and 24 hours in the future, respectively. It outperforms the other benchmarking methods. © 2023 IEEE.

关键词： Mean square error

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Evaluation of Age and Sex Effects on Force complexity and Fluctuation during isometric elbow flexion 7

Evaluation of Age and Sex Effects on Force Complexity and Fl...

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7th international conference on Advances in Biomedical Engineering, ICABME 2023

作者： Ghiatt, Kawtar Diab, Ahmad Kinugawa, Kiyoka Jiang, Ning Boudaoud, Sofiane Alliance Sorbonne Université Universite de Technologie de Compiégne Laboratoire de Biomecaniqué et de Bioingénierie Umr Cnrs 7338 Compiegne France Lebanese University Faculty of Public Health Tripoli Lebanon Lebanese International University Bekaa Lebanon Sorbonne Universite Cnrs Umr Biological Adaptation and Aging AP-HP Charles Foix Hospital Functional Exploration Unit for Older Patients Ivry-sur-Seine France Sorbonne Universite Cnrs Umr Biological Adaptation and Aging AP-HP Charles Foix Hospital France National Clinical Research Center for Geriatrics West China Hospital Sichuan University China

ISBN: (纸本)9798350325850

this study attempts to examine the impact of age and gender on fluctuations in force output during voluntary muscle contractions and how this impacts task performance. More precisely, the study investigates how these fluctuations vary during an elbow flexion task at sub-maximal forces. Interestingly, the study found that elderly women exhibit lower steadiness, meaning they exhibit a diminished capacity to maintain a steady force output and lower complexity than young adults when dealing with high levels of contraction. this suggests that women may have a diminished ability to maintain a steady and adaptable force output during the elbow flexion task, which could affect their performance. However, the findings from this study show that the existence of notable differences in force fluctuations among diverse age groups was limited to specific levels of contraction. thus, it can be deduced that various factors, apart from age alone, might contribute to the observed decrease in force fluctuations in this study. Additionally, there are differences in behavior between women and men. © 2023 IEEE.

关键词： Muscle

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Noncontact Vibration and Deformation Detection based on Low complexity CW Radar 7

Noncontact Vibration and Deformation Detection based on Low ...

引用

2022 7th international Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2022

作者： Wang, Chao Bai, Kaixi Li, Wenqi Shi, Jinpeng Chang, Wuliang Zhang, Weixing School of Cyber Science and Engineering Zhengzhou University No. 97 Wenhua Road Zhengzhou City China

A low-cost and high-precision CW radar system is proposed for non-contact detection of the target's tiny vibration. the radar system can obtain the vibration frequency and amplitude of the target by extracting the phase change of the reflected signal caused by the target's vibrations. the proposed radar system uses a combination of the phase shifter and the hardware-based phase comparator, which has the advantages of high detection accuracy and low system complexity. Besides, the combination establishes a linear relationship between the phase change caused by vibration and the output signal of the phase comparator, providing the system with the ability to detect the vibration amplitude. Experimental validations show that the radar system can accurately detect the vibration frequency and amplitude of the loudspeaker and monitor human respiration in real-time at a distance of 2 m. For the amplitude experimental, the absolute error of the vibration amplitude measured by the radar system within 30 cm is less than 0.1 mm. © Published under licence by IOP Publishing Ltd.

关键词： Comparators (optical)

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A Face Morph Detection Method Based on Convolutional Neural Networks and Occlusion Test 7

A Face Morph Detection Method Based on Convolutional Neural ...

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7th international conference on Image, Vision and Computing, ICIVC 2022

作者： Ma, Tian Bamweyana, Arnold Guo, Ming Benon, Kavuma Xi'an University of Science and Technology Department of Computer Science and Technology Xi'an China Kangchiao International School Department of Computer Science Xi'an China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Face morphing is by far the greatest threat to effective automatic boarder control systems. However, there is a lack of consideration and examination of a morphed face image frame as a blend of two images;hence, one image obstructs the other. To address this, we proposed a combination of an occlusion detection method, FSG-FD and a VGG19 neural network architecture to detect these face morphs. First, we designed our algorithms for each of our proposed systems;one for FSG-FD and the other for the VGG19 architecture. then, we blended the two methods into a joined model by pre-training the neural network and increasing its convolutional layers by an additional three layers. the experiments showed that our algorithm achieved higher accuracy than other methods at detecting face morphs. © 2022 IEEE.

关键词： Convolution

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Short-Time Fourier Transform with Optimum Window Type and Length: An Application for Sag, Swell and Transient 7th

Short-Time Fourier Transform with Optimum Window Type and Le...

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7th international conference on Soft Computing in Data Science (SCDS)

作者： Basir, Muhammad Sufyan Safwan Mohamad Affendy, Nur-Adibah Raihan Said, Mohamad Azizan Mohamad Ismail, Rizalafande Che Yusof, Khairul Huda Politekn Mukah Sarawak Dept Elect Engn KM 7-5 Jalan Oya Mukah Sarawak 96400 Malaysia Politekn Mukah Sarawak Dept Commerce KM 7-5 Jalan Oya Mukah Sarawak 96400 Malaysia Univ Malaysia Perlis Fac Elect Engn Technol Arau Perlis Malaysia Management & Sci Univ Fac Informat Sci & Engn U019 B Univ DrPersiaran OlahragaSect 13 Shah Alam 40100 Selangor Malaysia

ISBN: (纸本)9789819904044;9789819904051

the characteristics of power quality signals are non-stationary, where the behaviour confirms the negative consequence in sensitive equipment. Modern cross-term time-frequency distributions (TFDs) are able to characterize the power quality accurately but suffer from a delay in measurement since the power quality signals, in this case, sag, swell and transient, need to be analyzed in real-time. It is shown that one window shift (OWS) properties of linear time-frequency representation (TFR) results from short-time Fourier transform (STFT) satisfies accuracy, complexity and memory. By optimally selecting the window length of 512, the TFR is able to provide optimal time, and frequency localization, as well as the spectral leakage, can be reduced by the Hanning window. the proposed technique can accurately characterize the power quality signals averagely by 95%, as well as the complexity andmemory usage is low. Finally, the paper is concluded by the recommendation of pre-setting for optimum window type and length for real-time power quality measurement.

关键词： Power quality Short-time fourier transform Window length Hanning

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iCar: Multi-modal Learning-Inspired Vehicle Owner Recognition Based on FMCW Radar in Internet of things

iCar: Multi-modal Learning-Inspired Vehicle Owner Recognitio...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： He, Shuaishuai Wang, Yu Du, Xueping Gui, Zhenwen Ren, Xujia Yin, Xiaoyan Northwest University School of Information Science and Technology Xi'an China Northwest University School of Ist Xi'an China Sichuan Agriculture University Business and Tourism School Chengdu China China Electronics Technology Group Corporation The 7th Research Institute Guangzhou China

ISBN: (纸本)9798350345384

the existing human behavior recognition algorithms based on wireless signals are easily affected by the surrounding environment or weather. the built-in FMCW (Frequency Modulated Continuous Wave) radar in the vehicle can collect dynamic biometric features of people, which can be used to verify the identity of the car owner while accomplishing its predetermined tasks. In this paper, we propose a novel multi-modal learning-inspired vehicle owner identification and authentication frame-work, called as iCar, which integrates time-frequency features and micro-Doppler features. In particular, these two features are abstracted from original time-frequency spectrograms and micro-Doppler spectrograms, which are applied to handle frequency changes over time and dynamic biometric features, respectively. Experimental results and performance analysis confirm the superiority of the proposed algorithm. © 2023 IEEE.

关键词： Authentication

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