Implementation Of Convolutional Neural Network Research Articles

Fish age reading using deep learning methods for object-detection and segmentation

Abstract Determination of individual age is one essential step in the accurate assessment of fish stocks. In non-tropical environments, the manual count of ring-like growth patterns in fish otoliths (ear stones) is the standard method. It relies on visual means and individual judgment and thus is subject to bias and interpretation errors. The use of automated pattern recognition based on machine learning may help to overcome this problem. Here, we employ two deep learning methods based on Convolutional Neural Networks (CNNs). The first approach utilizes the Mask R-CNN algorithm to perform object detection on the major otolith reading axes. The second approach employs the U-Net architecture to perform semantic segmentation on the otolith image in order to segregate the regions of interest. For both methods, we applied a simple postprocessing to count the rings on the output masks returned, which corresponds to the age prediction. Multiple benchmark tests indicate the promising performance of our implemented approaches, comparable to recently published methods based on classical image processing and traditional CNN implementation. Furthermore, our algorithms showed higher robustness compared to the existing methods, while also having the capacity to extrapolate missing age groups and to adapt to a new domain or data source.

Enhancing Facial Recognition in Surveillance Systems through Embedded Super-resolution

This document details the implementation of a sub-pixel convolutional neural network designed to enhance the resolution of face images. The model uses a series of filters to progressively increase the number of pixels, estimating the necessary information for new pixels from the original image and training derived from 22000 synthetic images produced by adversarial neural networks. Within the context of surveillance and related applications, the trained convolutional network exhibits beneficial characteristics. For instance, it can be deployed within a device to achieve higher-resolution images than those the physical camera can produce. This research underscores the feasibility of such a device through the implementation and evaluation of the network on the NVIDIA Jetson TX2 embedded system. The findings demonstrate the model's practicality for real-time surveillance applications and its ability to produce superior-quality images compared to several interpolation methods, as determined by an exhaustive testing process measuring various attributes of the generated images.

A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images

Accurate species identification is crucial to assess the medical relevance of a mosquito specimen, but requires intensive experience of the observers and well-equipped laboratories. In this proof-of-concept study, we developed a convolutional neural network (CNN) to identify seven Aedes species by wing images, only. While previous studies used images of the whole mosquito body, the nearly two-dimensional wings may facilitate standardized image capture and reduce the complexity of the CNN implementation. Mosquitoes were sampled from different sites in Germany. Their wings were mounted and photographed with a professional stereomicroscope. The data set consisted of 1155 wing images from seven Aedes species as well as 554 wings from different non-Aedes mosquitoes. A CNN was trained to differentiate between Aedes and non-Aedes mosquitoes and to classify the seven Aedes species based on grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. Our best-performing CNN configuration achieved a macro F1 score of 99% to discriminate Aedes from non-Aedes mosquito species. The mean macro F1 score to predict the Aedes species was 90% for grayscale images and 91% for RGB images. In conclusion, wing images are sufficient to identify mosquito species by CNNs.

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3. Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.

Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system.

Fault diagnosis in semiconductor manufacturing processes using a CNN-based generative adversarial network1

In the current industry, quality inspection in semiconductor manufacturing is of immense significance. Significant achievements have been made in fault diagnosis in fabricated semiconductor wafer manufacturing due to the development of machine learning. Since real-time intermediate signals are non-linear and time-varying, the signals undergo various distortions due to changes in equipment, material, and process. This leads to a drastic change in information in intermediate signals. This paper presents a fault diagnosis model for semiconductor manufacturing processes using a generative adversarial network (GAN). The study aims to address the challenges associated with efficient and accurate fault identification in these complex processes. Our approach involves the extraction of relevant components, development of a paired generator model, and implementation of a deep convolutional neural network. Experimental evaluations were conducted using a comprehensive dataset and compared against six existing models. The results demonstrate the superiority of our proposed model, showcasing higher accuracy, specificity, and sensitivity across various shift tasks. This research contributes to the field by introducing a novel approach for fault diagnosis, paving the way for improved process control and product quality in semiconductor manufacturing. Future work will focus on further optimizing the model and extending its applicability to other manufacturing domains.

Analysis and Implementation of CNN in Real- time Classification and Translation of Kanji Characters

This research explores the training outcomes of the Convolutional Neural Network (CNN) algorithm applied to Kanji character recognition. It employs a CNN architecture with 10 layers for recognizing digital image of Kanji characters from N5 to N1 levels. The training of the CNN model reveals varying accuracies, influenced by factors such as architecture, training data size, and data quality. The lowest accuracy, occurring at the beginning of training, highlights challenges like poor random weight initialization and suboptimal architecture. Conversely, the highest accuracy demonstrates the optimal predictive ability of the model after multiple training iterations. The iterative training process refines the model's accuracy over time, with initial challenges paving the way for a better understanding of the data for future improvements. In the subsequent analysis and system development phase, two algorithms are compared to assess the effectiveness of the CNN algorithm in Kanji character recognition. Testing is conducted at various complexity levels to systematically evaluate accuracy. This testing involves complexity levels at each Kanji character level to assess system accuracy. The developed model shows potential for real-time classification of Kanji characters, with a focus on error rate and accuracy during training and testing. The built model can essentially be used to evaluate the ability to recognize Kanji characters effectively. The model's performance can be assessed based on the error rate and accuracy achieved during the training and testing processes.

Implementation of Convolutional Neural Networks in Memristor Crossbar Arrays with Binary Activation and Weight Quantization.

We propose a hardware-friendly architecture of a convolutional neural network using a 32 × 32 memristor crossbar array having an overshoot suppression layer. The gradual switching characteristics in both set and reset operations enable the implementation of a 3-bit multilevel operation in a whole array that can be utilized as 16 kernels. Moreover, a binary activation function mapped to the read voltage and ground is introduced to evaluate the result of training with a boundary of 0.5 and its estimated gradient. Additionally, we adopt a fixed kernel method, where inputs are sequentially applied to a crossbar array with a differential memristor pair scheme, reducing unused cell waste. The binary activation has robust characteristics against device state variations, and a neuron circuit is experimentally demonstrated on a customized breadboard. Thanks to the analogue switching characteristics of the memristor device, the accurate vector-matrix multiplication (VMM) operations can be experimentally demonstrated by combining sequential inputs and the weights obtained through tuning operations in the crossbar array. In addition, the feature images extracted by VMM during the hardware inference operations on 100 test samples are classified, and the classification performance by off-chip training is compared with the software results. Finally, inference results depending on the tolerance are statistically verified through several tuning cycles.

Implementation of Convolutional Neural Networks for Lung Cancer Detection from CT Scans

Implementation of Convolutional Neural Networks for Lung Cancer Detection from CT Scans

From Detection to Action: Implementing Deep Learning Inference in PLC Systems via Docker Services

This paper focuses on the implementation of recognition and detection algorithms for programmable logic controllers (PLCs) based on artificial intelligence (AI). AI methods based on deep neural networks (DNN) are characterized by a large number of hidden layers and neurons in hidden layers, which increases the computational complexity of computing the outputs of artificial neural networks (ANNs). Popular convolutional neural networks (CNN) are among such ResNet, AlexNet, GoogLeNet and others. A remote cloud solution is proposed in this paper to compute CNN outputs. The CNN implementation was tested on object recognition and detection applications. Training and testing of the CNN and YOLO detector was carried out in the Python environment.

Implementation and Assessment of Convolutional Neural Networks for Non-Invasive Monitoring of Shark Physiology and BMI Calculation Using YOLOv8 Pose Detection

Implementation and Assessment of Convolutional Neural Networks for Non-Invasive Monitoring of Shark Physiology and BMI Calculation Using YOLOv8 Pose Detection

SoC-Based Implementation of 1D Convolutional Neural Network for 3-Channel ECG Arrhythmia Classification via HLS4ML

SoC-Based Implementation of 1D Convolutional Neural Network for 3-Channel ECG Arrhythmia Classification via HLS4ML

Recognition of Sign Language by Using the Implementation of Convolutional Neural Networks

Abstract: The discipline of Sign Language Recognition (SLR) is primarily concerned with the development of techniques that may translate sign language into written or spoken language. The primary purpose of this field is to make it easier for people who are deaf-mute to communicate with the general public. Despite the inherent difficulties that are brought about by the activity's intricacy and the enormous repertory of manual movements that it demands, this specific activity has a significant influence on the larger social setting. The approaches that are now in use for sign language recognition (SLR) are dependent on features that are developed manually in order to portray the motion of sign language. These features are then used in the process of developing classification models. On the other hand, the issue of building dependable features that are capable of properly responding to the wide variety of hand gestures is a considerable difficulty. In this investigation, we suggest a unique convolutional neural network (CNN) architecture as a possible solution to the issue that was described before. One of the capabilities of the Convolutional Neural Network (CNN) is its capacity to independently extract discrete spatial-temporal properties from unprocessed video streams. This eliminates the need for previous information and eliminates the demand for feature building. CNN utilizes many video feeds that include color information, depth cues, and bodily joint locations. These streams are included into the network. A successful integration of color, depth, and trajectory information is achieved by the use of these streams as input. The effectiveness of the Convolutional Neural Network (CNN) is going to be improved as a result of this activity. Through the application of the proposed model to a real dataset received from Microsoft Kinect, the validity of the model is shown. The results of our investigation indicate that its performance is better to that of conventional techniques that are dependent on attributes that are manually developed.

Identification of CNN hyper-parameters for tobacco leaf quality classification on Nvidia Jetson Nano

At the moment, there are some inaccuracies in manual classification for tobacco leaf quality selection, which are influenced by some factors, such as human fatigue or poor lighting. These cases lead to the need for another method that is more consistent, faster and reliable. This research is an implementation of CNN (Convolutional Neural Network) in the classification of fresh tobacco leaves in terms of maturity grades. The primary objective is to develop an efficient CNN model capable to automize the classification of tobacco leaves into three maturity criteria: immature, mature, and old. This methodology consists of some key factors, including color thresholding strategies to purge the noise from the background, Basic Image Manipulation approaches, the systemized screening of different input sizes, and CNN models to enhance the results. The result of this research proves that the developed CNN model has 97.9 % accuracy achieved following 200 training sessions. The model is trained on a dataset comprising 1,249 fresh leaf photos, with a balanced 80:10:10 for train, validation and test ratio. However, the study emphasizes that the CNN model has successfully supported the tobacco leaf discrimination on a Jetson Nano Single-Board Computer with a Graphic Processing Unit (GPU). The study extends beyond the mere theoretical contribution to practical applications in sorting “Gagang Rejeb Sidi” tobacco leaf, the highest quality tobacco variety in South Malang, East Java, Indonesia. Classification using a webcam as an input device shows the fastest processing time of 203.17 ms and the maximum is 1,363 ms. This CNN model algorithm will be applied to a tobacco leaf selector machine, which has a high-speed conveyor and a three-position selector arm. The machine will be operated close to the field in post-harvest time under uniform lighting conditions. Overall, the result of this research is highly relevant in terms of the short duration and accuracy for understanding the commodity classification. It provides a new angle toward speeding up the classification process and improving Indonesian tobacco quality

Implementation of Convolutional Neural Network and Vincenty Formula on Face Attendance System Web-Based for Managing the Attendance

The level of student attendance at tertiary institutions has a crucial role in determining the quality of education. The Information Technology Department at Padang State Polytechnic realizes the urgent need to increase the efficiency of managing student attendance, which currently still relies on a manual attendance system. As an innovative solution, this research proposes designing a face-based attendance system that utilizes facial recognition technology to verify student attendance automatically. One of the challenges in developing a face-based attendance system is the accuracy of calculating the distance between the student's location and the institutional location. To overcome this problem, the research used the Vincenty Formula method which was proven to have a high level of accuracy in calculating the distance between two points on the earth. The integration of this method is expected to increase the accuracy of calculating the distance between the student's location and the institution. Apart from that, this attendance system adopts the Convolutional Neural Network (CNN) algorithm, an algorithm specifically designed to process two-dimensional data. CNN is used to learn and detect features in images so that facial recognition can be done with a high level of accuracy. This approach is expected to improve system performance in recognizing and verifying student attendance.

Convolutional Neural Network Implementation with AlexNet Architecture for Face Recognition

In today's digital era, the process of facial recognition has a very big role. Face recognition has benefits for authentication and identification processes. The development of facial recognition research continues to be carried out with the aim of being able to get the right algorithm, more accurate, faster processing, to be able to recognize faces from various angles. In this study, a performance test was performed on the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture, which is one of the deep learning algorithm developments for facial recognition. AlexNet has 8 convolution layers so that it will not leave even the slightest feature of the object. The process of training and testing the system uses the MATLAB programming language. The number of datasets used is 400 image data which is divided into 360 training image data and 40 test image data. The 400 data come from 4 classes of facial images that have been labeled with names and each classes have 100 images. The training process produces an accuracy of 100% and the testing process produces an accuracy of 95%.

Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications.

Edge artificial intelligence (AI) is receiving a tremendous amount of interest from the machine learning community due to the ever-increasing popularization of the Internet of Things (IoT). Unfortunately, the incorporation of AI characteristics to edge computing devices presents the drawbacks of being power and area hungry for typical deep learning techniques such as convolutional neural networks (CNNs). In this work, we propose a power-and-area efficient architecture based on the exploitation of the correlation phenomenon in stochastic computing (SC) systems. The proposed architecture solves the challenges that a CNN implementation with SC (SC-CNN) may present, such as the high resources used in binary-to-stochastic conversion, the inaccuracy produced by undesired correlation between signals, and the complexity of the stochastic maximum function implementation. To prove that our architecture meets the requirements of edge intelligence realization, we embed a fully parallel CNN in a single field-programmable gate array (FPGA) chip. The results obtained showed a better performance than traditional binary logic and other SC implementations. In addition, we performed a full VLSI synthesis of the proposed design, showing that it presents better overall characteristics than other recently published VLSI architectures.

An Efficient Method for Bangla Handwritten Digit Recognition Using Convolutional Neural Network

Handwritten digit recognition is a fundamental problem in the field of computer vision and pattern recognition. This paper presents a Convolutional Neural Network (CNN) approach for recognizing handwritten Bangla digits. The proposed method utilizes a dataset of handwritten Bangla digit images and trains a CNN model to classify these digits accurately. The dataset is preprocessed to enhance the quality of the images and make them suitable for training the CNN model. The trained model is then tested on a separate test dataset to evaluate its performance in terms of accuracy. With the Ekush: Bangla Handwritten Data - Numerals dataset, we tested our CNN implementation to determine the precision of handwritten characters. According to the test results, 25% of the images using a training set of more than 150,000 images from Ekush dataset had an accuracy of 98.3%.

CNN inference acceleration on limited resources FPGA platforms_epilepsy detection case study

<span lang="EN-US">The use of a convolutional neural network (CNN) to analyze and classify electroencephalogram (EEG) signals has recently attracted the interest of researchers to identify epileptic seizures. This success has come with an enormous increase in the computational complexity and memory requirements of CNNs. For the sake of boosting the performance of CNN inference, several hardware accelerators have been proposed. The high performance and flexibility of the field programmable gate array (FPGA) make it an efficient accelerator for CNNs. Nevertheless, for resource-limited platforms, the deployment of CNN models poses significant challenges. For an ease of CNN implementation on such platforms, several tools and frameworks have been made available by the research community along with different optimization techniques. In this paper, we proposed an FPGA implementation for an automatic seizure detection approach using two CNN models, namely VGG-16 and ResNet-50. To reduce the model size and computation cost, we exploited two optimization approaches: pruning and quantization. Furthermore, we presented the results and discussed the advantages and limitations of two implementation alternatives for the inference acceleration of quantized CNNs on Zynq-7000: an advanced RISC machine (ARM) software implementation-based ARM, NN, software development kit (SDK) and a software/hardware implementation-based deep learning processor unit (DPU) accelerator and DNNDK toolkit.</span>

Implementation of Convolutional Neural Network for Classification of Density Scale and Transparency of Needle Leaf Types

Implementation of Convolutional Neural Network for Classification of Density Scale and Transparency of Needle Leaf Types