Single-bit adaptive channel equalization for narrowband signals

In this paper, a new design of a single-bit adaptive channel equalization is proposed using sigma delta modulation and a single-bit block Least Mean Square (LMS) algorithm. With correlated narrowband input signals, this model is capable to converge and provide equivalent equalization filter with improvement in the SNR and very low Symbol Error Rate (SER). The input, filter coefficients and output values are all in single-bit and ternary format that results in a reduction in hardware complexity compared to traditional multi-bit channel equalization. Additionally, the technique avoids the need for successive conversion from multi-bit to single bit and back at the receiver and transmitter stages

Dictionary Attacks on Speaker Verification

In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population. The resulting master voice successfully matches a non-trivial fraction of people in an unknown population. Adversarial waveforms obtained with our approach can match on average 69% of females and 38% of males enrolled in the target system at a strict decision threshold calibrated to yield false alarm rate of 1%. By using the attack with a black-box voice cloning system, we obtain master voices that are effective in the most challenging conditions and transferable between speaker encoders. We also show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems

Smart green charging scheme of centralized electric vehicle stations

This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively

Deception and self-awareness

This paper presents a study conducted for the Shades of Grey EPSRC research project (EP/H02302X/1), which aims to develop a suite of interventions for identifying terrorist activities. The study investigated the body movements demonstrated by participants while waiting to be interviewed, in one of two conditions: preparing to lie or preparing to tell the truth. The effect of self-awareness was also investigated, with half of the participants sitting in front of a full length mirror during the waiting period. The other half faced a blank wall. A significant interaction was found for the duration of hand/arm movements between the deception and self-awareness conditions (F=4.335, df=1;76, p<0.05). Without a mirror, participants expecting to lie spent less time moving their hands than those expecting to tell the truth; the opposite was seen in the presence of a mirror. This finding indicates a new research area worth further investigation

Magnetron sputtering technique for analyzing the influence of RF sputtering power on microstructural surface morphology of aluminum thin films deposited on SiO2/Si substrates

In this research, aluminum (Al) thin films were deposited on SiO2/Si substrates using RF magnetron sputtering technique for analyzing the influence of RF sputtering power on microstructural surface morphologies. Different sputtering RF powers (100–400 W) were employed to form Al thin films. The characteristics of deposited Al thin films are investigated using X-ray diffraction pattern (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM) and Fourier-transforms infrared (FTIR) spectroscopy. The X-ray diffraction (XRD) results demonstrate that the deposited films in low sputtering power have amorphous nature. By increasing the sputtering power, crystallization is observed. AFM analysis results show that the RF power of 300 W is the optimum sputtering power to grow the smoothest Al thin films. FTIR results show that the varying RF power affect the chemical structure of the deposited films. The SEM results show that by increasing the sputtering power leads to the formation of isolated texture on the surface of substrate. In conclusion, RF power has a significant impact on the properties of deposited films, particularly crystallization and shape