scholarly journals Classification of Pedestrian using Convoluted Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1071-1075
Keyword(s):  
Neural Network ◽  
Pedestrian Detection ◽  
Extraction Techniques ◽  
Large Dataset ◽  
Major Work ◽  
Algorithm Performance ◽  
Single Person ◽  
Set Up ◽  
Window Approach

We present our work based on classification of pedestrians into a single person and group of people using Convoluted Neural Network (CNN). Major work was done on classification-based feature extraction techniques before CNN is applied to it. CNN can classify objects without extracting the features. Here, we have set up a complete channel for pedestrian detection using sliding window approach and classification using a CNN network. Alex Net and ResNet are the two architectures used in CNN for implementing the classification algorithm. Performance is evaluated on the PET and Caltech dataset which consists of a number of people who are walking with a group or separately in the scene. We got the optimistic results in case of small dataset used for testing. We have also tested our algorithm over large dataset to verify its performance with the help of performance evaluation metrics.

scholarly journals Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francisco J. Bravo Sanchez ◽  
Md Rahat Hossain ◽  
Nathan B. English ◽  
Steven T. Moore
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Limited Data ◽  
Extraction Techniques ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Current Classification ◽  
Efficient Learning ◽  
Sound Features

AbstractThe use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

Classification of Power Quality Events Using Wavelet Analysis and Probabilistic Neural Network

2016 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Pampa Sinha ◽  
Sudipta Debath ◽  
Swapan Kumar Goswami
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Power Quality ◽  
Distribution System ◽  
Reactive Power ◽  
Probabilistic Neural Network ◽  
Capacitor Switching ◽  
Set Up ◽  
Insulation Coordination

<p>Power quality studies have become an important issue due to widespread use of sensitive electronic equipment in power system. The sources of power quality degradation must be investigated in order to improve the power quality. Switching transients in power systems is a concern in studies of equipment insulation coordination. In this paper a wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching. The detail reactive powers for these five transients are determined and a model which uses the detail reactive power as the input to the Probabilistic neural network (PNN) is set up to classify the above mentioned transients. The simulation has been executed for an 11kv distribution system. With the help of neural network classifier, the transient signals are effectively classified.</p>

scholarly journals Feature Extraction Techniques for Leukocyte Classification - A Review

2018 ◽  
Vol 7 (2.24) ◽  
pp. 155 ◽  
Author(s):  
Diana Baby ◽  
Sujitha Juliet Devaraj
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Blood Sample ◽  
Early Identification ◽  
Statistical Estimation ◽  
Vital Role ◽  
Extraction Techniques ◽  
Manual Estimation

This paper covers an investigation on the various feature extraction techniques employed for the statistical estimation of leukocyte classification from blood sample images since the identification or analysis of these four classes of leukocytes plays a vital role in the early identification of various diseases. The manual estimation of these WBC’s by pathologist is error prone and time consuming. This paper mainly concentrates on the study of leukocyte classification methodology and various feature extraction techniques for the classification of four classes of Leukocytes such as Neutrophil, Lymphocyte, Monocyte, and Eosinophil which can be fed to SVM or neural network for further classification. 

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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