Emergency Gesture Communication by Patients, Elderly and Differently Abled with Care Takers Using Wearable Data Gloves

The Healthcare monitoring on a clinical base involves many implicit communication between the patient and the care takers. Any misinterpretation leads to adverse effects. A simple wearable system can precisely interpret the implicit communication to the care takers or to an automated support device. Simple and obvious hand movements can be used for the above purpose. The proposed system suggests a novel methodology simpler than the existing sign language interpretations for such implicit communication. The experimental results show a well-distinguished realization of different hand movement activities using a wearable sensor medium and the interpretation results always show significant thresholds.

Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas,manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation.For the large volume of the process,the deep learning-based methods can be better to achieve a high level of classifica-tion,object detection,and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients(HOG)is used to features extracted while using the deep learning technique to classify the ground-level segmentation process’s images.In this paper,we use mobile edge computing to process street images in advance andfilter out pictures that meet our needs,which significantly affect recognition efficiency.To measure the urban streets’cleanliness,our street clean-liness assessment approach provides a multi-level assessment model across differ-ent layers.Besides,with ground-level segmentation using a deep neural network,a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector(SSD)approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage’s accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.

Adaptive Sub-Threshold Voltage Level Control for Voltage Deviate-Domino Circuits

Leakage power and propagation delay are two significant issues found in sub-micron technology-based Complementary Metal-Oxide-Semiconductor(CMOS)-based Very Large-Scale Integration(VLSI)circuit designs.Positive Channel Metal Oxide Semiconductor(PMOS)has been replaced by Negative Channel Metal Oxide Semiconductor(NMOS)in recent years,with low dimen-sion-switching changes in order to shape the mirror of voltage comparator.NMOS is used to reduce stacking leakage as well as total exchange.Domino Logic Cir-cuit is a powerful and versatile digital programmer that gained popularity in recent years.In this study regarding Adaptive Sub Threshold Voltage Level Control Pro-blem,the researchers intend to solve the contention issues,reduce power dissipa-tion,and increase the noise immunity by proposing Adaptive Sub Threshold Voltage Level Control(ASVLC)-based domino circuit.The efficiency and effec-tiveness of the domino circuit are demonstrated through simulation results.The suggested system makes use of high-speed broad fan-gate circuits,occupies mini-mum space,and consumes meagre amount of power.The proposed circuit was validated in Cadence simulation tool at a supply voltage of 1V,frequency of 100 MHz,and an operating temperature of 27°C with 64 input OR gates.As per the simulation results,the suggested Domino Gate reduced the power dissipa-tion by 17.58 percent and improved the noise immunity by 1.21 times in compar-ison with standard domino logic circuits.

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistically accesses the free white areas for different users.Exploiting the free spaces helps to increase the spectrum efficiency.But the existing spectrum sensing techniques such as energy detectors,cyclo-stationary detectors suffer from various problems such as complexity,non-responsive behaviors under low Signal to Noise Ratio(SNR)and computational overhead,which affects the performance of the sensing accuracy.Many algorithms such as Long-Short Term Memory(LSTM),Convolutional Neural Networks(CNN),and Recurrent Neural Networks(RNN)play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks,but still require improvisation in terms of sensing accuracy under dynamic environmental conditions.This paper,we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory(COLSTM),an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity.The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency.Furthermore,the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances.The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds.The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared.Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments.Further,the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.

Smart Grid Communication Under Elliptic Curve Cryptography

Smart Grids(SGs)are introduced as a solution for standard power dis-tribution.The significant capabilities of smart grids help to monitor consumer behaviors and power systems.However,the delay-sensitive network faces numer-ous challenges in which security and privacy gain more attention.Threats to trans-mitted messages,control over smart grid information and user privacy are the major concerns in smart grid security.Providing secure communication between the service provider and the user is the only possible solution for these security issues.So,this research work presents an efficient mutual authentication and key agreement protocol for smart grid communication using elliptic curve crypto-graphy which is robust against security threats.A trust authority module is intro-duced in the security model apart from the user and service provider for authentication.The proposed approach performance is verified based on different security features,communication costs,and computation costs.The comparative analysis of experimental results demonstrates that the proposed authentication model attains better performance than existing state of art of techniques.

Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network.The sentiment classification accuracy is evaluated using various selection methods,especially those that deal with algorithm selection.In this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and non-informative.In order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andfinally produces the results as informative or non-informative.The challenge is to convert non-informative into informative using the concepts of classifiers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine(SVM)and decision trees.The work also addresses simulated annealing along with QER to classify data based on sentiment analysis.As the input volume is very fast,the work also addresses the concept of big data for information retrieval and processing.The result com-parison shows that the QER algorithm proved to be versatile when compared with the result of SVM.This work uses Twitter user comments for evaluating senti-ment analysis.

Study and Analysis of Defect Amplification Index in Technology Variant Business Application Development through Fault Injection Patterns

Software reliability for business applications is becoming a topic of interest in the IT community. An effective method to validate and understand defect behaviour in a software application is Fault Injection. Fault injection involves the deliberate insertion of faults or errors into software in order to determine its response and to study its behaviour. Fault Injection Modeling has demonstrated to be an effective method for study and analysis of defect response, validating fault-tolerant systems, and understanding systems behaviour in the presence of injected faults. The objectives of this study are to measure and analyze defect leakage;Amplification Index (AI) of errors and examine “Domino” effect of defects leaked into subsequent Software Development Life Cycle phases in a business application. The approach endeavour to demonstrate the phasewise impact of leaked defects, through causal analysis and quantitative analysis of defects leakage and amplification index patterns in system built using technology variants (C#, VB 6.0, Java).