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Automated Road Monitoring System Using CCTV with machine learning 3

Automated Road Monitoring System Using CCTV with Machine Lea...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Selvi, G. Thamarai Deepak, N. Jaganraj, A. Shyamalavannan, M. Dinesh, J. Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665462754

Automated Road Monitoring System (ARMS) is an integrated system for reducing road accidents through surveillance. The main focus of this is on the signal mechanism on lull roads and the second one is the surveillance and alerting system that is helpful for the people in hilly regions and travelers. In developing countries accidents are the cause for death for the majority reasons. 'Speed Kills', but still people are lacking to drive safely on the road. Nowadays these people are the main reason for this road traffic accidents that are responsible for death as well as these also contribute a huge loss in the country's economy. About 23 million people die in India per year according to the Million Death Study (MDS). Out of these 137000 are because of road traffic accidents. That is about 377 people per day. We can see that many of them are curved roads. Thousands of people lose their lives each year because of this situation. As far as concerned, mountain road accidents are not considered to a level and these mountain accidents must be looked upon seriously. One of the solutions for this problem is alerting the driver about the vehicles coming from opposite sides in hair pin road sections. In India there are around 13.6% of accidents due to hilly curves. This system is one of the solutions for reducing these kinds of accidents by installing Automated Road surveillance systems in accident-prone regions. This integrated system also has an accident detection system that uses machine learning and image processing techniques for surveillance. © 2022 IEEE.

关键词： Roads and streets

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Prosody Prediction with Discriminative Representation Method 3

Prosody Prediction with Discriminative Representation Method

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Zhang, Jipeng Yilahun, Hankiz Feng, Xiaoqin Chen, Yunlin Yang, Xipeng Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing China Xinjiang Key Laboratory of Multilingual Information Technology China Mobvoi Ai Lab Suzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Rhythm affects the naturalness and intelligibility of Text-To-Speech (TTS). However, rhythm prediction remains a great challenge, usually in two aspects: 1) the united annotation is a relatively difficult task, which depends on expert's experience. 2) traditional methods based on conditional random field (CRF), which heavily rely on feature engineering, such as word segmentation, part of speech(pos) etc. For above problems, we propose a method to reduce the dependency for united annotation data and conduct the joint experiment which use one unified model on independent data. Meanwhile, we also propose an algorithm of Layer Look Up Table (LLUT): use an embedding layer to learn a discriminative representation for different level of prosody data without any feature engineering. By using this method, the classifier can share the parameters and predict for different prosody level separately, which reduces the number of trainable model parameters. In order to better represent the input text, we use the pre-training model, like BERT, to provide the semantic information. Our experiment shows that the method of LLUT, is better able to acquire the discriminative meaning of different prosody levels. And also, our algorithm is proved to be general for sequence annotation tasks thus we can do extra task, like polyphone-prosody prediction. © 2022 IEEE.

关键词： Forecasting

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Slope recognition based on human body surface EMG signal Using CNN 3

Slope recognition based on human body surface EMG signal Usi...

引用

3rd international conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Ren, Weizhi Liu, Yali Song, Qiuzhi Deng, Hongbin The School of Mechatronical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665479974

In recent years, the development of intelligent exoskeleton robot technology has made considerable applications in the military and civilian fields. Accurate recognition of human motion patterns and compliant switching of control systems are technical difficulties to be solved in the field of intelligent exoskeleton. Convolutional neural networks (CNN) have achieved good applications in the fields of computer vision and speech recognition. Practice has proved that slope detection is an important part of human motion pattern recognition. However, few people are engaged in related research. In this paper, in view of the fact that the surface EMG signal is generated before the action and is similar to the audio signal, we introduce a slope-recognition method based on the raw surface EMG signal using CNN. Without using the other feature extraction and signal processing methods, we use short-time Fourier transform (STFT) to process the original EMG signal to generate a spectrogram as CNN input. As a result, compared with traditional machine learning algorithms, our method has a higher accuracy of 99.94%, which is vital for exoskeleton robots that directly interact with the human body due to safety and comfort. © 2022 IEEE.

关键词： Motion estimation

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EEG mental arithmetic task levels classification using machine learning and deep learning algorithms

EEG mental arithmetic task levels classification using machi...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Krashana Nirde Manasa Gunda Ramchandra Manthalkar Suhas Gajre Electronics and Telecommunication Engg.SGGSIE&T SRTMU Vishnupuri Nanded Nanded India

EEG signals are often-used modalities for monitoring mental arithmetic performance. In order to learn robust characteristics and predict the level of classification from EEG, we provide a deep neural networks and machine learning architecture. In this research, we considered online public dataset related to mental arithmetic task. EEG dataset recording are stored last 3 minutes of resting period (180 sec before task) and first 1 minute of the mental arithmetic task period (60 sec-while doing task). The task involves series of subtraction performed by 36 participants. The participants were divided among Good (1) and Bad (0) based on task level. We used machine learning and deep learning methods to classify three different task levels. (i.e., no task, normal task, and difficult task). In algorithm 1 we extracted five features like skewness, kurtosis, mean, standard deviation and variance from the EEG signals and applied as input to the classifiers. Here three machine learning classifiers used for three task levels classification like SVM, KNN and Decision Tree. KNN obtained high classification with 91% whereas SVM obtained S9% and Decision Tree obtained 65%. Algorithm 2 uses ANN and LSTM, two deep learning algorithms, to learn directly from raw EEG data. For better performance we used optimization techniques. ANN used rmsprop and obtained the classification accuracy of 96.80% whereas LSTM uses Adam and SGD and obtained the classification accuracy of 94%. Therefore, when raw data applied to deep learning algorithms can perform well when grouping of handmade features given to a machine learning algorithm.

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Designing Multimodal Cognitive Model of Emotion Recognition Using Voice and EEG signal 3rd

Designing Multimodal Cognitive Model of Emotion Recognition ...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Jaswal, Ram Avtar Dhingra, Sunil Kumar, Javalkar Dinesh Department of Instrumentation Kurukshetra University Haryana India Department of ECE Lingaya’s Vidyapeeth Haryana India

ISBN: (纸本)9789811627606

This paper is about identifying emotions as a multimodal entity by implementation of machine learning techniques for acquiring as well as analysing EEG and speech signal. The fusion of EEG and speech-based emotion recognition method can be used to detect and recognize different states of emotions more accurately. This fusion of brain signal and voice signal model can be designed and implemented in brain–computer interface (BCI) to achieve emotions more natural. These processing methods are explored as fusion model to detect emotional states accurately. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. Humans can use numerous modalities mutually to predict emotional states in human behaviour, since emotion affects almost all modes. Hence, everyone expects higher recognition rates and accuracy with less duration through the integration of multiple modalities for analysis of emotion recognitions. In recent years, the researchers took keen interest from different fields for the effective analysis and recognition of emotions. Everyone designs different cognitive models by taking different machine learning techniques (MLT) using facial expressions, voice, behaviour (gesture/posture) and by using biosensors (EEG, ECG, EMG, EOG, etc.). Emotions play a major role to identify health conditions of human. To recognize human emotions by single modality is less accurate, because single modality method can be futile since humans may sometimes deliberately hide their real emotions. To design a cognitive model of emotion recognition by using voice and EEG signal with high accuracy to respond fluxes of affective states can provide useful features of emotional states. In this paper, the emotion recognition based on multi-channel EEG signals as well as voice signal is reviewed as per standard pipeline fo

关键词： Electrocardiograms

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Design of Automatic Verification System for Electronic Balance Based on machine Vision 1

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3rd international conference on 3D Imaging Technologies - Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2021

作者： Zhang, Juanjuan Wu, Ying Gao, Huan Chongqing Academy of Metrology and Quality Inspection Chongqing China

ISBN: (数字)9789811924521

ISBN: (纸本)9789811924514

Based on the current situation of the application of electronic balance, the method of manual verification is not only inefficient, but also prone to Angle problems. Therefore, researchers put forward an automatic verification system based on machine vision in practice. Through the use of machine vision system to display LCD display character recognition, calculation error, judgment results and other content, and the use of servo electric cylinder to load or unload weight step by step, according to the cylinder, the detection of product material transport and backward is achieved. The final experimental results show that the machine vision system can quickly and accurately complete the character recognition, and the constructed detection system can carry out automatic metrological verification of electronic balance, which has the advantages of intelligence and high efficiency in practice. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Key Parameter Mapping Method of signal-To-Noise Ratio from Link Simulation to System Simulation

Key Parameter Mapping Method of Signal-To-Noise Ratio from L...

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2021 international conference on signal processing and machine learning, CONF-spml 2021

作者： Lei, Jizhao Li, Minghui Jin, Yihui Zhao, Di Wang, Huijun Liu, Yun Song, Ruiliang Lin, Wenliang Wang, Ke Macro Net Communication Co. Ltd Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Shijiazhuang China The Chinese Institute of Electronics Beijing China The 54th Research Institute of CETC Shijiazhuang China

ISBN: (纸本)9781665417341

Research institutions around the world plan to build a simulation platform that links constellation configuration and protocols together to support the design and verification of key technologies in 6G satellite communication systems. An important issue to improve the reliability and efficiency of simulation verification is how to effectively transfer the parameters of link-level simulation and system-level simulation. In this paper, by optimizing the traditional ground-based EESM algorithm, 12 kinds of channels are fitted, and all L2S interface parameter $\beta$ values under MCS are obtained, and a MCS is proposed for $\beta$ fitting verification, and the fitting effect meets the specified error Required by 3GPP. The wideband CQI value is obtained through the simulation results of all links and based on the subband weighted CQI method. Then specify the MCS-CQI mapping table according to the CQI value, and simulate the link adaptive adjustment process through the mapping table. Finally, the feedback SNR and CQI are used to verify whether the corresponding BLER value meets the requirement of less than 10%. © 2021 IEEE.

关键词： Mapping

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Convolutional neural network-based signal discrimination for P300 brain-computer interface

Convolutional neural network-based signal discrimination for...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yongzhi Liao Liumei Zhang Yaoxin Wang School of Computer Science Xi’an Shiyou University Xi’an China

The P300-based brain-machine interface can achieve efficient communication between human brain and computer, and help physically disabled people achieve autonomous control of external devices. In this paper, we propose a convolutional neural network-based signal detection method, which includes signal pre-processing, construction and training of convolutional neural network, and testing and performance evaluation of the model. This paper conducts experimental validation on dataset II of the 3rd BCI competition and compares it with some traditional machine learning methods and deep learning methods. The test accuracy of the experimental results reaches 0.85 and the AUC reaches 0.93, which indicates that the method has significantly improved in classification accuracy and model performance, and has high practicality and application prospects.

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2021 3rd international conference on signal processing and Communication, ICPSC 2021

2021 3rd International Conference on Signal Processing and C...

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3rd international conference on signal processing and Communication, ICPSC 2021

ISBN: (纸本)9781665428644

The proceedings contain 159 papers. The topics discussed include: early detection of skin tumor using dipole antenna;sample and hold circuit with clock boosting;classification of ancient inscription images on the basis of material of the inscriptions;smart surveillance of driver using machine learning;current trends, challenges, and future prospects for automated detection of diabetic retinopathy;intelligent health assessment system for paddy crop using CNN;revisiting the utility of transmission gate and passtransistor logic styles in CMOS VLSI design;optimized support vector machine model for visual sentiment analysis;automated text categorization;interference analysis in cognitive hybrid FSO/RF system;and user centric and collaborative movie recommendation system under customized platforms.

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Advanced machine learning Frameworks for Multimodal Image and Speech signal processing

Advanced Machine Learning Frameworks for Multimodal Image an...

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Advancement in Technology (ICONAT), international conference for

作者： P Ramesh Naidu Saurabh Shandilya Srividya C N Shantanu Sudhir Gujar Supriya Devi Dankan Gowda V Department OF Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Advance Computing Poornima College of Engineering Jaipur Rajasthan India Dept. of ECE Faculty of Engineering Management & Technology BGSIT Adichunchanagiri University B G Nagara India Department of CyberSecurity Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Department of Mathematics Alliance University Bangalore Karnataka India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework; the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework’s efficiency in predicting the multimodal input and demonstrate superiority over the prior methods.

关键词： Accuracy Surveillance Taxonomy Speech recognition machine learning Speech enhancement signal processing Feature extraction Data processing Real-time systems

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