Histopathological breast cancer image classification by deep neural network techniques guided by local clustering

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identifcation of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. Tis paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets

Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor

Security of embedded systems is the need of the hour. A mathematically secure algorithm runs on a cryptographic chip on these systems, but secret private data can be at risk due to side-channel leakage information. This research focuses on retrieving secret-key information, by performing machine-learning-based analysis on leaked power-consumption signals, from Field Programmable Gate Array (FPGA) implementation of the elliptic-curve algorithm captured from a Kintex-7 FPGA chip while the elliptic-curve cryptography (ECC) algorithm is running on it. This paper formalizes the methodology for preparing an input dataset for further analysis using machine-learning-based techniques to classify the secret-key bits. Research results reveal how pre-processing filters improve the classification accuracy in certain cases, and show how various signal properties can provide accurate secret classification with a smaller feature dataset. The results further show the parameter tuning and the amount of time required for building the machine-learning models

Fake it till you make it: Data Augmentation using Generative Adversarial Networks for all the crypto you need on small devices

Deep learning-based side-channel analysis performance heavily depends on the dataset size and the number of instances in each target class. Both small and imbalanced datasets might lead to unsuccessful side-channel attacks. The attack performance can be improved by generating traces synthetically from the obtained data instances instead of collecting them from the target device. Unfortunately, generating the synthetic traces that have characteristics of the actual traces using random noise is a difficult and cumbersome task. This research proposes a novel data augmentation approach based on conditional generative adversarial networks (cGAN) and Siamese networks, enhancing in this way the attack capability. We present a quantitative comparative machine learning-based side-channel analysis between a real raw signal leakage dataset and an artificially augmented leakage dataset. The analysis is performed on the leakage datasets for both symmetric and public-key cryptographic implementations. We also investigate non-convergent networks\u27 effect on the generation of fake leakage signals using two cGAN based deep learning models. The analysis shows that the proposed data augmentation model results in a well-converged network that generates realistic leakage traces, which can be used to mount deep learning-based side-channel analysis successfully even when the dataset available from the device is not optimal. Our results show potential in breaking datasets enhanced with ``faked\u27\u27 leakage traces, which could change the way we perform deep learning-based side-channel analysis

Channel operations in the residue number system with special modulus on hardware

This paper presents fast hardware algorithms for channel operations in the Residue Number System (RNS) in terms of addition, subtraction and multiplication. These algorithms compare favorably with other popular algorithms due to the use of special moduli in the form of 2n - 1. They are particularly applicable for the largest channel or redundant channels in RNS.4 page(s

Modular multiplication in the residue number system

Public-key cryptography is a mechanism for secret communication between parties who have never before exchanged a secret message. This thesis contributes arithmetic algorithms and hardware architectures for the modular multiplication Z = A × B mod M. This operation is the basis of many public-key cryptosystems including RSA and Elliptic Curve Cryptography. The Residue Number System (RNS) is used to speed up long word length modular multiplication because this number system performs certain long word length operations, such as multiplication and addition, much more efficiently than positional systems. A survey of current modular multiplication algorithms shows that most work in a positional number system, e.g. binary. A new classification is developed which classes these algorithms as Classical, Sum of Residues, Montgomery or Barrett. Each class of algorithm is analyzed in detail, new developments are described, and the improved algorithms are implemented and compared using FPGA hardware. Few modular multiplication algorithms for use in the RNS have been published. Most are concerned with short word lengths and are not applicable to public-key cryptosystems that require long word length operations. This thesis sets out the hypothesis that each of the four classes of modular multiplication algorithms possible in positional number systems can also be used for long word length modular multiplication in the RNS; moreover using the RNS in this way will lead to faster implementations than those which restrict themselves to positional number systems. This hypothesis is addressed by developing new Classical, Sum of Residues and Barrett algorithms for modular multiplication in the RNS. Existing Montgomery RNS algorithms are also discussed. The new Sum of Residues RNS algorithm results in a hardware implementation that is novel in many aspects: a highly parallel structure using short arithmetic operations within the RNS; fully scalable hardware; and the fastest ever FPGA implementation of the 1024-bit RSA cryptosystem at 0.4 ms per decryption.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2009

Adaptive temperature control systems design

This paper presents an advanced low-cost temperature control system. The system takes the highest performance-cost ratio as its aim. It uses the low-cost 8-bit MCS-51 MCU and integrated temperature sensor to achieve the on-site control. A lower cost remote host computer can be used in this system. In the case of a relatively larger application, it can be expanded to a control system of master-slave distribution and utilize the computing and storage resources of the microcontroller. It realizes a wider range of temperature adjustment, and then put the state of the system into a database of the microcontroller to be ascertained later.5 page(s

A Case study on computer education using problem/project based learning

In the development of a new professional course, Digital Systems Design, at the author's university, a problem based learning (PBL) approach has been used. A major component of this course is a team-based project to design and build a digital controller on an FPGA to control the traffic lights of a complex traffic intersection. The project follows the CDIO (Conceive, Design, Implement, Operate) context of engineering education that is being adopted by considerable computer and engineering departments world-wide.4 page(s

Wavelet based pitch detection and voiced/unvoiced decision

This paper describes property of the sudden change of a speech signal on its Glottal Closure Instant (GCI) and thereby discusses the principle of the localization of wavelets in both time and frequency domains. Based on this discussion, an algorithm for voiced/unvoiced segment decision and pitch detection is presented.5 page(s

Digital design of a BCD multiplication scheme

This paper presents a new scheme for BCD multiplication. This new scheme features a completely carry-free multiplication. The parallel structure of the new algorithm is investigated. This fast architecture was derived by looking at multiplication in a fresh perspective. A carry is able to be computed without performing multiplication on any of the previous bits. The new approach is expected to have a lot of potential contributions to digital design.4 page(s