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In this paper, we propose a novel parallel architecture for fast hardware implementation of elliptic curve point multiplication (ECPM), which is the key operation of an elliptic curve cryptography processor. The point multiplication over binary fields is synthesized on both FPGA and ASIC technology by designing fast elliptic curve group operations in Jacobian projective coordinates. A novel combined point doubling and point addition (PDPA) architecture is proposed for group operations to achieve high speed and low hardware requirements for ECPM. It has been implemented over the binary field which is recommended by the National Institute of Standards and Technology (NIST). The proposed ECPM supports two Koblitz and random curves for the key sizes 233 and 163 bits. For group operations, a finite-field arithmetic operation, e.g. multiplication, is designed on a polynomial basis. The delay of a 233-bit point multiplication is only 3.05 and 3.56 μs, in a Xilinx Virtex-7 FPGA, for Koblitz and random curves, respectively, and 0.81 μs in an ASIC 65-nm technology, which are the fastest hardware implementation results reported in the literature to date. In addition, a 163-bit point multiplication is also implemented in FPGA and ASIC for fair comparison which takes around 0.33 and 0.46 μs, respectively. The area-time product of the proposed point multiplication is very low compared to similar designs. The performance () and Area × Time × Energy (ATE) product of the proposed design are far better than the most significant studies found in the literature. [ABSTRACT FROM AUTHOR]

Traditional cryptanalysis assumes that an adversary only has access to input and output pairs, but has no knowledge about internal states of the device. However, the advent of side-channel analysis showed that a cryptographic device can leak critical information. In this circumstance, Machine learning is known as a powerful and promising method of analysing of side-channel information. In this paper, an experimental investigation on a FPGA implementation of elliptic curve cryptography (ECC) was conducted to explore the efficiency of side-channel information characterisation based on machine learning techniques. In this work, machine learning is used in terms of principal component analysis (PCA) for the preprocessing stage and a Cascade-Forward Back-Propagation Neural Network (CFBP) as a multi-class classifier. The experimental results show that CFBP can be a promising approach in characterisation of side-channel information. [ABSTRACT FROM AUTHOR]

This study presents a description of an efficient hardware implementation of an elliptic curve cryptography processor (ECP) for modern security applications. A high‐performance elliptic curve scalar multiplication (ECSM), which is the key operation of an ECP, is developed both in affine and Jacobian coordinates over a prime field of size p using the National Institute of Standards and Technology standard. A novel combined point doubling and point addition architecture is proposed using efficient modular arithmetic to achieve high speed and low hardware utilisation of the ECP in Jacobian coordinates. This new architecture has been synthesised both in application‐specific integrated circuit (ASIC) and field‐programmable gate array (FPGA). A 65 nm CMOS ASIC implementation of the proposed ECP in Jacobian coordinates takes between 0.56 and 0.73 ms for 224‐bit and 256‐bit elliptic curve cryptography, respectively. The ECSM is also implemented in an FPGA and provides a better delay performance than previous designs. The implemented design is area‐efficient and this means that it requires not many resources, without any digital signal processing (DSP) slices, on an FPGA. Moreover, the area–delay product of this design is very low compared with similar designs. To the best of the authors' knowledge, the ECP proposed in this study over Fp performs better than available hardware in terms of area and timing. [ABSTRACT FROM AUTHOR]

Abstract: Simultaneously developing velocity and temperature fields in the slip-flow regime are investigated numerically in trapezoidal microchannels with constant wall temperatures. A wide range of channel aspect ratios (0.25⩽ α ⩽2) and side angles (30°⩽ ϕ ⩽90°) are considered in the Reynolds number range 0.1⩽ Re ⩽10. A control-volume based numerical method is used to solve the Navier–Stokes and energy equations with velocity-slip and temperature-jump at the walls. As characterized by the Knudsen number (Kn ⩽0.1), the effects of rarefaction on the key flow features are examined in detail. Major reductions in the friction and heat transfer coefficients are observed in the entrance region due to large amounts of velocity-slip and temperature-jump. In the fully developed region, the friction coefficient decreases strongly both with increasing Kn and aspect ratio but has a weaker dependence on the side angle. The heat transfer coefficient also decreases strongly with increasing rarefaction and aspect ratio; however, as the aspect ratio increases, its sensitivity to Kn decreases. Practical engineering correlations are also provided for fully developed flow friction and heat transfer coefficients. [Copyright &y& Elsevier]

Aim: This research aimed to evaluate the effect of gastroesophageal reflux disease (GERD) on pulmonary volumes, airflows, and airway resistance in the patients without respiratory symptoms and compare them with the healthy subjects. Background: GERD is the return of gastric content into the esophagus and beyond. GERD may play an essential role in the extraesophageal diseases, including chest pain, asthma, laryngitis, chronic cough, and sinusitis. The relation between GERD and airway involvement in asthma and also bronchoconstrictor effects of GERD are well recognized, but its impact on lung parameters in the patients with GERD without respiratory symptoms is unclear. Methods: In a case-control study, 78 GERD patients without pulmonary symptoms and 93 healthy subjects as control group were enrolled. The impulse oscillometry examined airway resistance. The body plethysmograph measured the pulmonary volumes and airflows. Results: The mean age of GERD patients and the healthy subjects were 37.30±9.76 and 34.74±11.10, respectively. A total of 53.8% of patients and 67.7% of healthy subjects were male. The lung volumes measured by the body plethysmography were normal in both patients and healthy subjects. However, there was a significant difference between the groups in forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) (P=0.01) and maximal mid expiratory flow (MMEF) (P=0.008). Airway resistance at R5Hz was significantly higher in the case group than the control group (P=0.001). Conclusion: The results of the current study demonstrated that GERD patients have small airway disease even in the absence of respiratory symptoms. [ABSTRACT FROM AUTHOR]

Background and Objectives: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the disease, distinguishing this complication within the fundus images facilitates early DR detection. In this paper, an automatic analysis of retinal images using convolutional neural network (CNN) is presented.Methods: Our method incorporates a novel technique utilizing a two-stage process with two online datasets which results in accurate detection while solving the imbalance data problem and decreasing training time in comparison with previous studies. We have implemented our proposed CNNs using the Keras library.Results: In order to evaluate our proposed method, an experiment was conducted on two standard publicly available datasets, i.e., Retinopathy Online Challenge dataset and E-Ophtha-MA dataset. Our results demonstrated a promising sensitivity value of about 0.8 for an average of >6 false positives per image, which is competitive with state of the art approaches.Conclusion: Our method indicates significant improvement in MA-detection using retinal fundus images for monitoring diabetic retinopathy. [ABSTRACT FROM AUTHOR]