Gần 300 câu trắc nghiệm môn Kinh tế chính trị (có đầy đủ đáp án) được phân làm 3 phần. Các câu trắc nghiệm đã được kiểm duyệt nhiều lần, cả về nội dung lẫn hình thức trình bày (lỗi chính tả, dấu câu...) và được đánh mã số câu hỏi rất phù hợp cho nhu cầu tự học, cũng như sưu tầm.
Các bạn có thể tải file tại: http://vietlod.com/quiz/
This document describes a method for detecting car number plates using neural networks. It involves three main steps: 1) image pre-processing to filter noise and segment the image, 2) feature extraction using edge detection on segmented characters, and 3) character recognition using neural networks trained on extracted features and character ASCII values. The method is tested on various Indian license plate images with the goal of accurately identifying characters. It aims to improve on other techniques by leveraging neural networks for recognition.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 65, NO. 3, M.docxsheronlewthwaite
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 65, NO. 3, MARCH 2018 2727
Electric Locomotive Bearing Fault Diagnosis
Using a Novel Convolutional
Deep Belief Network
Haidong Shao, Hongkai Jiang , Member, IEEE, Haizhou Zhang, and Tianchen Liang
Abstract—Bearing fault diagnosis is of significance to
enhance the reliability and security of electric locomotive.
In this paper, a novel convolutional deep belief network
(CDBN) is proposed for bearing fault diagnosis. First, an
auto-encoder is used to compress data and reduce the di-
mension. Second, a novel CDBN is constructed with Gaus-
sian visible units to learn the representative features. Third,
exponential moving average is employed to improve the
performance of the constructed deep model. The proposed
method is applied to analyze experimental signals collected
from electric locomotive bearings. The results show that
the proposed method is more effective than the traditional
methods and standard deep learning methods.
Index Terms—Convolutional deep belief network (CDBN),
electric locomotive bearing, exponential moving average
(EMA), fault diagnosis, feature learning.
NOMENCLATURE
ANFIS Adaptive neuro fuzzy inference system.
ANN Artificial neural network.
BPNN Back propagation neural network.
CDBN Convolutional deep belief network.
CNN Convolutional neural network.
CRBM Convolutional restricted Boltzmann machine.
DAE Deep auto-encoder.
DBN Deep belief network.
EMA Exponential moving average.
FD Frequency domain.
PCA Principal component analysis.
RBM Restricted Boltzmann machine.
SVM Support vector machine.
TD Time domain.
Manuscript received January 13, 2017; revised April 24, 2017 and
June 26, 2017; accepted August 5, 2017. Date of publication August
25, 2017; date of current version December 15, 2017. This work was
supported in part by the National Natural Science Foundation of China
under Grant 51475368, in part by the Shanghai Engineering Research
Center of Civil Aircraft Health Monitoring Foundation of China under
Grant GCZX-2015-02, and in part by the Innovation Foundation for Doc-
tor Dissertation of Northwestern Polytechnical University under Grant
CX201710. (Corresponding author: Hongkai Jiang.)
The authors are with the School of Aeronautics, Northwestern Poly-
technical University, Xi’an 710072, China (e-mail: [email protected]
edu.cn; [email protected]; [email protected];
[email protected]).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIE.2017.2745473
I. INTRODUCTION
E LECTRIC locomotive is playing a more and more impor-tant role in the modern transportation. The key parts of
electric locomotive usually get various faults due to the harsh
operating conditions, which may result in great catastrophes.
Bearing is one of the most widely used components in elec-
tric locomotive [1]; thus, automatic and accurate fault diagnosis
techniques are critically needed to ensure the sa ...
Classification of Vehicles Based on Audio Signals using Quadratic Discriminan...ijsc
The focusof this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method, we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.
Classification of vehicles based on audio signalsijsc
The document describes a method for classifying vehicles based on their audio signals using quadratic discriminant analysis. Feature vectors containing short-time energy, average zero-crossing rate, and pitch frequency are extracted from periodic segments of vehicle audio signals. The method achieves better classification accuracy by only considering feature vectors with high energy, as these correspond better to the vehicle sounds and exclude low energy background noise regions. Simulation results show the proposed method of separating high energy vectors based on thresholds of average energy and zero-crossing rate improves classification performance compared to considering all vectors.
IRJET- Musical Instrument Recognition using CNN and SVMIRJET Journal
This document discusses a study that uses convolutional neural networks (CNNs) and support vector machines (SVMs) to recognize musical instruments in audio recordings. The researchers aim to convert audio excerpts to images and use CNNs to classify instruments, then combine the CNN classifications with SVM classifications to improve accuracy. They discuss related work on instrument recognition using other methods. The proposed model uses MFCC features with SVM and passes audio converted to images through four convolutional layers and fully connected layers in the CNN. Combining the CNN and SVM results through weighted averaging is expected to provide higher accuracy than either method alone for classifying instruments in the IRMAS dataset.
Gần 300 câu trắc nghiệm môn Kinh tế chính trị (có đầy đủ đáp án) được phân làm 3 phần. Các câu trắc nghiệm đã được kiểm duyệt nhiều lần, cả về nội dung lẫn hình thức trình bày (lỗi chính tả, dấu câu...) và được đánh mã số câu hỏi rất phù hợp cho nhu cầu tự học, cũng như sưu tầm.
Các bạn có thể tải file tại: http://vietlod.com/quiz/
This document describes a method for detecting car number plates using neural networks. It involves three main steps: 1) image pre-processing to filter noise and segment the image, 2) feature extraction using edge detection on segmented characters, and 3) character recognition using neural networks trained on extracted features and character ASCII values. The method is tested on various Indian license plate images with the goal of accurately identifying characters. It aims to improve on other techniques by leveraging neural networks for recognition.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 65, NO. 3, M.docxsheronlewthwaite
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 65, NO. 3, MARCH 2018 2727
Electric Locomotive Bearing Fault Diagnosis
Using a Novel Convolutional
Deep Belief Network
Haidong Shao, Hongkai Jiang , Member, IEEE, Haizhou Zhang, and Tianchen Liang
Abstract—Bearing fault diagnosis is of significance to
enhance the reliability and security of electric locomotive.
In this paper, a novel convolutional deep belief network
(CDBN) is proposed for bearing fault diagnosis. First, an
auto-encoder is used to compress data and reduce the di-
mension. Second, a novel CDBN is constructed with Gaus-
sian visible units to learn the representative features. Third,
exponential moving average is employed to improve the
performance of the constructed deep model. The proposed
method is applied to analyze experimental signals collected
from electric locomotive bearings. The results show that
the proposed method is more effective than the traditional
methods and standard deep learning methods.
Index Terms—Convolutional deep belief network (CDBN),
electric locomotive bearing, exponential moving average
(EMA), fault diagnosis, feature learning.
NOMENCLATURE
ANFIS Adaptive neuro fuzzy inference system.
ANN Artificial neural network.
BPNN Back propagation neural network.
CDBN Convolutional deep belief network.
CNN Convolutional neural network.
CRBM Convolutional restricted Boltzmann machine.
DAE Deep auto-encoder.
DBN Deep belief network.
EMA Exponential moving average.
FD Frequency domain.
PCA Principal component analysis.
RBM Restricted Boltzmann machine.
SVM Support vector machine.
TD Time domain.
Manuscript received January 13, 2017; revised April 24, 2017 and
June 26, 2017; accepted August 5, 2017. Date of publication August
25, 2017; date of current version December 15, 2017. This work was
supported in part by the National Natural Science Foundation of China
under Grant 51475368, in part by the Shanghai Engineering Research
Center of Civil Aircraft Health Monitoring Foundation of China under
Grant GCZX-2015-02, and in part by the Innovation Foundation for Doc-
tor Dissertation of Northwestern Polytechnical University under Grant
CX201710. (Corresponding author: Hongkai Jiang.)
The authors are with the School of Aeronautics, Northwestern Poly-
technical University, Xi’an 710072, China (e-mail: [email protected]
edu.cn; [email protected]; [email protected];
[email protected]).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIE.2017.2745473
I. INTRODUCTION
E LECTRIC locomotive is playing a more and more impor-tant role in the modern transportation. The key parts of
electric locomotive usually get various faults due to the harsh
operating conditions, which may result in great catastrophes.
Bearing is one of the most widely used components in elec-
tric locomotive [1]; thus, automatic and accurate fault diagnosis
techniques are critically needed to ensure the sa ...
Classification of Vehicles Based on Audio Signals using Quadratic Discriminan...ijsc
The focusof this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method, we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.
Classification of vehicles based on audio signalsijsc
The document describes a method for classifying vehicles based on their audio signals using quadratic discriminant analysis. Feature vectors containing short-time energy, average zero-crossing rate, and pitch frequency are extracted from periodic segments of vehicle audio signals. The method achieves better classification accuracy by only considering feature vectors with high energy, as these correspond better to the vehicle sounds and exclude low energy background noise regions. Simulation results show the proposed method of separating high energy vectors based on thresholds of average energy and zero-crossing rate improves classification performance compared to considering all vectors.
IRJET- Musical Instrument Recognition using CNN and SVMIRJET Journal
This document discusses a study that uses convolutional neural networks (CNNs) and support vector machines (SVMs) to recognize musical instruments in audio recordings. The researchers aim to convert audio excerpts to images and use CNNs to classify instruments, then combine the CNN classifications with SVM classifications to improve accuracy. They discuss related work on instrument recognition using other methods. The proposed model uses MFCC features with SVM and passes audio converted to images through four convolutional layers and fully connected layers in the CNN. Combining the CNN and SVM results through weighted averaging is expected to provide higher accuracy than either method alone for classifying instruments in the IRMAS dataset.
Acoustic Scene Classification by using Combination of MODWPT and Spectral Fea...ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. Mie Mie Oo | Lwin Lwin Oo "Acoustic Scene Classification by using Combination of MODWPT and Spectral Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27992.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/27992/acoustic-scene-classification-by-using-combination-of-modwpt-and-spectral-features/mie-mie-oo
Automatism System Using Faster R-CNN and SVMIRJET Journal
The document describes a proposed system to automatically manage vacant parking spaces using computer vision techniques. The system would use existing surveillance cameras installed in parking lots. It detects vehicles in images using a Faster R-CNN object detection model. This model uses a Region Proposal Network to quickly detect objects. An SVM classifier is then used to classify detected objects as free or occupied parking spaces. The goal is to assist drivers in finding available spaces more efficiently.
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
This document is a resume for Naveen Avemulapalli summarizing his qualifications for an electrical engineering position. It includes details about his educational and professional background such as an MSEE from Michigan Technological University with a 3.85 GPA. It also lists relevant coursework, skills, projects and a publication demonstrating experience in areas like wireless communications, signal processing, and embedded systems.
Traffic Signboard Classification with Voice alert to the driver.pptxharimaxwell0712
The basic idea of proposed system is to provide alertness to the driver about the presence of traffic signboard at a particular distance apart. It generates a warning to the driver in advance of any danger. The warning allows the driver to take appropriate actions in order to avoid the accident.The system takes continuous video input from the console monitor or camera installed on the car's bonnet. The underlying algorithm extracts the features of the input image and matches them with an existing library of traffic sign.
The output is fed to the driving assistance system and in turn drives the car accordingly. We developed this intelligent system using Machine Learning.This device will take camera feeds and upgrade the system
instantaneously.
IRJET- Application of MCNN in Object DetectionIRJET Journal
This document discusses using a multi-column convolutional neural network (MCNN) for object detection in videos. The MCNN approach is compared to other methods like CNN and HOG-BOW-Gray pooling and is shown to achieve over 95% accuracy for pedestrian detection. The document outlines extracting frames from videos, dividing images into regions, classifying regions using CNNs, and combining results to detect objects. The MCNN approach is concluded to be useful for applications like medical imaging due to its high detection accuracy.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING -- Part 1NanubalaDhruvan
In part 1 it is discussed about the introduction of traffic management and various methods and literature reviews of various papers and their specifications and finally the research gap
Combinatorial optimization and deep reinforcement learning민재 정
The document discusses using deep learning approaches for solving combinatorial optimization problems like task allocation. It reviews different reinforcement learning methods that have been applied to problems like the vehicle routing problem using pointer networks, transformers, and graph neural networks. Future work opportunities are identified in applying these deep learning techniques to multi-vehicle routing problems and using them to solve specific task allocation scenarios.
This document discusses lane line detection using computer vision techniques. It begins with an introduction that outlines the importance of lane detection for traffic safety and autonomous vehicles. It then reviews several academic papers on lane detection approaches. The problem is defined as detecting lane lines to guide autonomous vehicles and avoid accidents. The methodology section outlines the experimental procedure, which includes preprocessing the image, applying edge detection and masking, using Hough transforms to identify lines, and overlaying the detected lines on the original image. Test images are presented and conclusions discuss how the techniques learned will help identify lane lines to keep autonomous vehicles in their lanes.
Parallel WaveGAN, Yamamoto, Ryuichi, Eunwoo Song, and Jae-Min Kim. "Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram." ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. review by June-Woo Kim
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
IRJET- Texture Analysis and Fracture Identification of Bones X-Ray Images...IRJET Journal
This document presents a study on developing an image processing system for classifying bone fractures using X-ray images. The proposed system uses preprocessing, feature selection, feature extraction using GLCM, and classification with K-Means clustering and SVM. X-ray images of fractured and non-fractured bones are obtained, preprocessed to remove noise, and features are selected using DWT. GLCM is used to extract texture features like entropy, contrast and homogeneity. K-Means clustering groups similar features and SVM classifies images as fractured or non-fractured. The system achieves 90% accuracy in fracture detection and classification.
This document summarizes a research paper on traffic sign recognition using convolutional neural networks (CNNs). It discusses how a two-tier CNN architecture combined with YOLO networks can accurately detect and identify traffic signs, even in adverse weather conditions. The first part provides background on traffic sign recognition and related work using methods like support vector machines and HOG features. It then describes the current implementation which uses a two-tier CNN for sign detection and identification, and analyzes the results showing over 95% accuracy. In conclusion, the implementation proves effective for traffic sign recognition under varying conditions.
Implementation of Various Machine Learning Algorithms for Traffic Sign Detect...IRJET Journal
This document discusses implementing various machine learning algorithms for traffic sign detection and recognition. It compares the accuracies of KNN, multinomial logistic regression, CNN, and random forest algorithms on a German traffic sign dataset. For real-time traffic sign detection, it uses the YOLO v4 model. The document reviews several papers on traffic sign recognition using techniques like SVM, CNN, Capsule Networks and analyzes their reported accuracies. It then describes the proposed system for traffic sign recognition using two datasets and data preprocessing steps before applying the algorithms and evaluating their performance.
A Survey on: Sound Source Separation MethodsIJCERT
now a day’s multimedia databases are growing rapidly on large scale. For the effective management and exploration of large amount of music data the technology of singer identification is developed. With the help of this technology songs performed by particular singer can be clustered automatically. To improve the Performance of singer identification the technologies are emerged that can separate the singing voice from music accompaniment. One of the methods used for separating the singing voice from music accompaniment is non-negative matrix partial co factorization. This paper studies the different techniques for separation of singing voice from music accompaniment.
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATIONsipij
The document describes a method for front and rear vehicle detection using hypothesis generation and verification. In the hypothesis generation stage, potential vehicles are identified using shadow, texture, and symmetry clues. In the hypothesis verification stage, Pyramid Histograms of Oriented Gradients features are extracted and dimensionally reduced using PCA. Genetic algorithm and linear SVM are then used to improve feature performance and classification accuracy, achieving over 97% correct classification on test images.
- The document discusses the speaker's 25 years of experience applying AI techniques to software engineering projects. It covers early work in the 1990s on fault prediction and the challenges of applying machine learning at that time. It then discusses subsequent work in areas like search-based software engineering, natural language processing for requirements engineering, and using simulation and search techniques for testing autonomous vehicle systems. The speaker reflects on both the benefits and challenges of these different AI applications in software engineering.
A Survey on Portable Camera-Based Assistive Text and Product Label Reading Fr...IRJET Journal
This document presents a survey of existing portable camera-based assistive text and product label reading systems for blind persons. It proposes a new framework that uses motion-based targeting to isolate the object of interest and a novel text localization algorithm combining gradient features and edge pixel distributions to automatically locate and recognize text. The system is designed to help blind persons read text labels on hand-held objects in their daily lives through a portable camera, data processing components, and audio output via Bluetooth earpiece. It aims to provide better independent access to common objects like medication bottles than existing optical aids and screen readers.
Acoustic Scene Classification by using Combination of MODWPT and Spectral Fea...ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. Mie Mie Oo | Lwin Lwin Oo "Acoustic Scene Classification by using Combination of MODWPT and Spectral Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27992.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/27992/acoustic-scene-classification-by-using-combination-of-modwpt-and-spectral-features/mie-mie-oo
Automatism System Using Faster R-CNN and SVMIRJET Journal
The document describes a proposed system to automatically manage vacant parking spaces using computer vision techniques. The system would use existing surveillance cameras installed in parking lots. It detects vehicles in images using a Faster R-CNN object detection model. This model uses a Region Proposal Network to quickly detect objects. An SVM classifier is then used to classify detected objects as free or occupied parking spaces. The goal is to assist drivers in finding available spaces more efficiently.
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
This document is a resume for Naveen Avemulapalli summarizing his qualifications for an electrical engineering position. It includes details about his educational and professional background such as an MSEE from Michigan Technological University with a 3.85 GPA. It also lists relevant coursework, skills, projects and a publication demonstrating experience in areas like wireless communications, signal processing, and embedded systems.
Traffic Signboard Classification with Voice alert to the driver.pptxharimaxwell0712
The basic idea of proposed system is to provide alertness to the driver about the presence of traffic signboard at a particular distance apart. It generates a warning to the driver in advance of any danger. The warning allows the driver to take appropriate actions in order to avoid the accident.The system takes continuous video input from the console monitor or camera installed on the car's bonnet. The underlying algorithm extracts the features of the input image and matches them with an existing library of traffic sign.
The output is fed to the driving assistance system and in turn drives the car accordingly. We developed this intelligent system using Machine Learning.This device will take camera feeds and upgrade the system
instantaneously.
IRJET- Application of MCNN in Object DetectionIRJET Journal
This document discusses using a multi-column convolutional neural network (MCNN) for object detection in videos. The MCNN approach is compared to other methods like CNN and HOG-BOW-Gray pooling and is shown to achieve over 95% accuracy for pedestrian detection. The document outlines extracting frames from videos, dividing images into regions, classifying regions using CNNs, and combining results to detect objects. The MCNN approach is concluded to be useful for applications like medical imaging due to its high detection accuracy.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
TRAFFIC MANAGEMENT THROUGH SATELLITE IMAGING -- Part 1NanubalaDhruvan
In part 1 it is discussed about the introduction of traffic management and various methods and literature reviews of various papers and their specifications and finally the research gap
Combinatorial optimization and deep reinforcement learning민재 정
The document discusses using deep learning approaches for solving combinatorial optimization problems like task allocation. It reviews different reinforcement learning methods that have been applied to problems like the vehicle routing problem using pointer networks, transformers, and graph neural networks. Future work opportunities are identified in applying these deep learning techniques to multi-vehicle routing problems and using them to solve specific task allocation scenarios.
This document discusses lane line detection using computer vision techniques. It begins with an introduction that outlines the importance of lane detection for traffic safety and autonomous vehicles. It then reviews several academic papers on lane detection approaches. The problem is defined as detecting lane lines to guide autonomous vehicles and avoid accidents. The methodology section outlines the experimental procedure, which includes preprocessing the image, applying edge detection and masking, using Hough transforms to identify lines, and overlaying the detected lines on the original image. Test images are presented and conclusions discuss how the techniques learned will help identify lane lines to keep autonomous vehicles in their lanes.
Parallel WaveGAN, Yamamoto, Ryuichi, Eunwoo Song, and Jae-Min Kim. "Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram." ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. review by June-Woo Kim
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
IRJET- Texture Analysis and Fracture Identification of Bones X-Ray Images...IRJET Journal
This document presents a study on developing an image processing system for classifying bone fractures using X-ray images. The proposed system uses preprocessing, feature selection, feature extraction using GLCM, and classification with K-Means clustering and SVM. X-ray images of fractured and non-fractured bones are obtained, preprocessed to remove noise, and features are selected using DWT. GLCM is used to extract texture features like entropy, contrast and homogeneity. K-Means clustering groups similar features and SVM classifies images as fractured or non-fractured. The system achieves 90% accuracy in fracture detection and classification.
This document summarizes a research paper on traffic sign recognition using convolutional neural networks (CNNs). It discusses how a two-tier CNN architecture combined with YOLO networks can accurately detect and identify traffic signs, even in adverse weather conditions. The first part provides background on traffic sign recognition and related work using methods like support vector machines and HOG features. It then describes the current implementation which uses a two-tier CNN for sign detection and identification, and analyzes the results showing over 95% accuracy. In conclusion, the implementation proves effective for traffic sign recognition under varying conditions.
Implementation of Various Machine Learning Algorithms for Traffic Sign Detect...IRJET Journal
This document discusses implementing various machine learning algorithms for traffic sign detection and recognition. It compares the accuracies of KNN, multinomial logistic regression, CNN, and random forest algorithms on a German traffic sign dataset. For real-time traffic sign detection, it uses the YOLO v4 model. The document reviews several papers on traffic sign recognition using techniques like SVM, CNN, Capsule Networks and analyzes their reported accuracies. It then describes the proposed system for traffic sign recognition using two datasets and data preprocessing steps before applying the algorithms and evaluating their performance.
A Survey on: Sound Source Separation MethodsIJCERT
now a day’s multimedia databases are growing rapidly on large scale. For the effective management and exploration of large amount of music data the technology of singer identification is developed. With the help of this technology songs performed by particular singer can be clustered automatically. To improve the Performance of singer identification the technologies are emerged that can separate the singing voice from music accompaniment. One of the methods used for separating the singing voice from music accompaniment is non-negative matrix partial co factorization. This paper studies the different techniques for separation of singing voice from music accompaniment.
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATIONsipij
The document describes a method for front and rear vehicle detection using hypothesis generation and verification. In the hypothesis generation stage, potential vehicles are identified using shadow, texture, and symmetry clues. In the hypothesis verification stage, Pyramid Histograms of Oriented Gradients features are extracted and dimensionally reduced using PCA. Genetic algorithm and linear SVM are then used to improve feature performance and classification accuracy, achieving over 97% correct classification on test images.
- The document discusses the speaker's 25 years of experience applying AI techniques to software engineering projects. It covers early work in the 1990s on fault prediction and the challenges of applying machine learning at that time. It then discusses subsequent work in areas like search-based software engineering, natural language processing for requirements engineering, and using simulation and search techniques for testing autonomous vehicle systems. The speaker reflects on both the benefits and challenges of these different AI applications in software engineering.
A Survey on Portable Camera-Based Assistive Text and Product Label Reading Fr...IRJET Journal
This document presents a survey of existing portable camera-based assistive text and product label reading systems for blind persons. It proposes a new framework that uses motion-based targeting to isolate the object of interest and a novel text localization algorithm combining gradient features and edge pixel distributions to automatically locate and recognize text. The system is designed to help blind persons read text labels on hand-held objects in their daily lives through a portable camera, data processing components, and audio output via Bluetooth earpiece. It aims to provide better independent access to common objects like medication bottles than existing optical aids and screen readers.
Similar to Slides of my presentation at EUSIPCO 2017 (20)
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
3. A Hybrid Approach with
Multi-channel I-Vectors and
Convolutional Neural Networks for
Acoustic Scene Classification
Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer and Gerhard Widmer
4. A closer look into our winning submission
at IEEE DCASE-2016 challenge1
for
Acoustic Scene Classification
1) www.cs.tut.fi/sgn/arg/dcase2016/
13.
Deeeeeep learning
⬛ Pros:
⬜ A powerful method for supervised learning
⬜ Convolutional Neural Networks (CNNs)
⬜ Spectrograms as images
⬜ Feature Learning
⬜ Successfully applied on images, speech and music
⬛ Cons:
⬜ Confusion of classes when dealing with noisy scenes
and blurry spectrograms
⬜ Lack of generalization and overfitting if the training data does
not contain various sessions
Piczak, K. J., et al "Environmental sound classification with convolutional neural networks.", 2015.
photo credit: Yann Lecun's slides at NIPS2016 keynote 4/23
14.
Factor Analysis
⬛ Pros:
⬜ Session Variability reduction
⬜ Use of a Universal Background Model (UBM)
⬜ Better generalization due to the unsupervised methodology
⬜ Successfully applied on sequential data such as Speech and
Music
⬛ Cons:
⬜ Relying on engineered features
⬜ Limits to use specialized features for Audio Scene Analysis
because of the independence and Gaussian assumptions in FA
[1] Dehak, Najim, et al. "Front-end factor analysis for speaker verification.",2011.
[2] Elizalde, B, et al. "An i-vector based approach for audio scene detection." (2013).
5/23
15.
A Hybrid system to overcome the
complexities ...
photo credit: www.imgflip.com
16.
A hybrid approach to ASC
⬛ We combine a CNN with an I-Vector based ASC system:
⬜ A CNN is trained on spectrograms
⬜ I-Vector features (based on FA) are extracted from MFCCs
⬛ Late fusion
⬜ A score fusion technique is used to combine the two methods
⬛ Model averaging for better generalization
⬜ Multiple models are trained and the decision from different
models are averaged
Brummer, N., et al. "On calibration of language recognition scores." , 2006.
6/23
18.
A hybrid approach to ASC
⬛ A VGG-style fully convolutional architecture
⬜ A well-known model for object recognition
Conv layer Pooling layer Average pooling layer
Slide...
Sumtheprobabilities
30secs
Feature Learning part Feed-Forward part
Simonyan, K., et al. "Very deep convolutional networks for large-scale image recognition.", 2014.
7/23
20.
I-Vector Features
GMM Train
I-Vector
model
Sparse
statistics
Adapted GMM params = GMM params – unknown matrix . hidden factor
Learned via EM
Training
MFCCs
Many components high dimension
[1] Dehak, Najim, et al. "Front-end factor analysis for speaker verification.",2011.
[2] Elizalde, B, et al. "An i-vector based approach for audio scene detection." (2013).
[3] Kenny, Patrick, et al. "Uncertainty Modeling Without Subspace Methods For Text-Dependent
Speaker Recognition.", 2016.
low dimension
I-vector
Point estimate
low dimension
EM
8/23
21.
I-Vector Features
GMM
MFCCs
Sparse
statistics
I-vector
Adapted GMM params = GMM params – unknown matrix . hidden factor
Sparse
statistics
TrainingExtraction
Learned via EM
Many components
high dimension
high dimension
Train
I-Vector
model
I-vector
Point estimate
low dimension
EM
[1] Dehak, Najim, et al. "Front-end factor analysis for speaker verification.",2011.
[2] Elizalde, B, et al. "An i-vector based approach for audio scene detection." (2013).
[3] Kenny, Patrick, et al. "Uncertainty Modeling Without Subspace Methods For Text-Dependent
Speaker Recognition.", 2016.
low dimension
9/23
22.
I-Vector Features
⬛ Requires a Universal Background Model (UBM):
⬜ A GMM with 256 Gaussian components
⬜ MFCCs features
⬛ MAP estimation of a hidden factor:
⬜ m: mean from the GMM
⬜ M: adapted GMM mean to MFCCs of an audio segment
⬜ Solving the following factor analysis equation:
M = m + T.y
⬜ y is the hidden factor and its MAP estimation is the I-vector
Dehak, Najim, et al. "Front-end factor analysis for speaker verification.",2011.
10/23
23.
Improving I-Vector Features for ASC
GMM I-vectorleft
right
average
difference
GMM I-vector
GMM I-vector
GMM I-vector
⬛ Tuning MFCC parameters:
⬛ I-vectors from MFCCs of different channels
Elizalde, B, et al. "An i-vector based approach for audio scene detection." (2013).
11/23
24.
Post-processing and Scoring I-Vector
Features
⬛ Length-Normalization
⬛ Within-class Covariance Normalization (WCCN)
⬛ Linear Discriminant Analysis (LDA)
⬛ Cosine Similarity:
⬜ Average I-vectors of each class in training set (Model I-vector)
⬜ Compute cosine similarity from each test I-vector to model I-
vector of each class
⬜ Pick the class with maximum similarity
[1] Garcia-Romero, D., et al. "Analysis of i-vector Length Normalization in Speaker Recognition.", 2011.
[2] Hatch, A. O.,et al. "Within-class covariance normalization for SVM-based speaker recognition.", 2006.
[3] Dehak, Najim, et al. "Cosine similarity scoring without score normalization techniques." 2010.
12/23
27.
Linear Logistic Regression for Score
Fusion
⬛ Combining cosine scores of I-vectors with CNN probabilities
⬛ A Linear Logistic Regression (LLR) model is trained on validation
set
⬛ A coefficient is learned for each model and a bias term for each
class.
⬛ Final score is computed by applying the learned coefficients and
the bias terms on the test set scores.
13/23
28.
Model averaging
⬛ 4 separate models trained from each fold
⬛ Average the final score from models in each fold
14/23
30.
TUT Acoustic Scenes 2016 dataset
⬛ 30-seconds audio segments from 15 acoustic scenes:
⬜ Bus - traveling by bus in the city (vehicle)
⬜ Cafe / Restaurant - small cafe/restaurant (indoor)
⬜ Car - driving or traveling as a passenger, in the city (vehicle)
⬜ City center (outdoor)
⬜ Forest path (outdoor)
⬜ Grocery store - medium size grocery store (indoor)
⬜ Home (indoor)
⬜ Lakeside beach (outdoor)
⬜ Library (indoor)
⬜ Metro station (indoor)
⬜ Office - multiple persons, typical work day (indoor)
⬜ Residential area (outdoor)
⬜ Train (traveling, vehicle)
⬜ Tram (traveling, vehicle)
⬜ Urban park (outdoor)
⬛ Development set:
⬜ Each acoustic scene has 78 segments totaling 39 minutes of audio.
⬜ 4 folds cross validation
⬛ Evaluation set:
⬜ 26 segments totaling 13 minutes of audio.
Mesaros, A.,et al "TUT database for acoustic scene classification and sound event detection." , 2016.
15/23
45.
Conclusion
⬛ Performance of I-Vectors can be noticeably improved by tuning
MFCCs
⬛ Different channels contain different information from a scene that
is beneficial to the I-vector system
⬛ I-Vectors and CNNs are complementary
⬛ Score Calibration improved both I-Vectors and CNN
⬛ A late-fusion can efficiently combine the two system’s predictions
⬛ This method is easily adaptable to new conditions
23/23