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Angle-Based Feature learning in GNN for 3D Object Detection Using Point Cloud 3rd

Angle-Based Feature Learning in GNN for 3D Object Detection ...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Ansari, Md. Afzal Meraz, Md. Chakraborty, Pavan Javed, Mohammed Indian Institute of Information Technology Allahabad India

ISBN: (纸本)9789811908392

In this paper, we present new feature encoding methods for detection of 3D objects in point clouds. We used a graph neural network (GNN) for detection of 3D objects, namely cars, pedestrians, and cyclists. Feature encoding is one of the important steps in detection of 3D objects. The dataset used is point cloud data which is irregular and unstructured, and it needs to be encoded in such a way that ensures better feature encapsulation. Earlier works have used relative distance as one of the methods to encode the features. These methods are not resistant to rotation variance problems in graph neural networks. We have included angular-based measures while performing feature encoding in graph neural networks. Along with that, we have performed a comparison between other methods like absolute, relative, Euclidean distances, and a combination of the angle and relative methods. The model is trained and evaluated on the subset of the KITTI object detection benchmark dataset under resource constraints. Our results demonstrate that a combination of angle measures and relative distance has performed better than other methods. In comparison to the baseline method (relative), it achieved better performance. We also performed time analysis of various feature encoding methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Improving system latency of AI accelerator with on-chip pipelined activation preprocessing and multi-mode batch inference 3

Improving system latency of AI accelerator with on-chip pipe...

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IEEE 3rd international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Chen, Wenxuan Wang, Zheng Lei, Ming Dong, Bo Wang, Zhuo Yang, Yongkui Chen, Chao Guo, Weiyu Liang, Chen Zhang, Qian Fang, Wenqi Yu, Zhibin Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Xidian Univ Sch Microelect Xian Peoples R China Univ Sci & Technol China Suzhou Peoples R China Nanhu Lab Jiaxing Peoples R China

ISBN: (纸本)9781665419130

State-of-the-art neural network accelerators exploit massive computing parallelism to achieve high throughput. However, significant latency is observed on the master-slave-based AI acceleration system which limits its adaptation in real-time applications. Investigation in de-facto GPU system reveals tremendous timing overhead for preprocessing of input activations, which is commonly executed on the host machine through manually customized low-level APIs, nevertheless still results in the moderate utilization rate of processing elements. In this work, we observe one of the key factors for low utilization of processing elements (PEs) is the sparsity of input activations caused by the default DRAM bandwidth utilization (BU) of only 18.75%. To optimize this, we design two on-chip preprocessing modules that merge up-to-five input frames in one DRAM transfer to increase BU to 93.75% and facilitates parallel img2col buffering. Furthermore, the merged activations are simultaneously processed by the neuron engine to reduce the system latency and significantly increases PE utilization. Explicitly, multiple batch inferencing modes including Intra-PE-, temporal- and spatial-sharing are proposed to pipeline with the preprocessing modules, resulting in a 65%similar to 75% reduction of system latency. The on-chip preprocessing modules incur the physical overheads of 31.0% in area and 28.7% in power consumption under 40nm SMIC standard cell library.

关键词： machine learning accelerator latency improvement multi-mode inference

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Neural Network Approach to Channel estimation in Wireless Communication

Neural Network Approach to Channel estimation in Wireless Co...

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IEEE international conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Narendranath Durga Tangudu Swaraj Kumar Satya Kumar Vankayala Seungil Yoon Standards Research Group Samsung R&D Institute Bengaluru India Network Call and OAM Team Call S/W Group Samsung R&D Institute Bengaluru India OAM S/W R&D Group Samsung Electronics Suwon South Korea

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

To meet the high throughput and stringent Quality of Service (QoS) requirements of the fifth and the sixth generation (5G and 6G) users, operators have explored technologies such as Multi-Input Multi-Output (MIMO), massive MIMO (mMIMO), Base Station (BS) densification, Non-Orthogonal Multiplex Access (NOMA) etc. The Central Unit (CU) and Distributed Unit (DU) split architectures defined in Open Radio Access Network (ORAN) and 3rd Generation Partnership Project (3GPP), gives the possibilities to explore the software-based methods, which were restricted earlier due to hardware-centric systems. With software defined Radio Access Network (RAN) at cloud and artificial intelligence being a promising domain for solving various problems, these two can be leveraged in exploring to solve or optimize various existing challenges like channel estimation in the wireless receivers, which is very computationally expensive. In this work, we attempted to improve the performance of the compute intensive module channel estimation using machine learning architecture. Convolutional Neural Network (CNN) based image processing architectures, Super Resolution CNN (SRCNN) and Denoising CNN (DnCNN) were implemented for channel estimation module, along with various combinations of spatial data arrangements of the input signal. The spatial data arrangement of complex and real components of input signal outperformed and yielded better accuracy compared to traditional methods such as estimated Minimum Mean Square Error (MMSE) and Approximate Linear Minimum Mean Square Error (ALMMSE).

关键词： Wireless communication Accuracy Superresolution Channel estimation Computer architecture machine learning Quality of service

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Performance Analysis of Adaptive Filter and machine learning Algorithms for Heart Rate Estimation Using PPG signal 3

Performance Analysis of Adaptive Filter and Machine Learning...

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3rd international conference on Information and Communication Technology for Development for Africa (ICT4DA)

作者： Yigzaw, Tsion Gared, Fikreselam Kassaw, Amare Bahir Dar Univ Bahir Dar Inst Technol Bahir Dar Ethiopia

ISBN: (纸本)9781665436663

Photoplethysmography (PPG) signal provide advanced and simple ways for estimating heart rate (HR) information as an unremarkable system on wearable devices. In this paper, we analyze the performance of adaptive filter and machine learning (ML) algorithms for estimation of HR during physical activity. Three cascades recursive least square (RLS) and cascades normalized least mean square (NLMS) adaptive filters are developed and combined using convex combination scheme to reduce motion artifacts (MA) from the recorded PPG signal. Then, ML based spectral tracking algorithms is applied, to locate the spectral peak corresponding to HR. Four different supervised ML algorithms (Support Vector machine, Decision Tree, K- Nearest Neighbor and Logistic Regression) are examined to track the spectral peaks and the decision tree out performs all three algorithms with an accuracy of 98.96%. Experimental results on the PPG datasets including 23 subjects used in the 2015 IEEE signal processing cup showed that the proposed approach has a very good performance by achieving an average absolute error (AAE) of 1.98 beats per minute (BPM) and the personal correlation coefficient of 0.9899. AAE result proved that the proposed method provides accurate HR estimation performance in comparison with other existing works.

关键词： Adaptive filter HR MA ML PPG

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Spectral Analysis for Automatic Speech Recognition and Enhancement 1

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3rd international conference on machine learning for Networking (MLN)

作者： Oruh, Jane Viriri, Serestina Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-4000 Durban South Africa

ISBN: (数字)9783030708665

ISBN: (纸本)9783030708665;9783030708658

Accurate recognition of noisy speech signal is still an obstacle for wider application of speech recognition technology. The robustness of a speech recognition system is heavily influenced by the ability to handle the presence of background noise. In this work, a Short Time Fourier Transform (STFT) filtering technique for the enhancement and recognition of the speech signal is presented. Conventionally, STFT filtering has been applied in speech analysis. However, in this study the combination of modified STFT with Adaptive window width based on the Chirp Rate, termed ASTFT, in conjunction with Spectrogram Features is proposed for optimal speech recognition and enhancement. LibriSpeech ASR Corpus is the benchmark dataset for this experiment. The spectrum from the enhanced Speech signal is estimated using several spectrogram features to obtain a unit peak amplitude. Priori signal-to-Noise Ratio (SNR) estimation is performed on the modified STFT speech signal, and it achieved an SNR of 31.86 dB which is considered to be an effectively clean speech signal.

关键词： Noise reduction STFT filtering Spectrum estimation Automatic speech recognition Speech enhancement signal-to-Noise-Ratio

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A Technique Based on Ensemble machine learning for The Analysis of Electronic Nose signals

A Technique Based on Ensemble Machine Learning for The Analy...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Amit Taneja Santhosh Kumar Rajamani Deepika Shekhawat Yerrolla Chanti Kapil Joshi Ashish Garg AIT-CSE Chandigarh University Department of E.N.T MAEER MIT Pune’s MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Pune Maharashtra India Department of Computer Science & Engineering Manipal University Jaipur Rajasthan School of Computer Science and Artificial Intelligent SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs)—reliefF, chi-square, and Gini index—for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.

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Research on Model Construction and Algorithm of Intelligent Analysis and Fault Diagnosis of Power Equipment

Research on Model Construction and Algorithm of Intelligent ...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Longxiang Chen Lihua Li Ning Yang Yang Yang State Grid Corporation Of China Beijing China China Electric Power Research Institute Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In this paper, a hierarchical intelligent analysis and fault diagnosis model of power equipment is proposed, which includes data acquisition layer, feature extraction layer, fault diagnosis layer and result output layer. Key parameters such as current and voltage are collected in real time by sensors, and fault-related features are extracted by signal processing technology and machine learning algorithm. In the feature extraction layer, wavelet transform (WT) and principal component analysis (PCA) are used to reduce the dimension of features, so as to fully capture the effective information in fault signals. In the fault diagnosis layer, the deep learning (DL) model is adopted, and a large number of historical fault data are trained to realize high-precision fault identification. Aiming at the shortcomings of existing algorithms, this paper proposes a hybrid fault diagnosis algorithm combining DL and physical model. The algorithm improves the diagnosis accuracy and real-time performance by combining the feature learning ability of DL model and the prior knowledge of physical model. Specifically, a dual-stream network structure is designed. One stream processes the original data, and the other stream processes the data converted by the physical model. Finally, the features of the two streams are weighted and fused at the high level of the network for classification and diagnosis. The experimental results show that the hybrid fault diagnosis algorithm proposed in this paper is superior to other algorithms in accuracy, precision, recall and F1 score, which shows its effectiveness and superiority in dealing with complex fault diagnosis problems. In the experiment with 14,000 training data, 4,000 verification data and 2,000 test data, the accuracy of the algorithm is 0.957, the precision is 0.943, the recall rate is 0.962 and the F1 score is 0.952.

关键词： Fault diagnosis Representation learning Analytical models Accuracy signal processing algorithms Feature extraction Real-time systems Data models Classification algorithms Principal component analysis

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Multiple Feature Extraction Based on PPG signal to Realize Blood Pressure Measurement of Composite Multi-channel Convolutional Neural Network Model

Multiple Feature Extraction Based on PPG Signal to Realize B...

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Electronic Communications, Internet of Things and Big Data (ICEIB), international conference on

作者： Cheng-Gang Lu Lei Wang Department of Electrical Engineering Feng Chia University Taichung Taiwan

Photoplethysmography (PPG) changes according to arterial blood pressure (ABP). The change in the physiological signal reflects the change in the functional state of the heart and vascular system. To prevent cardiovascular disease due to hypertension, the PPG signal has been widely used for non-invasive continuous estimation of blood pressure. In recent years, artificial neural network-based algorithms have found the relationship between ABP and related physiological signals. Compared with traditional cuff measurement methods, it is more intuitive to understand the overall blood pressure trend and avoid invasive pressure Measured bleeding risk. Most artificial neural networks use an electrocardiogram (ECG) and PPG at the same time, and the ECG acquisition device needs electrodes and wires to the chest cavity, which indirectly affects the convenience of physiological signal acquisition. Using three feature extraction techniques with different detailed attributes for a single PPG measurement and substituting short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) into the "compound multi-channel convolutional neural network(CMCNN)" model, we evaluate the overall blood pressure value. Considering that the distorted data caused by motion artifacts causes the training results to fail to converge, we select Physionet's MIMIC-III database and use data purification-based processing steps. We screen out the artifact labels for PPG and ABP at first and then use the artifact labels to perform seven-level data segmentation. Finally, using STFT, CWT, and HHT, we obtain spectrogram, scalogram, and Hilber spectrum. The original waveform of PPG and the spectrum, the scalogram, and the Hilbert spectrum are used as input data for learning and analysis. Compare with the traditional machine learning method, the blood pressure estimation results is closer to the actual situation.

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Emotion Classification Using Xception and Support Vector machine 3rd

Emotion Classification Using Xception and Support Vector Mac...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Phukan, Arpan Gupta, Deepak Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote India Arunachal Pradesh Itanagar India

ISBN: (纸本)9789811908392

There has been a sudden increase in demand for algorithms or models to correctly and accurately identify human emotions. The conformity for machines has come a long way from when smart machines capable of reaching a decision on their own were all that was expected of them, to machines capable of understanding what goes on in a person’s brain. Such autonomous agents can prove to be helpful not only in developing smarter machines but also in the field of medicine. Early prediction or recognizing brainwave patterns for epilepsy, seizures, manic depression, etc. is a key to achieve faster aid responses or prevention. In our work, we are limiting our focus to the most common practices used by researchers in this field, which is to obtain the electroencephalogram or EEG data, extract features and implement a classification algorithm. However, we are also trying to capitalize upon the massive improvements made in the field of supervised and unsupervised learning. The robust depth-wise separable convolution architecture called Xception has been implemented in this study to observe its performance as a feature extractor to the notoriously mutating EEG data. The EEG dataset being used in this study is open source. It is available in Kaggle and has three classes, namely positive, negative and neutral. We are implementing wavelet transform along with the Xception architecture to extract features from the dataset which are then classified using support vector machine. We achieve stellar results as a performance score of 98% can be observed for the measures accuracy, precision, recall as well as F1 score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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A review of machine learning approaches in plant leaf disease detection and classification 3

A review of machine learning approaches in plant leaf diseas...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Applalanaidu, Majji V. Kumaravelan, G. Pondicherry University Department of Computer Science Puducherry India

ISBN: (纸本)9780738111834

To identify the recent advancements in the development of plant disease detection and classification system based on machine learning (ML) and Deep learning (DL) models. In this study, we have collected more than 45 papers published during the year 2017-2020 from the peer-reviewed journals of different databases such as Scopus and Web of Science analogous to the keywords such as plant disease identification, recognition, and classification using ML and DL algorithms. An organized way of analysis of various plant disease classification models has been shown in well-formed tables. In this paper, we have conducted a systematic literature study on the applications of the state-of-the-art ML and DL algorithms such as Support Vector machine (SVM), Neural Network (NN), K-Nearest Neighbor (KNN), Naïve Bayes (NB), other few popular ML algorithms and AlexNet, GoogLeNet, VGGNet, and other few popular DL algorithms respectively for plant disease categorization. Each stated algorithm is characterized through the corresponding processing methods such as image segmentation, feature extraction, along with the standardized experimental-setup metrics such as total number of training/testing dataset employed, number of diseases under considerations, type of classifier utilized, and the percentage of classification accuracy. This work will be a beneficial resource for researchers to recognize any particular type of plant diseases through data-driven approaches. The development of mobile-based applications using the studied ML/DL approaches will surely increase agricultural productivity. © 2021 IEEE.

关键词： learning algorithms

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