Guest Editorial Special Issue on Selected Papers From IEEE ISCAS 2020

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Guest Editorial Circuits and Systems for Smart Agriculture and Healthy Foods

This Special Issue of the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (JETCAS) is dedicated to Circuits and Systems applied to innovative products for the Agriculture and Food value chain

IEEE Access Special Section Editorial: Recent Advances on Hybrid Complex Networks: Analysis and Control

Complex networks typically involve multiple disciplines due to network dynamics and their statistical nature. When modeling practical networks, both impulsive effects and logical dynamics have recently attracted increasing attention. Hence, it is of interest and importance to consider hybrid complex networks with impulsive effects and logical dynamics. Relevant research is prevalent in cells, ecology, social systems, and communication engineering. In hybrid complex networks, numerous nodes are coupled through networks and their properties usually lead to complex dynamic behaviors, including discrete and continuous dynamics with finite values of time and state space. Generally, continuous and discrete sections of the systems are described by differential and difference equations, respectively. Logical networks are used to model the systems where time and state space take finite values. Although interesting results have been reported regarding hybrid complex networks, the analysis methods and relevant results could be further improved with respect to conservative impulsive delay inequalities and reproducibility of corresponding stability or synchronization criteria. Therefore, it is necessary to devise effective approaches to improve the analysis method and results dealing with hybrid complex networks

Design and application of reconfigurable circuits and systems

Open Acces

Deep learning for cancer tumor classification using transfer learning and feature concatenation

Deep convolutional neural networks (CNNs) represent one of the state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death in women. Early detection and treatment of breast cancer are vital for improving survival rates. In this paper, we propose a deep neural network framework based on the transfer learning concept for detecting and classifying breast cancer histopathology images. In the proposed framework, we extract features from images using three pre-trained CNN architectures: VGG-16, ResNet50, and Inception-v3, and concatenate their extracted features, and then feed them into a fully connected (FC) layer to classify benign and malignant tumor cells in the histopathology images of the breast cancer. In comparison to the other CNN architectures that use a single CNN and many conventional classification methods, the proposed framework outperformed all other deep learning architectures and achieved an average accuracy of 98.76%

Combined HW/SW Drift and Variability Mitigation for PCM-based Analog In-memory Computing for Neural Network Applications

Matrix-Vector Multiplications (MVMs) represent a heavy workload for both training and inference in Deep Neural Networks (DNNs) applications. Analog In-memory Computing (AIMC) systems based on Phase Change Memory (PCM) has been shown to be a valid competitor to enhance the energy efficiency of DNN accelerators. Although DNNs are quite resilient to computation inaccuracies, PCM non-idealities could strongly affect MVM operations precision, and thus the accuracy of DNNs. In this paper, a combined hardware and software solution to mitigate the impact of PCM non-idealities is presented. The drift of PCM cells conductance is compensated at the circuit level through the introduction of a conductance ratio at the core of the MVM computation. A model of the behaviour of PCM cells is employed to develop a device-aware training for DNNs and the accuracy is estimated in a CIFAR-10 classification task. This work is supported by a PCM-based AIMC prototype, designed in a 90-nm STMicroelectronics technology, and conceived to perform Multiply-and-Accumulate (MAC) computations, which are the kernel of MVMs. Results show that the MAC computation accuracy is around 95% even under the effect of cells drift. The use of a device-aware DNN training makes the networks less sensitive to weight variability, with a 15% increase in classification accuracy over a conventionally-trained Lenet-5 DNN, and a 36% gain when drift compensation is applied