In this paper, a novel high-speed elliptic curve cryptography (ECC) processor implementation for point multiplication (PM) on field-programmable gate array (FPGA) is proposed. A new segmented pipelined full-precision multiplier is used to reduce the latency, and the Lopez-Dahab Montgomery PM algorithm is modified for careful scheduling to avoid data dependency resulting in a drastic reduction in the number of clock cycles (CCs) required. The proposed ECC architecture has been implemented on Xilinx FPGAs' Virtex4, Virtex5, and Virtex7 families. To the best of our knowledge, our single- and three-multiplier-based designs show the fastest performance to date when compared with reported works individually. Our one-multiplier-based ECC processor also achieves the highest reported speed together with the best reported area-time performance on Virtex4 (5.32 μs at 210 MHz), on Virtex5 (4.91 μs at 228 MHz), and on the more advanced Virtex7 (3.18 μs at 352 MHz). Finally, the proposed three-multiplier-based ECC implementation is the first work reporting the lowest number of CCs and the fastest ECC processor design on FPGA (450 CCs to get 2.83 μs on Virtex7).

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.

A new technique to detect eye blinks is proposed based on automatic tracking of facial landmarks to localise the eyes and eyelid contours. Automatic facial landmarks detectors are trained on an in-the-wild dataset and shows an outstanding robustness to varying lighting conditions, facial expressions, and head orientation. The proposed technique estimates the facial landmark positions and extracts the vertical distance between eyelids for each video frame. Next, a Savitzky–Golay (SG) filter is employed to smooth the obtained signal while keeping the peak information to detect eye blinks. Finally, eye blinks are detected as sharp peaks and a finite state machine is used to check for false blink and true blink cases based on their duration. The efficiency of the proposed technique is shown to outperform the state-of-the-art methods on three standard datasets.

HbA1c is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that HbA1c estimates can be obtained from 5- 12 weeks of daily blood glucose measurements. However, these methods suffer from accuracy limitations when applied to incomplete data with missing periods of measurements. The aim of this work is to overcome these limitations improving the accuracy and robustness of HbA1c prediction from time series of blood glucose. A novel data-driven HbA1c prediction model based on deep learning and convolutional neural networks is presented. The model focuses on the extraction of behavioral patterns from sequences of self-monitored blood glucose readings on various temporal scales. Assuming that subjects who share behavioral patterns have also similar capabilities for diabetes control and resulting HbA1c, it becomes possible to infer the HbA1c of subjects with incomplete data from multiple observations of similar behaviors. Trained and validated on a dataset, containing 1543 real world observation epochs from 759 subjects, the model has achieved the mean absolute error of 4.80±0.62 mmol/mol, median absolute error of 3.81 ±0.58 mmol/mol and [Formula: see text] of 0.71 ±0.09 on average during the 10 fold cross validation. Automatic behavioral characterization via extraction of sequential features by the proposed convolutional neural network structure has significantly improved the accuracy of HbA1c prediction compared to the existing methods.

Neurodegenerative diseases causing dementia are known to affect a person’s speech and language. Part of the expert assessment in memory clinics therefore routinely focuses on detecting such features. The current outpatient procedures examining patients’ verbal and interactional abilities mainly focus on verbal recall, word fluency, and comprehension. By capturing neurodegeneration-associated characteristics in a person’s voice, the incorporation of novel methods based on the automatic analysis of speech signals may give us more information about a person’s ability to interact which could contribute to the diagnostic process. In this proof-of-principle study, we demonstrate that purely acoustic features, extracted from recordings of patients’ answers to a neurologist’s questions in a specialist memory clinic can support the initial distinction between patients presenting with cognitive concerns attributable to progressive neurodegenerative disorders (ND) or Functional Memory Disorder (FMD, i.e., subjective memory concerns unassociated with objective cognitive deficits or a risk of progression). The study involved 15 FMD and 15 ND patients where a total of 51 acoustic features were extracted from the recordings. Feature selection was used to identify the most discriminating features which were then used to train five different machine learning classifiers to differentiate between the FMD/ND classes, achieving a mean classification accuracy of 96.2%. The discriminative power of purely acoustic approaches could be integrated into diagnostic pathways for patients presenting with memory concerns and are computationally less demanding than methods focusing on linguistic elements of speech and language that require automatic speech recognition and understanding.

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis’s congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.