Yet Another SHA-3 Round 3 FPGA Results Paper

The NIST run SHA-3 competition is nearing completion. Currently in its final round, the five remaining competitors are still being examined in hardware, software and for security metrics in order to select a final winner. While there have been many area and speed results reported, one such metric that does not appear to be covered in very great detail is that of power and energy measurements on FPGA. This work attempts to add some new results to this section, namely, measured area, power, energy and iteration time results thereby giving NIST further metrics on which to base their selection decision

An FPGA Technologies Area Examination of the SHA-3 Hash Candidate Implementations

This paper presents an examination of the different FPGA architectures used to implement the various hash function candidates for the currently ongoing NIST-organised SHA-3 competition~\cite{Sha3NIST}. This paper is meant to be used as both a quick reference guide used in conjunction with the results table~\cite{Sha3zoo} as an aid in finding the ”best-fit” FPGA for a particular algorithm, as well as a helpful guide for explaining the many different terms and measurement units used in the various FPGA packages

Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel

Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is validated on a large dataset of EEG recordings from 17 neonates. The obtained results show an increase in the detection rate at very low false detections per hour, particularly achieving a 12% improvement in the detection of short seizure events over the static RBF kernel based system

Toward a personalized real-time diagnosis in neonatal seizure detection

The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures

Distinguishing multiplications from squaring operations

Abstract. In this paper we present a new approach to attacking a modular exponentiation and scalar multiplication based by distinguishing multiplications from squaring operations using the instantaneous power consumption. Previous approaches have been able to distinguish these operations based on information of the specific implementation of the embedded algorithm or the relationship between specific plaintexts. The proposed attack exploits the expected Hamming weight of the result of the computed operations. We extrapolate our observations and assess the consequences for elliptic curve cryptosystems when unified formulae for point addition are used

A Hardware Analysis of Twisted Edwards Curves for an Elliptic Curve Cryptosystem

This paper presents implementation results of a reconfigurable elliptic curve processor defined over prime fields $GF(p)$. We use this processor to compare a new algorithm for point addition and point doubling operations on the twisted Edwards curves, against a current standard algorithm in use, namely the Double-and-Add. Secure power analysis versions of both algorithms are also examined and compared. The algorithms are implemented on an FPGA, and the speed, area and power performance of each are then evaluated for various modes of circuit operation using parallel processing. To the authors\u27 knowledge, this work introduces the first documented FPGA implementation for computations on twisted Edwards curves over fields $GF(p)$

All-or-Nothing Transforms as a Countermeasure to Differential Side-Channel Analysis

All-or-Nothing Encryption was introduced by Rivest as a countermeasure to brute force key search attacks. This work identifies a new application for All-or-Nothing Transforms, as a protocol-level countermeasure to Differential Side-Channel Analysis (DSCA). We describe an extension to the All-or-Nothing protocol, that strengthens the DCSA resistance of the cryptosystem. The resultant scheme is a practical alternative to Boolean and arithmetic masking, used to protect implementations of encryption and decryption operations on electronic devices

Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms

FPGA Implementations of SHA-3 Candidates:CubeHash, Grøstl, L{\sc ane}, Shabal and Spectral Hash

Abstract: Hash functions are widely used in, and form an important part of many cryptographic protocols. Currently, a public competition is underway to find a new hash algorithm(s) for inclusion in the NIST Secure Hash Standard (SHA-3). Computational efficiency of the algorithms in hardware will form one of the evaluation criteria. In this paper, we focus on five of these candidate algorithms, namely CubeHash, Grøstl, L{\sc ane}, Shabal and Spectral Hash. Using Xilinx Spartan-3 and Virtex-5 FPGAs, we present architectures for each of these hash functions, and explore area-speed trade-offs in each design. The efficiency of various architectures for the five hash functions is compared in terms of throughput per unit area. To the best of the authors\u27 knowledge, this is the first such comparison of these SHA-3 candidates in the literature

Challenges of developing robust AI for intrapartum fetal heart rate monitoring

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development