Emergency management systems using mobile cloud computing: A survey

Among today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource-limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.     

Mechanism of charge recombination and IPCE in ZnO dye‐sensitized solar cells having I−/I and Br−/Br redox couple

The influence of intensity and wavelength variation on the solar cell parameters of two different ZnO‐based liquid state DSSCs named as Cell (A) ZnO/EosinY/LiI and Cell (B) ZnO/EosinY/LiBr was studied. It was found that V_oc and I_sc depend logarithmically and linearly on light flux, respectively, which indicates that light absorption and carrier diffusion do not limit the solar cell efficiency. The data was analyzed to ascertain the charge recombination mechanism between conduction band electrons and the electrolytes. The regeneration of dye due to I⁻/I₃ and Br⁻/Br redox couple was examined by studying the wavelength dependence of IPCE. An estimation of series and shunt resistance is made using two methods: (i) different illumination method (ii) single I–V curve, for the two cells in order to understand the role of the electrolyte in controlling the solar cell parameters. Copyright © 2010 John Wiley & Sons, Ltd.     

Efficient photocatalytic degradation of bisphenol A by green synthesized CuO decorated nickel hexacyanoferrate nanocomposite

Bisphenol A (BPA), a chemical additive in polycarbonate plastics and epoxy resins, is suspected to be an endocrine-disrupter. Extensive use and irregular treating methods have led to frequent detection of BPA in wastewater, raising demand for their removal by efficient nanomaterials-based technique. Nanocomposite CuO@NiHCF was synthesized via green method using Citrus aurantium peel extract. Crystalline structure (particle size <50 nm) of CuO@NiHCF was analysed by microscopic and spectroscopic analysis. This nanocomposite showed 97% degradation of BPA (50 mg L⁻¹) at neutral pH in sunlight. Moreover, improved particle stability (zeta potential: −56.2 mV; 2.0 eV) and high surface area, pore volume (81 m²g⁻¹, 13.9 nm) resulted from synergism of NiHCF (−26.3 mV; 2.4 eV), and CuO (−11.5 mV; 1.9 eV) led to efficient photodegradation of BPA. Degradation of BPA was found photo-adsorptive. Moreover, degradation was carried out by OH radicals based with ring-opening mechanisms by GC–MS. High efficiency and sustainability of CuO@NiHCF were revealed by its multiple reusability (n = 10), leading to a promising and sustainable photocatalyst.     

Nanostructured Spinel Manganates and Their Composites for Electrochemical Energy Conversion and Storage

Spinel manganates (AMn₂O₄; A = Co, Ni, Cu, Zn, and Fe; collectively referred to as AMO) are promising electrode materials for water electrolyzers, pseudocapacitors, and batteries owing to their inherent advantages such as valence variability, high catalytic activity, conductivity, stability, low-cost, and environmental friendliness. Nanostructured materials, with a large surface area and short ion diffusion length, offer great potential for achieving enhanced electrochemical performance. This review summarizes spinel manganates with various nanostructured morphologies and discusses the impact of the structure and composition on the electrochemical performance. The review demonstrates that nanostructured spinel manganates with preferred A-site cation significantly improve the thermodynamics and electrochemical reaction kinetics at solid–liquid and solid–solid interfaces. Notably, faceted, hollow, 1D nanostructured CoMn₂O₄ and its nanocomposites (CoMn/CoMn₂O₄ and NiMn₂O₄/C) exhibit outstanding electrochemical performance. The review also provides an overview of the importance of energy conversion and storage, and the advantages of spinel manganates as electrode materials. Additionally, the review describes feasible methods of synthesizing AMO nanostructures and nanocomposites. The insights provided in this review are expected to contribute to the synthesis of spinel manganates with desired morphologies and compositions, enabling the future development of efficient electrode materials for energy conversion and storage devices.     

Deep Fuzzy SegNet-based lung nodule segmentation and optimized deep learning for lung cancer detection

Pattern Analysis and Applications - Globally, lung cancer has a high fatality rate and is a lethal disease. Since lung cancer affects both men and women, it requires extra consideration when...     

Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate. Hence to improve the precision of classification, Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient. The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets. The validation of testing sets is evaluated using RL that provides feedback to Classifiers. Finally, at the user interface, the signals are played by decoding the signal after being retrieved from the classifier back based on the input query. The results are evaluated in the form of accuracy, recall, precision, f-measure, and error rate, where generative adversarial network attains an increased accuracy rate than other methods: Multi-Layer Perceptron, Recurrent Neural Networks, Deep belief Networks, and Convolutional Neural Networks.     

A dual-band semi-circular patch antenna for WiMAX and WiFi-5/6 applications

A semi-circular patch antenna (SCPA) is designed for WiMAX and WiFi-5/6 communications. The footprint of the proposed SCPA is only 30 × 40 × 1.575 mm³, which is composed of a semi-circular patch and a partial ground plane. The main strength of this work is that the estimated wide dual-frequency span of 2.39–3.75 GHz and 5.39–7.18 GHz are contributed with two sharp resonances at 2.77 and 6.46 GHz, respectively. The proposed SCPA has been practically tested after fabrication. The measured results confirm that the designed antenna achieved the bandwidth necessity for WiMAX 1.5 (2.5–2.69 GHz), WiMAX 2 (3.4–3.6 GHz), WiFi-5 (5.15–5.85 GHz) and WiFi-6 (5.925–7.125 GHz). In the two resonance frequencies of 2.77 and 6.46 GHz, the designed antenna achieved a gain of 2.558 and 4.109 dB, respectively. In addition, the SCPA also manifests good radiation properties and achieves an average efficiency of more than 90%. A professional version of the 3D electromagnetic simulator is utilized to examine the effect of diffident parameters and model the tested antenna.     

Charge Storage Capabilities of Fractal Porous Silicon Obtained Using Simple Metal Assisted Porosification Method

Silicon - Simpler methods for fabricating electrodes for charge storage applications are required because electrode materials have appreciable significance to enhance the electrochemical...     

Adaptive dewarping of severely warped camera-captured document images based on document map generation

International Journal on Document Analysis and Recognition (IJDAR) - Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern...