DCNN-based embedded models for parallel diagnosis of ocular diseases

An automated system for detecting ocular diseases with computer-aided tools is essential to identify different eye disorders through fundus pictures. This is because diagnosing ocular illnesses manually is a complicated, time-consuming, and error-prone process. In this research, two multi-label embedded architectures based on a deep learning strategy were proposed for ocular disease recognition and classification. The ODIR (Ocular Disease Intelligent Recognition) dataset was adopted for those models. The suggested designs were implemented as parallel systems. The first model was developed as a parallel embedded system that leverages transfer learning to implement its classifiers. The implementation of these classifiers utilized the deep learning network from VGG16, while the second model was introduced with a parallel architecture, and its classifiers were implemented based on newly proposed deep learning networks. These networks were notable for their small size, limited layers, speedy response, and accurate performance. Therefore, the new proposed design has several benefits, like a small classification network size (20 % of VGG16), enhanced speed, and reduced energy consumption, as well as the suitability for IoT applications that support smart systems like Raspberry Pi and Self-powered components, which possess the ability to function as long as a charged battery is available. The highest accuracy of 0.9974 and 0.96 has been obtained in both proposed models for Myopia ocular disease detection and classification. Compared to research that had been presented in the same field, the performance accuracy of each of the two models shown was high. The P3448-0000 Jetson Nano Developer Kit is used to implement both of the proposed embedded model

Run-Time Reconfigurable FFT Engine

This paper develops a system level architecture for implementing a cost-efficient, FPGA-based realtime FFT engine. This approach considers both the hardware cost (in terms of FPGA resource requirements), and performance (in terms of throughput). These two dimensions are optimized based on using run time reconfiguration, double buffering technique and the hardware virtualization to reuse the available processing components. The system employs sixteen reconfigurable parallel FFT cores. Each core represents a 16 complex point parallel FFT processor, running in continuous realtime FFT engine. The architecture support transform length of 256 complex points, as a demonstrator to the idea design, using fixed-point arithmetic and has been developed using radix-4 architecture. The parallel Booth technique for realizing the complex multiplier (required in the basic butterfly operation) is chosen. That is to save a lot of hardware compared to other techniques. The simulation results that have been performed using VHDL modeling language and ModelSim software shows that the full design can be implemented using single FPGA platform requiring about 50,000 Slices

Real Time Image Segmentation for Face Detection Based on Fuzzy Logic

The segmentation of objects whose colorcomposition is not trivial represents a difficult task. In this work we propose a fuzzy algorithm application for the segmentation of such objects. It is chosen; by the characteristics that it represents the face segmentation. A priori knowledge about spectral information for certain face skin region classes is used in order to classify image in fuzzy logic classification procedure. The basic idea was to perform the classification procedure first in the supervised and then in fuzzy logic manner. Some information, needed for membership function definition, was taken from supervised maximum likelihood classification. The system uses three membership functions which are taken as Gaussian distribution curve. For real time needs, the system is implemented on an FPGA

SIMD-реалізація глибоких CNN для виявлення короткозорості в одноплатній комп'ютерній системі

Deep learning algorithms, especially Convolution Neural Networks (CNN), have been rapidly developed due to their flexibility and scalability to be adopted in several fields for modeling real-world applications like object detection, image classification, etc. However, their high accuracy incurs intensive computations. Therefore, it is crucial to carefully choose a suitable computer platform and implementation methodology for CNN network architectures while achieving increased efficiency. Parallel architectures are prevalent in CNN implementation. Herein, we present a new Single Instruction Multi Data (SIMD) parallel implementation of the proposed CNN to speed up the execution process and make it suitable to deploy on low-cost, low-power consumption platforms. The proposed implementation produces an improved model of deep CNN executable on a cost-efficient platform and portability to work autonomously with multi-core processing units while maintaining working accuracy. Raspberry Pi 3 B is a low-power target device for implementing our model. The proposed approach is characterized by high diagnostic accuracy of up to 96.35 % while incurring power consumption of 3.65 Watts, achieving power reduction between 19.17 % and 68.45 % compared to the prior work. Meanwhile, it has a fine inference time for the selected platform. The outstanding results of this study reflect the success of employing parallel architectures to utilize the quad courses of the ARM processor on the target platform. The presented model can be an efficient medical assistant to provide automated detection and diagnosis for myopia ocular disease. Thus, it can be a promising healthcare toolkit that reduces the effort of the medical staff and increases the quality of the provided medical services for myopia patientsАлгоритми глибокого навчання, особливо згорткові нейронні мережі (CNN), набули швидкого розвитку завдяки своїй гнучкості та масштабованості для використання в декількох областях для моделювання реальних застосувань, таких як виявлення об’єктів, класифікація зображень тощо. Однак їх висока точність вимагає інтенсивних обчислень. Тому надзвичайно важливо ретельно обирати відповідну комп’ютерну платформу та методологію реалізації мережевих архітектур CNN із забезпеченням підвищеної ефективності. У реалізації CNN переважають паралельні архітектури. В даному дослідженні представлено нову паралельну реалізацію Single Instruction Multi Data (SIMD) запропонованої CNN з метою  прискорити процес виконання та зробити її придатною для розгортання на недорогих платформах з низьким енергоспоживанням. Запропонована реалізація дозволяє отримати вдосконалену модель глибокої CNN для реалізації на економічно ефективній платформі і забезпечує портативність для автономної роботи з багатоядерними процесорами при збереженні точності роботи. Для реалізації нашої моделі використовувався малопотужний цільовий пристрій Raspberry Pi 3 B. Запропонований підхід характеризується високою точністю діагностики до 96,35 % при енергоспоживанні 3,65 Вт, досягаючи зниження енергоспоживання на 19,17–68,45 % порівняно з попередньою роботою. У той же час, він забезпечує гарний час висновку для обраної платформи. Видатні результати даного дослідження відображають успіх застосування паралельних архітектур для використання чотирьох ядер процесора ARM на цільовій платформі. Представлена модель може бути ефективним медичним помічником для автоматизованого виявлення та діагностики короткозорості очей. Таким чином, це може стати перспективним медичним інструментарієм, що дозволяє зменшити зусилля медичного персоналу та підвищити якість наданих медичних послуг для пацієнтів з короткозорістю

Embedded Real-Time Video Surveillance System based on Multi-Sensor and Visual Tracking

This Paper describes the design and implementation of an embedded remote video surveillance system for general purposes security application. The proposed system is able to detect and report vandalism, tampering, and theft activities before they take place via SMS, Email or by a phone call. The system has been enriched with a vast range of sensors to increase its sensing capability of different types of attacks.Moreover, the proposed system has been enhanced by adding a visual verification technique to overcome false alarms generated by sensors, where a video camera is integrated within the system software to capture video footage, verify, and track the abnormal events taking into consideration bandwidth consumption and real-time processing. Finally, the system was implemented using SBC (Raspberry Pi) as a working platform supported by OpenCV and Python as a programming language. The results proved that the proposed system can achieve monitoring and reporting in real-time. Where the average processing time specified to complete all the required tasks for each frame (starting from video source to broadcasting stage) does not exceed 64%. Moreover, the proposed system achieved a reduction in the utilized data size as a result of using image processing algorithms, reaching an average of 91%, which decreased the amount of transferred data to an average of 13.4 Mbit/sec and increased the bandwidth efficiency to an average of 92%. Finally, this system is characterized by being flexible, portable, easy to install, expandable and cost-effective. Therefore, it can be considered as an efficient technology for different monitoring purposes