An extended ECC‐based anonymity‐preserving 3‐factor remote authentication scheme usable in TMIS

A telecare medicine information system (TMIS) helps in providing an efficient communication platform to patients from home to consult doctors at a clinical center. In TMIS, the patient's confidentiality, security, and mutual authentication are very crucial; so remote authentication plays a vital role for verifying the legitimacy of patients. Recently, Amin and Biswas have devised a remote authentication protocol for TMIS, claiming it to be secured from various malicious vulnerabilities. We examine this protocol and find that it is not able to withstand many attacks that include off‐line and online password‐guessing, identity‐guessing, user impersonation, privileged insider, and known session key temporary information attacks. We propose a 3‐factor–based authentication protocol for TMIS by overcoming these security shortcomings. We present its security verification in formal and informal ways, which assert its resistivity against various security threats. We use the Burrows‐Abadi‐Needham logic for validating it, and with the Automated Validation of Internet Security Protocols and Applications tool, it is simulated. Further, the performance evaluation and the security functionalities justify high degree of security with efficient complexity.     

Intrusion detection in RFID system using computational intelligence approach for underground mines

The radio frequency identification technology (RFID) is commonly used for object tracking and monitoring. In this paper, we discuss a model for intrusion detection system based on RFID to identify the abnormal behavior of underground mines' toxic gases. This model consists of various types of sensor nodes that are integrated with RFID tag, which are deployed in the underground mines by using Zigbee protocol. It consists of coordinators, routers, and sensor nodes, according to different capabilities and the probabilities of intrusive activities that occur in underground mines. It can detect the real‐time abnormal behavior of the toxic gases viz. methane, carbon monoxide, carbon dioxide, hydrogen sulfide, and nitrogen dioxide gases, using artificial neural network middleware techniques. It increases the detection accuracy and reduces the false alarm rate, using multilayer perceptron, radial basis function network, and probabilistic and general regression neural network (PNN/GRNN) techniques. The simulations are performed on the toxic gas dataset, which has been generated in a real‐time scenario by using different gas sensors. The real‐time dataset contains intrusive and nonintrusive values of methane, carbon monoxide, carbon dioxide, hydrogen sulfide, and nitrogen dioxide gases. Experimentally, the PNN/GRNN provides higher detection accuracy as 90.153% for carbon monoxide, 86.713% for carbon dioxide, 93.752% for hydrogen sulfide, and 75.472% for nitrogen dioxide. The PNN/GRNN also provides low false alarm rate as 9.85% for carbon monoxide, 13.29% for carbon dioxide, 6.24% for hydrogen sulfide, and 24.53% for nitrogen dioxide compared with the multilayer perceptron and radial basis function networks.     

Insider attack mitigation in a smart metering infrastructure using reputation score and blockchain technology

International Journal of Information Security - The increasing use of smart metering infrastructure invites security threats through trusted insiders in spite of the devices’ authentication...     

Remote sensing of alpine treeline ecotone dynamics and phenology in Arunachal Pradesh Himalaya

Global warming is inducing the elevational alpine treeline ecotone (ATE) to migrate to higher elevations in the Himalaya. Prior research on ATE dynamics has been primarily based on field inventory and studied at the community level. The potential of using remote sensing and geographic information system for the delineation of the treeline ecotone has been explored. In this study, we used satellite-derived Normalized Difference Vegetation Index (NDVI) data from Landsat-1/2 Multispectral Scanner (MSS), Resourcesat-1/2 Linear Imaging Self Scanning Sensor (LISS-III), and National Oceanographic and Atmospheric Administration-Advanced Very High-Resolution Radiometer (NOAA-AVHRR) to investigate long-term ATE dynamics. Satellite remote sensing of treeline in Arunachal Pradesh Himalaya revealed an upward shift over the past four decades. The ATE has shifted c. 452 m ± 74 m upward in vertical dimension at a rate c. 113 m decade^?1. Furthermore, the land surface phenology along ATE and forest area has changed significantly over the past 33 years. The significant positive trend in length of the growing season (LOS; p < 0.05) and delay in the end of the growing season (EOS) was observed. The start of the growing season (SOS) had a negative tendency with non-significant linear trend. The treeline upward shift and significant lengthening of the growing season at ATE and forest area indicate changing climatic patterns and processes.     

Novel quantum inspired binary neural network algorithm

Sādhanā - In this paper, a quantum based binary neural network algorithm is proposed, named as novel quantum binary neural network algorithm (NQ-BNN). It forms a neural network structure...     

(Two-Dimensional)<Superscript>2</Superscript> whitening reconstruction for newborn recognition

Recently, various feature extraction techniques and its variations have been proposed for computer vision. However, most of these techniques are sensitive to the images acquired in uncontrolled environment. The illumination, expression and occlusion for face images result in random error entries in the 2days matrix representing the face. Techniques such as Principal Component Analysis (PCA) do not handle these entries explicitly. This paper proposes a (Two-dimensional)² whitening reconstruction (T²WR) pre-processing step to be coupled with the PCA algorithm. This combined method would process illumination & expression variations better than standalone PCA. This technique has been compared with state-of-the-art Two-dimensional whitening reconstruction (TWR) pre-processing method. The final results clearly indicate the reason for better performance of T²WR over TWR. The histograms plotted for both these algorithms show that T²WR makes for a smoother frequency distribution than TWR. The proposed method indicated increased recognition rate and accuracy with increasing number of training images; up to 93.82 % for 2 images, 94.76 % for 4 images and 97.42 % for 6 training images.     

Differential scanning calorimetry studies on coal. 2. Hydrogenation of coals

Results of exothermic heats involved during hydrogenation of twenty U.S. raw coals of varying rank at 5 · 6 MPa (gauge) and temperatures up to 570 °C are reported. The heat evolved during hydrogenation up to 570 °C decreases with increase in coal rank. A part of the total heat released during hydrogenation of coals appears to be due to the exothermic reaction between H₂ and surface carbon-oxygen complexes removed during the reaction. The transition temperature, that is the temperature corresponding to the onset of exotherms, is markedly dependent on coal rank. A sharp increase in the transition temperature occurs for coals having a carbon content, on a dry-ash-free basis, in the 75–80% range. Demineralization of coals lower in rank than HVA bituminous decreases the heat of hydrogenation; in the case of higher-rank coals, exothermic heats increase upon demineralization. The presence of pyrite has a beneficial catalytic effect on coal hydrogenation.     

Coupled Sharp-interface and Density-dependent Model for Simultaneous Optimization of Production Well Locations and Pumping in Coastal Aquifer

The main management challenge in coastal aquifers is to prevent saltwater intrusion, ensuring ample freshwater supply. Saltwater intrusion happens due to unregulated pumping from production wells. Therefore, it is essential to have an effective management policy, which ensures the requisite amount of freshwater to be withdrawn from coastal aquifers without causing saltwater intrusion. A methodology for optimizing production well locations and maximizing pumping from production wells is presented to achieve these conflicting objectives. The location of production wells directly affects the amount of freshwater pumped out of the coastal aquifer. Simultaneous optimization of production well locations and pumping from the same is achieved by linking mathematical simulation models with the optimization algorithm. A new methodology using coupled sharp-interface and density-dependent simulation models is developed to find optimal well locations and optimize the amount of freshwater pumped from the coastal aquifer. The performance of the developed methodology is evaluated for saltwater intrusion in the coastal city of Puri, India. The performance evaluation results show the developed methodology's applicability for managing saltwater intrusion while maximizing freshwater pumping in coastal aquifers under constraints of well location.

     

Optimal Control of Nonlinear Inverted Pendulum System Using PID Controller and LQR: Performance Analysis Without and With Disturbance Input

Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions.The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.     

Stochastic manufacturing system with process deterioration and machine breakdown

This paper presents a realistic manufacturing inventory model with process deterioration and machine breakdown. In economic manufacturing quantity model, process usually starts with ‘in-control’ state and produces items of good quality. After some random point of time, process may deteriorate and shift to ‘out-of-control’ state due to occurrence of assignable cause. From that point, process produces some percentage of non-conforming items. Further process deterioration after machine shift may result in machine breakdown at any random time during the production period. If machine breakdown occurs during the production period, then corrective (emergency) repair is performed immediately otherwise preventive (regular) repair is performed at the end of production period. The proposed model is formulated assuming that the time required for production facility shifting from ‘in-control’ state to ‘out-of-control’ state, time when machine breaks down, corrective and preventive repairing time and demand of items follows probability distribution. We have derived analytically the optimal production time which minimises the total expected production cost annually for machine breakdown and no machine breakdown cases. The solution procedure is illustrated with the help of numerical examples for different probability distributions. Sensitivity of the optimal solution with respect to different parameters are also analysed.