scholarly journals An optimized method towards formal verification of mixed signals using differential fed neural network over FFNN

2019 ◽  
Vol 9 (5) ◽  
pp. 3423
Author(s):  
Vidya D.S. ◽  
Manjunath Ramachandra
Keyword(s):  
Neural Network ◽  
Formal Verification ◽  
Semiconductor Device ◽  
Cost Effective ◽  
Consumer Electronics ◽  
Manufacturing Cost ◽  
Training Models ◽  
Software And Hardware ◽  
On Chip ◽  
Verification Techniques

<span lang="EN-US">Today, the semiconductor industries are rapidly usinganalog and mixed signals to achieve cost-effective solutions on a System on Chip (SoC) design.  The SoC device is a part of analog, digital and essential mixed-signal models/circuits merged on a semiconductor device, which provides the platform to build modern retail/consumer electronics appliances with smart technology. In order to evaluate the mixed signals, the conventional approaches are not effective with respect to its performance, time and manufacturing cost. Thus, the recent researches were much interested in formal verification technique as it provides the evidence of conscious algorithms in a system. The demand for formal verification in the SoC designs in the context of software and hardware platform is high because of its cost and accuracy. Thus, the paper introduces atechnique of formal verification for mixed signals by using training models of the Differential fed neural network (DFNN) over feedforward neural network (FFNN). The formal verification is performed through equivalence checking by using recently adopted designs as reference designs. The outcomes of the verification techniques suggests that DFNN based technique improves the training accuracy and optimizes the hardware resources like area, power than the FFNN based technique.</span>

AUTOMATIC FAILURE ANALYSIS USING SAFECHARTS

2007 ◽  
Vol 17 (01) ◽  
pp. 57-78
Author(s):  
YEAN-RU CHEN ◽  
PAO-ANN HSIUNG
Keyword(s):  
Failure Analysis ◽  
Formal Verification ◽  
Timed Automata ◽  
Consumer Electronics ◽  
Critical Systems ◽  
Human Beings ◽  
Medical Systems ◽  
Safety Critical ◽  
Safety Critical Systems ◽  
Verification Techniques

With rapid developments in science and technology, we now see the ubiquitous use of different types of safety-critical systems in our daily lives such as in avionics, consumer electronics, and medical systems. In such systems, unintentional design faults might result in injury or even death to human beings. To avoid such mishaps, we need to verify safety-critical systems thoroughly and formal verification techniques such as model checking are a very promising approach. However, modeling the systems formally is a challenging task, which is further aggravated by the necessity to model faults and automatic repairs in safety-critical systems. Currently, there is no automatic technique in formal verification that can aid system designers in formally modeling the faults and repairs. This work contributes by proposing an extension to the Safecharts model so that faults and repairs are easily modeled and then the Safecharts are transformed into semantically equivalent Extended Timed Automata models that can be directly model checked. In this way, automatic failure analysis techniques are integrated into the SGM model checker. Application examples show the feasibility and benefits of the proposed model-driven verification of safety-critical systems.

Formal Verification of Control System Software

2019 ◽  
Author(s):  
Pierre-Loïc Garoche
Keyword(s):  
Control System ◽  
Formal Verification ◽  
Formal Analysis ◽  
Critical Systems ◽  
System Software ◽  
Unified Approach ◽  
Verification Methods ◽  
Autonomous Cars ◽  
Computer Scientists ◽  
Verification Techniques

The verification of control system software is critical to a host of technologies and industries, from aeronautics and medical technology to the cars we drive. The failure of controller software can cost people their lives. This book provides control engineers and computer scientists with an introduction to the formal techniques for analyzing and verifying this important class of software. Too often, control engineers are unaware of the issues surrounding the verification of software, while computer scientists tend to be unfamiliar with the specificities of controller software. The book provides a unified approach that is geared to graduate students in both fields, covering formal verification methods as well as the design and verification of controllers. It presents a wealth of new verification techniques for performing exhaustive analysis of controller software. These include new means to compute nonlinear invariants, the use of convex optimization tools, and methods for dealing with numerical imprecisions such as floating point computations occurring in the analyzed software. As the autonomy of critical systems continues to increase—as evidenced by autonomous cars, drones, and satellites and landers—the numerical functions in these systems are growing ever more advanced. The techniques presented here are essential to support the formal analysis of the controller software being used in these new and emerging technologies.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

Implementation of an On-Chip Learning Neural Network IC Using Highly Linear Charge Trap Device

2021 ◽  
pp. 1-13
Author(s):  
Jong-Moon Choi ◽  
Do-Wan Kwon ◽  
Je-Joong Woo ◽  
Eun-Je Park ◽  
Kee-Won Kwon
Keyword(s):  
Neural Network ◽  
Linear Charge ◽  
Charge Trap ◽  
On Chip

scholarly journals 3D printed microfluidic lab-on-a-chip device for fiber-based dual beam optical manipulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haoran Wang ◽  
Anton Enders ◽  
John-Alexander Preuss ◽  
Janina Bahnemann ◽  
Alexander Heisterkamp ◽  
...  
Keyword(s):  
Optical Fibers ◽  
Single Cells ◽  
Lab On A Chip ◽  
Cost Effective ◽  
Optical Manipulation ◽  
Hydrodynamic Focusing ◽  
Trapping Region ◽  
Dual Beam ◽  
3D Printed ◽  
On Chip

Abstract3D printing of microfluidic lab-on-a-chip devices enables rapid prototyping of robust and complex structures. In this work, we designed and fabricated a 3D printed lab-on-a-chip device for fiber-based dual beam optical manipulation. The final 3D printed chip offers three key features, such as (1) an optimized fiber channel design for precise alignment of optical fibers, (2) an optically clear window to visualize the trapping region, and (3) a sample channel which facilitates hydrodynamic focusing of samples. A square zig–zag structure incorporated in the sample channel increases the number of particles at the trapping site and focuses the cells and particles during experiments when operating the chip at low Reynolds number. To evaluate the performance of the device for optical manipulation, we implemented on-chip, fiber-based optical trapping of different-sized microscopic particles and performed trap stiffness measurements. In addition, optical stretching of MCF-7 cells was successfully accomplished for the purpose of studying the effects of a cytochalasin metabolite, pyrichalasin H, on cell elasticity. We observed distinct changes in the deformability of single cells treated with pyrichalasin H compared to untreated cells. These results demonstrate that 3D printed microfluidic lab-on-a-chip devices offer a cost-effective and customizable platform for applications in optical manipulation.

scholarly journals Detection of Strawberry Diseases Using a Convolutional Neural Network

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Jia-Rong Xiao ◽  
Pei-Che Chung ◽  
Hung-Yi Wu ◽  
Quoc-Hung Phan ◽  
Jer-Liang Andrew Yeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Powdery Mildew ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Cost Effective ◽  
Gray Mold ◽  
Leaf Blight ◽  
Crown Rot ◽  
Accuracy Rate ◽  
Fruit Disease

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

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