Crypto Embedded System for Electronic Document

In this paper, a development of low-cost RSA-based Crypto Embedded System targeted for electronic document security is presented. The RSA algorithm is implemented in a re-configurable hardware, in this case Field Programmable Gate Array (FPGA). The 32-bit soft cores of AlteraÂ’s Nios RISC processor is used as the basic building blocks of the proposed complete embedded solutions. AlteraÂ’s SOPC Builder is used to facilitate the development of crypto embedded system, particularly in hardware/software integration stage. The use of Cryptographic Application Programming Interface (CAPI) to bridge the application and the hardware, and the associated communication layer in the embedded system is also discussed. The result obtained shows that the crypto embedded system provides a suitable compromise between the constraints of speed, space and required security level based on the specific demands of targeted applications

An FPGA-based quantum computing emulation framework based on serial-parallel architecture

Hardware emulation of quantum systems can mimic more efficiently the parallel behaviour of quantum computations, thus allowing higher processing speed-up than software simulations. In this paper, an efficient hardware emulation method that employs a serial- parallel hardware architecture targeted for field programmable gate array (FPGA) is proposed. Quantum Fourier transform and Grover’s search are chosen as case studies in this work since they are the core of many useful quantum algorithms. Experimental work shows that, with the proposed emulation architecture, a linear reduction in resource utilization is attained against the pipeline implementations proposed in prior works. The proposed work contributes to the formulation of a proof-of-concept baseline FPGA emulation framework with optimization on datapath designs that can be extended to emulate practical large-scale quantum circuits

Gender classification: a convolutional neural network approach

An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition

Stochastic Computing Correlation Utilization in Convolutional Neural Network Basic Functions

In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work

Finger-Vein Biometric Identification Using Convolutional Neural Network

A novel approach using a convolutional neural network (CNN) for finger-vein biometric identification is presented in this paper. Unlike existing biometric techniques such as fingerprint and face, vein patterns are inside the body, making them virtually impossible to replicate. This also makes finger-vein biometrics a more secure alternative without being susceptible to forgery, damage, or change with time. In conventional finger-vein recognition methods, complex image processing is required to remove noise and extract and enhance the features before the image classification can be performed in order to achieve high performance accuracy. In this regard, a significant advantage of the CNN over conventional approaches is its ability to simultaneously extract features, reduce data dimensionality, and classify in one network structure. In addition, the method requires only minimal image preprocessing since the CNN is robust to noise and small misalignments of the acquired images. In this paper, a reduced-complexity four-layer CNN with fused convolutional-subsampling architecture is proposed for finger-vein recognition. For network training, we have modified and applied the stochastic diagonal Levenberg-Marquardt algorithm, which results in a faster convergence time. The proposed CNN is tested on a finger-vein database developed in-house that contains 50 subjects with 10 samples from each finger. An identification rate of 100.00% is achieved, with an 80/20 percent ratio for separation of training and test samples, respectively. An additional number of subjects have also been tested, in which for 81 subjects an accuracy of 99.38% is achieved

A Low-complexity Complex-valued Activation Function for Fast and Accurate Spectral Domain Convolutional Neural Network

Conventional Convolutional Neural Networks (CNNs), which are realized in spatial domain, exhibit high computational complexity. This results in high resource utilization and memory usage and makes them unsuitable for implementation in resource and energy-constrained embedded systems. A promising approach for low-complexity and high-speed solution is to apply CNN modeled in the spectral domain. One of the main challenges in this approach is the design of activation functions. Some of the proposed solutions perform activation functions in spatial domain, necessitating multiple and computationally expensive spatial-spectral domain switching. On the other hand, recent work on spectral activation functions resulted in very computationally intensive solutions. This paper proposes a complex-valued activation function for spectral domain CNNs that only transmits input values that have positive-valued real or imaginary component. This activation function is computationally inexpensive in both forward and backward propagation and provides sufficient nonlinearity that ensures high classification accuracy. We apply this complex-valued activation function in a LeNet-5 architecture and achieve an accuracy gain of up to 7% for MNIST and 6% for Fashion MNIST dataset, while providing up to 79% and 85% faster inference times, respectively, over state-of-the-art activation functions for spectral domain

Diabetes-related cardiovascular and economic burden in patients hospitalized for heart failure in the US: a recent temporal trend analysis from the National Inpatient Sample

We aimed to study the cardiovascular and economic burden of diabetes mellitus (DM) in patients hospitalized for heart failure (HF) in the US and to assess the recent temporal trend. Data from the National Inpatient Sample were analyzed between 2005 and 2014. The prevalence of DM increased from 40.4 to 46.5% in patients hospitalized for HF. In patients with HF and DM, mean (SD) age slightly decreased from 71 (13) to 70 (13) years, in which 47.5% were males in 2005 as compared with 52% in 2014 (p trend < 0.001 for both). Surprisingly, the presence of DM was associated with lower in-hospital mortality risk, even after adjustment for confounders (adjusted OR = 0.844 (95% CI [0.828–0.860]). Crude mortality gradually decreased from 2.7% in 2005 to 2.4% in 2014 but was still lower than that of non-diabetes patients’ mortality on a yearly comparison basis. Hospitalization for HF also decreased from 211 to 188/100,000 hospitalizations. However, median (IQR) LoS slightly increased from 4 (2–6) to 4 (3–7) days, so did total charges/stay that jumped from 15,704 to 26,858 USD (adjusted for inflation, p trend < 0.001 for both). In total, the prevalence of DM is gradually increasing in HF. However, the temporal trend shows that hospitalization and in-hospital mortality are on a descending slope at a cost of an increasing yearly expenditure and length of stay, even to a larger extent than in patient without DM

Gender Classification: A Convolutional Neural Network Approach

An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition

Aortic valve function post-replacement of severe aortic stenosis by transcatheter procedure versus surgery: a systematic review and metanalysis

Transcatheter aortic valve replacement (TAVR) has shown to reduce mortality compared to surgical aortic valve replacement (sAVR). However, it is unknown which procedure is associated with better post-procedural valvular function. We conducted a meta-analysis of randomized clinical trials that compared TAVR to sAVR for at least 2 years. The primary outcome was post-procedural patient-prosthesis-mismatch (PPM). Secondary outcomes were post-procedural and 2-year: effective orifice area (EOA), paravalvular gradient (PVG) and moderate/severe paravalvular leak (PVL). We identified 6 trials with a total of 7022 participants with severe aortic stenosis. TAVR was associated with 37% (95% CI [0.51–0.78) mean RR reduction of post-procedural PPM, a decrease that was not affected by the surgical risk at inclusion, neither by the transcatheter heart valve system. Postprocedural changes in gradient and EOA were also in favor of TAVR as there was a pooled mean difference decrease of 0.56 (95% CI [0.73–0.38]) in gradient and an increase of 0.47 (95% CI [0.38–0.56]) in EOA. Additionally, self-expandable valves were associated with a higher decrease in gradient than balloon ones (beta = 0.38; 95% CI [0.12–0.64]). However, TAVR was associated with a higher risk of moderate/severe PVL (pooled RR: 9.54, 95% CI [5.53–16.46]). All results were sustainable at 2 years