Image encryption method based on improved ECC and modified AES algorithm

Multimedia Tools and Applications - Currently, embedded systems can be found everywhere in quotidian life. In the development of embedded systems, information security is one of the important...     

Analyzing the fractal feature of nickel thin films surfaces modified by low energy nitrogen ion

Fractal concepts are used to explore how different energies (10, 20 and 50 keV) and fluence of 5 × 10¹⁷ N⁺ cm^?2 affect the morphology of nickel thin film. The nickel thin film with thickness of 100 nm is prepared by electron beam evaporation technique at room temperature on stainless steel (AISI 316) substrates. The nanoscale three‐dimensional (3‐D) surface micro‐morphologies are investigated by atomic force microscopy (AFM). Interface width is used to describe the surface height fluctuations. The autocorrelation function with height‐height correlation function give the quantitative data about the morphology of surface. The value of roughness exponent and fractal dimension is computed by height‐height correlation function. Fractal measure is an important analysis which provides fundamental insights into the texture characteristics and a direct way of testing their functional role.     

Robust Beamforming and Power Allocation in CR MISO Networks with SWIPT to Maximize the Minimum Achievable Rate

Wireless Personal Communications - In this paper, a new method is presented to maximize the minimum achievable rate of the users in a cognitive radio multiple input single output network. The...     

Virtual learning environment to predict withdrawal by leveraging deep learning

The current evolution in multidisciplinary learning analytics research poses significant challenges for the exploitation of behavior analysis by fusing data streams toward advanced decision-making. The identification of students that are at risk of withdrawals in higher education is connected to numerous educational policies, to enhance their competencies and skills through timely interventions by academia. Predicting student performance is a vital decision-making problem including data from various environment modules that can be fused into a homogenous vector to ascertain decision-making. This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short-term memory (LSTM) model. The deployed LSTM model outperforms baseline logistic regression and artificial neural networks by 10.31% and 6.48% respectively with 97.25% learning accuracy, 92.79% precision, and 85.92% recall.     

Pull-in behavior of functionally graded micro/nano-beams for MEMS and NEMS switches

Microsystem Technologies - In this paper, pull-in behavior of cantilever micro/nano-beams made of functionally graded materials (FGM) with small-scale effects under electrostatic force is...     

Prediction of asphaltene deposition during turbulent flow using heat transfer approach

In this study, asphaltene deposition from crude oil has been studied experimentally using a test loop and prediction using theoretical study under turbulent flow (Reynolds numbers below 5000). The effects of many parameters such as oil velocity, surface temperature and concentration of flocculated asphaltene on the asphaltene deposition were investigated. The results showed that asphaltene deposition thickness increases with increasing both surface temperature and concentration of flocculated asphaltene and decreasing oil velocity. Thermal approach was used to describe the mechanisms involved in this process and the results of data fitting showed that there are good agreements between the results of the proposed model and the measured asphaltene deposition rates.     

A low complexity peak-to-average power ratio reduction scheme based on selected mapping and radix-II IFFT in OFDM systems

Because of its lack of feedback process and the simplicity of its searching algorithm, conventional selected mapping (CSLM) is an efficient peak-to-average power ratio (PAPR) reduction technique in orthogonal frequency division multiplexing systems compared to the present techniques such as partial transmit sequence and active constellation extension. The requirement for large numbers of inverse fast Fourier transform (IFFT) blocks to provide desired PAPR reduction performance is introduced as the most significant drawback of the CSLM. This paper uses the special structure of an N-point radix-II IFFT in the CSLM and proposes a low complexity method to reduce the redundant calculations with almost the same PAPR reduction, bit-error rate, and power spectral density performances as those of the CSLM. The simulation results show that the computational complexity is reduced by at least 46.8% compared to that of the CSLM with approximately the same PAPR performance.     

A noise-shaping SAR ADC for energy limited applications in 90 nm CMOS technology

In this paper, an ultra-low-power successive approximation register analog-to-digital converter (ADC) for energy limited applications is presented. The ADC resolution is enhanced by using a noise-shaping technique which does not need any integrator and only uses a finite impulse response (FIR) filter. To provide a first-order noise-shaping, the quantization error is firstly extracted by using the digital-to-analog converter (DAC) dummy capacitor and it is then employed in the error feedback scheme. The proposed structure employs a low-gain and low-swing operational transconductance amplifier (OTA) to realize the FIR filter which operates only at the sampling phase. To minimize the power consumption of the ADC analog part, the OTA is powered off during the conversion phase. The proposed ADC is designed and simulated in a 90 nm CMOS technology using Spectre with a 0.5 V single power supply. The simulated ADC uses a fully-differential 8-bit charge redistribution DAC with an oversampling ratio of 8 and achieves 10.7-bit accuracy. The simulated average power consumption is 4.53 μW and the achieved maximum SNDR and SFDR are 66.1 and 73.1 dB, respectively, resulting in a figure of merit of 27.6 fJ/conversion-step.