Implementation Of Convolutional Neural Network Research Articles

Convolutional neural networks and Internet of Things for fault detection by aerial monitoring of photovoltaic solar plants

Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access.This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method.

Implementation of Convolutional Neural Network (CNN) Method for Fish Processed Cuisine Image Identification Application with Google Maps Features

The rapid advancement of science and technology encourages dynamic transformation in various sectors, especially in the field of information and communication technology (ICT), especially with the existence of Android-based smartphones. This advancement revolutionizes the way we access information, especially about various processed fish dishes in Indonesia. However, despite the plethora of culinary offerings, travelers often find it difficult to discover traditional dishes through social media platforms. To bridge this gap, a new app that utilizes artificial neural networks has been developed. The app allows users to photograph and upload images of processed fish dishes to recognize and provide detailed descriptions and recipes. In addition, integrating Google Maps makes it easy for users to find nearby places that serve these dishes. Testing the app with a dataset consisting of 1577 images of six types of processed fish dishes yielded promising results, with accuracy reaching 97.57% over 120 epochs. This innovation not only preserves cultural heritage but also enhances the culinary experience for locals and tourists.

Quantization-Based Optimization Algorithm for Hardware Implementation of Convolution Neural Networks

Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm for CNN hardware design and model the performance metrics for the purpose of CNN implementation in resource-constrained devices (RCDs) and optimizing layers in deep neural networks (DNNs). The algorithm novelty is based on blending two quantization techniques to perform full model quantization with optimum accuracy, and without additional neurons. The algorithm is applied to a selected CNN model and implemented on an FPGA. Implementing CNN using broad data is not possible due to capacity issues. With the proposed quantization algorithm, we succeeded in implementing the model on the FPGA using 16-, 12-, and 8-bit quantization. Compared to the 16-bit design, the 8-bit design offers a 44% decrease in resource utilization, and achieves power and energy reductions of 41% and 42%, respectively. Models show that trading off one quantization bit yields savings of approximately 5.4K LUTs, 4% logic utilization, 46.9 mW power, and 147 μJ energy. The models were also used to estimate performance metrics for a sample DNN design.

Multilayer Neural Network Hardware Designs for Faster Convolutional Processing

The demand for efficient hardware implementations of convolutional neural networks (CNNs) has surged with the proliferation of deep learning applications in various domains such as computer vision, natural language processing, and autonomous systems. Convolutional layers, being the fundamental building blocks of CNNs, are computationally intensive, requiring optimized hardware architectures for real-time inference tasks. This paper presents novel hardware designs targeting faster convolutional processing within multilayer neural networks. We propose a multi-faceted approach that leverages parallelism, pipelining, and hardware acceleration techniques to enhance the efficiency of convolution operations. Our design optimally exploits the inherent data-level and model-level parallelism present in CNNs to achieve high throughput while minimizing latency.

Implementation of Convolutional Neural Network (CNN) Algorithm for Autonomous Robot

In Indonesia, the development of autonomous robots has emerged intensively since the last coronavirus pandemic, especially the autonomous UV disinfection (A-UV) robot. A-UV disinfection robot has the purpose of purifying germs and pathogens in critical areas, such as the hospital. As the minuscule creature can be difficult to control, the anticipation of letting no human have contact with it is one of the other purposes of the A-UV disinfection robot. However, the systematic development of the autonomous robot is the priority, where the robot can offer a collision-free obstacle, and target-lock when arriving at the designated location. In this study, two main contributions are proposed to develop the autonomous robot: 1) Convolutional Neural Network (CNN) algorithm to learn the potential surrounding the lock area from the dataset to ensure collision-free during the operation. 2) Original design to ensure the compactness of the autonomous robot with almost omnidirectional UV light. We design the surrounding area with “BOX” as the obstacle and “SIGN STOP” as the target in our CNN dataset. The performance is validated to have 97% and 99% for training and validation performance and 0.3% for loss. The robot prototype was also developed and tested inside a workspace with a size of 2.1 x 3 m. The robot prototype successfully performed the required tasks.

A SWOT ANALYSIS OF BREAST CANCER DIAGNOSIS IN DIGITAL MAMMOGRAPHY USING DEEP CONVOLUTIONAL NEURAL NETWORK

Breast cancer is a significant global health challenge that affects both men and women, leading to cause-specific deaths. Current early screening interventions, such as digital mammography (DM), are susceptible to high false-positives and false-negatives. This paper explores the potential of convolutional neural network (CNN), a form of artificial intelligence (AI), to support screening mammography with the aim to enhance accuracy in lesion detection, image classification and diagnostic prediction. Because the adoption of AI in cancer diagnosis is still in its infancy, the objective of this paper is to provide insight into the benefits and limitations of deep learning-based approaches to detect and diagnose cancer. An analysis of the implementation of CNN in AI-screening mammography models was conducted, using the SWOT strategic analysis tool. Internal strengths that improve the predictive accuracy of CNN include transfer learning and data augmentation, whereas the internal weaknesses include a lack of data standardisation and reproducibility. External opportunities consist of increased sensitivity in differentiating between microcalcifications and non-tumorous structures, improved predictive diagnosis and reduced workload. Nevertheless, integration within clinical settings must also consider the external threats of breaching patient privacy, automation biases and the role of clinical judgement. 

FPGA acceleration of convolutional neural networks: Current applications and future prospects

In the contemporary era of big data, the utilization of deep learning technology has become widespread in the extraction of valuable analytical insights from the data under consideration. This technology finds extensive applications in various domains, including image information recognition, speech processing, and text language analysis. When it comes to accelerating convolutional neural networks (CNNs), the utilization of Field-Programmable Gate Arrays (FPGAs) offers distinct advantages over other hardware accelerators. Nevertheless, it is important to acknowledge that FPGA-based acceleration also comes with its own structural limitations. This article places its focus on two main aspects: firstly, it delves into the present-day application landscape and the latest developmental trajectories of convolutional neural networks. Secondly, it elucidates the inherent characteristics of FPGA implementations of CNNs. Furthermore, the article conducts a comprehensive examination of the pertinent constraints associated with FPGA-accelerated deep learning algorithms. The discussion extends beyond the present moment and ventures into the future, offering insights into the potential advancements in the field of deep learning. Importantly, it also brings into focus the prospective avenues for further research regarding the application of FPGAs in the domain of convolutional neural networks.

Implementation of FFT-Based Convolutional Neural Networks on Medical Imagery for Studying Performance Boost

This study focuses on the implementation of CNNs based on the Fast Fourier Transform (FFT) in medical images to study the performance boost compared to traditional neural networks. Using a Kaggle dataset of brain tumour imagery, we examine the effectiveness of FFT-based CNNs for the detection and classification of brain tumours. By leveraging the advantages of FFT, we aim to improve the efficiency and accuracy of CNNs in analyzing medical images. Our results demonstrate that FFT-based CNNs outperform traditional CNNs in terms of accuracy and computational efficiency. This performance boost is attributed to the inherent properties of FFT, which enable faster convolution operations and reduced complexity. In order to ensure a fair comparison between traditional CNNs and Fourier Transform-based convolutions, the forward pass for both methods was implemented from scratch using NumPy and SciPy libraries. By doing so, any optimization benefits gained by either technique through specific libraries or computation platforms are eliminated. This approach allows for a more accurate evaluation of the relative performance of each method in terms of inference time and accuracy, while also providing insights into potential areas for improvement and optimization.

Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation

Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.

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

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

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 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.