A Study of Address Shortage in a Tree Based ZigBee Network for Mobile Health Applications

There are increasing demands for mobile health applications. This paper reports the development of a mobile health profile which dedicates to mobile applications. The mobile health profile is developed in association with the ZigBee Health Care profile and the IEEE 11073 standard which is normally applied to non-mobile applications. Since mobile sensors have to be carried by patients, the mobile health profile must facilitate mobility. In this investigation, a ZigBee fixed-mobile network (ZFMN) is defined and developed to supplement the ZigBee Health Care Profile for patient monitoring. The mobility study of ZigBee is performed using a random waypoint OPNET simulation model. In a ZFMN, the critical issue of address shortage is identified and discussed. It is analyzed that the problematic address shortage in a ZFMN may generate a huge amount of orphaned end devices and thus the packet drop percentage may potentially rise to 70%, rendering the network unable to function properly. Without introducing additional governing schemes, it is evaluated that the communication of the entire ZigBee network may paralyze. Further vigorous test are performed (by OPNET) on the communication capability of ZFMN when devices are randomly moving and sending data in 1s. It is vital to point out that under the adverse condition of address shortage, the performance of a ZFMN is still encouraging as long as the packet drop percentage has been kept below 3% before running out of address. The conclusion drawn in this analysis is that the packet drop percentage should be kept below 3% to provide a satisfactory QoS for an effective mobile health application using ZFMN such as patient monitoring. Such finding is also important for other future mobile application design of ZigBee. The address shortage issue is left as an open problem that needs attention for a resolution.

Passive Localization Using Time Difference of Arrival and Frequency Difference of ArrivalWC

In order to improve the accuracy and engineering feasibility of four-Satellite localization system, the frequency difference measurement is introduced to the four-Satellite TDOA (Time Difference of Arrival) localization algorithm. The TDOA/FDOA (Frequency Difference of Arrival) localization algorithm is used to optimize the GDOP (geometric dilution of precision) of four-Satellite localization. The simulation results show that the absolute position measurement accuracy has little influence on TDOA/FDOA localization accuracy as compared with TDOA localization. Under the same conditions, TDOA/FDOA localization has better accuracy and its GDOP shows more uniform distribution in diamond configuration case. The localization accuracy of four-Satellite TDOA/FDOA is better than the localization accuracy of four-Satellite TDOA.

Performance Evaluation of Intelligent Adaptive Traffic Control Systems: A Case Study

Mashhad, the second largest city in Iran, like many other big cities, is faced with increasing traffic congestion owing to rapidly increasing population and annual pilgrimage. In recent years, Mashhad traffic and transportation authorities have been challenged with how to manage the increasing congestion with limited budgets for major roadway construction projects. Mashhad has recognized the need to improve the existing system capacity to get the most out of their cur- rent transportation system infrastructures. Since most of the delay times occur at signalized intersections, using an intelligent control system with proper capabilities to overcome the growing traffic requirements is recommended. Following comprehensive studies carried out with the aim of developing the Mashhad traffic control center, the SCATS adaptive traffic control system was introduced as the selected intelligent control system for integrating signalized intersections. The first intersection was equipped with this system in 2005. This paper describes the results of a field evaluation in which fixed actuated-coordinated signal timings are compared with those dynamically computed by SCATS. The ef- fects of this system on optimizing fuel consumption as well as reducing air pollutants are fully discussed. It is found that SCATS consistently reduced travel times and the average delay per stopped or approaching vehicle. The positive impact of adaptive traffic control systems on fuel consumption and air pollution are also highlighted.

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in farming.In Pakistan,several kinds of fruits are produced and exported the other countries.Citrus is an important fruit,and its production in Pakistan is higher than the other fruits.However,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this Fruit.The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure.In the agriculture sector,deep learning showing significant success in the last five years.This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification.In the proposed framework,the augmentation technique is applied initially by creating more training data from existing samples.They were then modifying the two pre-trained models named Resnet18 and Inception V3.The modified models are trained using an augmented dataset through transfer learning.Features are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few more.The experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus Fruits.On these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,respectively.The proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

Smart Grid: Future of Electrical Transmission and Distribution

For century, the need of change in the system of power grid is being felt and being considered as the most important change that we need in the modern era electrical system but before moving towards a new change the things in past should be kept in mind so that there are better chances of great and beneficial change. The purpose of this research is to investigate the changes need to be incorporated in a conventional system to make a system self-sufficient and automated in order to make Electrical Power system more reliable. This paper assesses the current one way power system that is needed to be changed and has tried to provide an overview of the changes that we need in the system. The paper has focused more on the smart grid system and has explained the importance of smart grid system in terms of efficiency, automation and decision making capability in case of faults occurred on primitive grids with the help of comparative studies. The paper also highlighted the results in form of comparison with conventional grids and threw some light on the vision, control and the application of the smart grid system that will provide a two way system to the electrical network of the country and will make the distribution and consumption of energy more efficient also which is going to increase the reliability and accuracy in the system.

Performance Analysis of Sub-Rating for Handoff Calls in HCN

Hierarchical Cellular Networks (HCN) offer more efficient channel utilization and better quality of service (QoS) under the high tele-traffic condition compared to the single-tier system. One of the important measures of QoS in HCN as in any single-tier system is the handoff dropping rate. Although the existing approaches such as guard channel and queuing can reduce forced termination probability, they also result in higher new call blocking probability. The channel sub-rating strategy has found to be an effective technique to reduce the handoff force termination probability while preserving the new call blocking probability in a single-tier system. In this paper, we propose a new call admission control scheme for HCN based on the channel sub-rating. Analytic models based on 1-D Markov process in microcell and 2-D Markov process in macrocell are developed. Experimental results show that our scheme achieves lower blocking and forced termination probabilities compared to the traditional guard channel scheme. The effect of channel sub-rating on the voice quality degradation is also studied. Results demonstrate that we can establish a good balance between the forced termination probability and the voice quality degradation by varying the number of sub-ratable full-rate channels.

Minus Infinity Plus Infinity

Mankind has always been fascinated by nature and has striven to understand its functioning.What lies beyond the stars,what hides inside atoms,how to reach the stars,how to tap the vast energy inside the atoms,what is the correlation between space and time and how to exploit it-these are only a sample of the infinite questions to which we seek answers.In this editorial we explore some of the pressing issues that we are currently trying to address.

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The automated detection process is simpler and takes less time than manual processing.In addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians.We proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this paper.For tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)algorithm.Popular preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation process.Then,PSO-based segmentation is applied.In the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer learning.Using a serial approach,features are extracted from both trained models and fused features for final classification.For classification,a variety of machine learning classifiers are used.Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of 0.99.Finally,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

SWITCHED-CURRENT FILTER DESIGN USING CASCADED SECTIONS

A new analogue sampled-data active device, named as a switched-current operationalamplifier (SIOA), is presented. The use of active circuit elements may simplify drawing the circuitdiagram significantly greatly and may permit easier analysis and synthesis of SI networks. Anumber of all pole and elliptic (second-or third-order) switched-current (SI) filters are derivedfrom the switched capacitor prototypes. These can be used as simple self-contained filters or asfilter sections in the cascaded realizations of a higher order transfer functions. To illustrate theapproach, a fifth-order low-pass filter is designed.

Fast Arithmetics Using Chinese Remaindering

In this paper,some issues concerning the Chinese remaindering representation are discussed.A new converting method is described. An efficient refinement of the division algorithm of Chiu,Davida and Litow is given.

Sparse Representation by Frames with Signal Analysis

The use of frames is analyzed in Compressed Sensing (CS) through proofs and experiments. First, a new generalized Dictionary-Restricted Isometry Property (D-RIP) sparsity bound constant for CS is established. Second, experiments with a tight frame to analyze sparsity and reconstruction quality using several signal and image types are shown. The constant is used in fulfilling the definition of D-RIP. It is proved that k-sparse signals can be reconstructed if by using a concise and transparent argument1. The approach could be extended to obtain other D-RIP bounds (i.e. ). Experiments contrast results of a Gabor tight frame with Total Variation minimization. In cases of practical interest, the use of a Gabor dictionary performs well when achieving a highly sparse representation and poorly when this sparsity is not achieved.

Passwords Management via Split-Key

This paper proposes a scheme for password management by storing password encryptions on a server. The method involves having the encryption key split into a share for the user and one for the server. The user’s share shall be based solely on a selected passphrase. The server’s share shall be generated from the user’s share and the encryption key. The security and trust are achieved by performing both encryption and decryption on the client side. We also address the issue of countering dictionary attack by providing a further enhancement of the scheme.

A critical fault detection analysis & fault time in a UPFC transmission line

This paper discusses a critical study of fault detection and fault time analysis in a Unified Power Flow Controller (UPFC) transmission line. Here the Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) approach are used for processing the faulty current signal to obtain fundamental current signal. The extracted fault current signals from the current transformer are fed to DWT and DFT approach for computing spectral energy (SE). The differential spectral energy (DSE) of phase currents are evaluated by taking the difference of SE obtained at sending and receiving end. The DSE is the key factor for deciding the fault in any of the phase or not. The Daubechy mother wavelet (db4) is used here because of its high accuracy of detection with less processing time. The novelty of the scheme is that it can accurately detect the critical fault variation of the line. Number of simulations are validated at the extreme condition of the line and compared to other conventional existing scheme. Multi-phase fault in double circuit line, CT saturation, UPFC operating condition (series voltage and angle), UPFC location and wind speed variation including wind farm simulation are validated to verify the performance of the scheme. The advantages of the scheme is that it works effectively to detect the fault at any stage of critical condition of the line and fault detection time remains within 20 msec (less than one cycle period). This scheme protects both internal and external zone including parameter variation of the line.

Opposition-based differential evolution for IIR system identification problem

Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems.Success achieved for those evolution-ary optimization techniques are due to simplicity and flexibility of algorithm structures.In this paper,optimal set of filter coefficients are searched by the evolutionary optimiza-tion technique called Opposition-based Differential Evolution(ODE)for solving infinite impulse response(IIR)system identification problem.Opposition-based numbering con-cept is embedded into the primary foundation of Differential Evolution(DE)technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution.The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle.With this competent population,faster convergence speed and better solution quality are achieved.Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation,crossover and selection adopted in the basic DE technique.When tested against standard benchmark examples,for same order and reduced order models,the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed.

LIDAR:learning from imperfect demonstrations with advantage rectification

In actor-critic reinforcement learning(RL)algorithms,function estimation errors are known to cause ineffective random exploration at the beginning of training,and lead to overestimated value estimates and suboptimal policies.In this paper,we address the problem by executing advantage rectification with imperfect demonstrations,thus reducing the function estimation errors.Pretraining with expert demonstrations has been widely adopted to accelerate the learning process of deep reinforcement learning when simulations are expensive to obtain.However,existing methods,such as behavior cloning,often assume the demonstrations contain other information or labels with regard to performances,such as optimal assumption,which is usually incorrect and useless in the real world.In this paper,we explicitly handle imperfect demonstrations within the actor-critic RL frameworks,and propose a new method called learning from imperfect demonstrations with advantage rectification(LIDAR).LIDAR utilizes a rectified loss function to merely learn from selective demonstrations,which is derived from a minimal assumption that the demonstrating policies have better performances than our current policy.LIDAR learns from contradictions caused by estimation errors,and in turn reduces estimation errors.We apply LIDAR to three popular actor-critic algorithms,DDPG,TD3 and SAC,and experiments show that our method can observably reduce the function estimation errors,effectively leverage demonstrations far from the optimal,and outperform state-of-the-art baselines consistently in all the scenarios.