Effect of composition on steady state and transient photoconductivity in isocoordinated In<Subscript>x</Subscript>Sb<Subscript>30−x</Subscript>Se<Subscript>70</Subscript> (0 ≤ x ≤ 25) chalcogenide films

The study of steady state and transient photocurrent measurement provide important information about carrier generation and recombination phenomena in various semiconducting systems for photo-sensor device applications. In the present work, the composition dependent analysis of photocurrents was studied for thermally evaporated Se-rich In_xSb_30?xSe₇₀ films of average thickness 800 nm. The indirect optical gap has been calculated from the transmission and reflection data and the variation of molecular units was studied from the Raman spectroscopy. The initial rise of photocurrent sharply to approach a steady state value during illumination and fast decay to a constant persistent current after stopping the illumination has been observed. The intensity dependence of photocurrent obeys the power law I_Ph?=?F^γ, where the value of exponent tells about the recombination process. The decay of photocurrent has been fitted with stretched exponential function for different compositions and at different light intensities. These results are important for the development of low cost photo absorbers for solar cell applications and visible region responsive photo sensor devices.     

A review of forensic approaches to digital image Steganalysis

Multimedia Tools and Applications - Traditional or Binary Steganalysis brands a digital object such as an image as stego or innocent only but modern day information security requires deeper insight...     

Towards a Light Weight Routing Security in IoT Using Non-cooperative Game Models and Dempster–Shaffer Theory

Wireless Personal Communications - The internet of things (IoT) has become an emerging technology owing to the rapidly increasing number of devices and their connectivity to the internet. Routing...     

Minimum connection count wavelength assignment strategy for WDM optical networks

In this paper, we have proposed one wavelength assignment strategy for optical networks which assigns the wavelength according to minimum connection count. The performance of proposed strategy is compared with the most commonly used strategy among the existing strategies in terms of number of searches to find the wavelength for connection establishment. The searching takes the time and directly affects the connection establishment time. The simulation is done using different network models. The results show that the proposed strategy is much better than existing strategy in terms of number of searches required to find a wavelength for establishing the connection and hence connection establishment time reduces.     

Diffusion induced residual stress in comb-type microaccelerometer structure

This paper presents the effect of residual stresses due to deep boron diffusion on microaccelerometer structure. The microaccelerometer structure was simulated at various residual stresses using finite element method. The residual stress due to the diffusion process was estimated to be 450 MPa by using Raman spectroscopy. After fabricating the microaccelerometer structure, the deflection (~2 μm) of the moving comb fingers due to the stress was found to be in good agreement with the simulated value (2.1 μm). Sensitivity of the accelerometers was measured to be ~50 mV/g for 0–30 g range. In the initial range (0–5 g), the output of the accelerometers are depicting nonlinearity as anticipated from the simulated results.     

Improved universal quantitative steganalysis in spatial domain using ELM ensemble

Quantitative steganalysis seeks to extract the additional information about the hidden message in the covert communications. Most of the quantitative steganalyzers available in the literature target a specific embedding algorithm and generally extract the payload information using structural paradigm. Modern steganalyzers use supervised machine learning to estimate the stego payload using sophisticated feature sets. In this paper, an Ensemble Framework based universal quantitative steganalyzer for digital images is proposed which employs optimised Extreme Learning Machines as the base regressors. The framework exploits inherent diversity of the base regressor and the use of random subspaces of the image features further reduces the prediction error. The proposed ensemble regressor exhibits improved payload predictions when evaluated vis-à-vis the individual base regressor and other state-of-the-art algorithms. The experimental results across different embedding algorithms, image datasets and variedly sized feature sets demonstrate the robustness and wide applicability of the proposed framework.

     

Machine Learning with Dimensionality Reduction for DDoS Attack Detection

With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However, the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing time. In this research work, we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive accuracy. We show that the ‘lite’ model trained on reduced dataset not only saves the computational power, but also improves the predictive performance. We show that dimensionality reduction can improve both effectiveness (recall) and efficiency (precision) of the model as compared to the model trained on ‘full’ dataset.