Showing papers in "Indonesian Journal of Electrical Engineering and Computer Science in 2018"

Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA

Abstract: In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset. The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.

Cyber Threat Intelligence – Issue and Challenges

Abstract: Today threat landscape evolving at the rapid rate with many organization continuously face complex and malicious cyber threats. Cybercriminal equipped by better skill, organized and well-funded than before. Cyber Threat Intelligence (CTI) has become a hot topic and being under consideration for many organization to counter the rise of cyber-attacks. The aim of this paper is to review the existing research related to CTI. Through the literature review process, the most basic question of what CTI is examines by comparing existing definitions to find common ground or disagreements. It is found that both organization and vendors lack a complete understanding of what information is considered to be CTI, hence more research is needed in order to define CTI. This paper also identified current CTI product and services that include threat intelligence data feeds, threat intelligence standards and tools that being used in CTI. There is an effort by specific industry to shared only relevance threat intelligence data feeds such as Financial Services Information Sharing and Analysis Center (FS-ISAC) that collaborate on critical security threats facing by global financial services sector only. While research and development center such as MITRE working in developing a standards format (e.g.; STIX, TAXII, CybOX) for threat intelligence sharing to solve interoperability issue between threat sharing peers. Based on the review for CTI definition, standards and tools, this paper identifies four research challenges in cyber threat intelligence and analyses contemporary work carried out in each. With an organization flooded with voluminous of threat data, the requirement for qualified threat data analyst to fully utilize CTI and turn the data into actionable intelligence become more important than ever. The data quality is not a new issue but with the growing adoption of CTI, further research in this area is needed.

Ensemble Method for Indonesian Twitter Hate Speech Detection

Abstract: Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naive Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.

Internet of Things based Smart Environmental Monitoring for Mushroom Cultivation

Abstract: Environmental condition is a significant factor that needs to be controlled in mushroom production. Mushrooms are unable to grow if the temperature is higher than 33°C or lower than 25°C. Thus, this work focuses on developing an automatic environmental control system to provide optimum condition to mushroom production house. Environmental factors considered in the system are temperature, humidity and carbon dioxide. For this, DHT11 temperature humidity sensor and MQ135 CO2 sensor are connected to the ESP8266 Wi-Fi module to become IoT (Internet of Things) sensors that send big amount of data to the internet for monitoring and assessment. This enable users to monitor the environmental condition anywhere whenever accessing the internet. Based on the analysis of the data, the system will automatically on and off the irrigation system to put the temperature at an optimum level.

Random Forest Approach for Sentiment Analysis in Indonesian Language

Abstract: Sentiment analysis become very useful since the rise of social media and online review website and, thus, the requirement of analyzing their sentiment in an effective and efficient way. We can consider sentiment analysis as text classification problem with sentiment as its categories. In this study, we explore the use of Random Forest for sentiment classification in Indonesian language. We also explore the use of bag of words (BOW) features with some term weighting methods variation such as Binary TF, Raw TF, Logarithmic TF and TF.IDF. The experiment result showed that sentiment analysis system using random forest give good performance with average OOB score 0.829. The result also depicted that all of the four term weighting method has competitive result. Since the score difference is not very significant, we can say that the term weighting method variation in study has no remarkable effect for sentiment analysis using Random Forest.

Evaluation of CNN, Alexnet and GoogleNet for Fruit Recognition

Abstract: Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also leads to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available dataset. The experimental results indicate that all these techniques produce excellent recognition accuracy, but basic CNN achieves the fastest recognition result compared with Alexnet and Googlenet.

Optimal Grid-Connected PV System for a Campus Microgrid

Abstract: This paper discusses on the implementation of a grid-connected PV system for university campus in Malaysia. The primary goal of this study is to develop a grid-connected microgrid comprises of Photovoltaic (PV) and a battery storage system to meet the campus load demand and minimize grid dependency. The microgrid modeled and simulated in Hybrid Optimization Model for Electrical Renewable (HOMER) software. Actual load profile and renewable resources were used as an input parameter for the hybrid system. The campus selected is Universiti Kuala Lumpur, British Malaysian Institute as it represents typical load profile for a small campus. Therefore, the results can be used to represent hybrid system development for other small campuses in Malaysia as well. Firstly, optimal sizing of renewable energy (RE) were simulated with respect to total Net Present Cost (NPC) and Cost of Energy (COE). Then, sensitivity analysis conducted to determine the system performance based on changes of load growth, and renewable resources. The results demonstrate optimal HRES combinations for the campus microgrid comprises of 50 kWp of PV generations with 50 kW inverter. However, inclusion of 576 kWh battery storage system will increase the NPC but has higher RE penetration.

Leaf Recognition using Texture Features for Herbal Plant Identification

Abstract: This research investigates the application of texture features for leaf recognition for herbal plant identification Malaysia is rich with herbal plants but not many people can identify them and know about their uses Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary Leaf image is chosen for plant recognition since it is available and visible all the time Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier A new leaf dataset has been constructed from ten different herb plants Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF

Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting

Abstract: Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics

Abstract: Recently Big Data has become one of the important new factors in the business field. This needs to have strategies to manage large volumes of structured, unstructured and semi-structured data. It’s challenging to analyze such large scale of data to extract data meaning and handling uncertain outcomes. Almost all big data sets are dirty, i.e. the set may contain inaccuracies, missing data, miscoding and other issues that influence the strength of big data analytics. One of the biggest challenges in big data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics and unpredictable conclusions. Data cleaning is an essential part of managing and analyzing data. In this survey paper, data quality troubles which may occur in big data processing to understand clearly why an organization requires data cleaning are examined, followed by data quality criteria (dimensions used to indicate data quality). Then, cleaning tools available in market are summarized. Also challenges faced in cleaning big data due to nature of data are discussed. Machine learning algorithms can be used to analyze data and make predictions and finally clean data automatically.

Internet of Things based Wireless Plant Sensor for Smart Farming

Abstract: About 10% of the world’s workforce is directly dependent on agriculture for income and about 99% of food consumed by humans comes from farming. Agriculture is highly climate dependent and with global warming and rapidly changing weather it has become necessary to closely monitor the environment of growing crops for maximizing output as well as increasing food security while minimizing resource usage. In this study, we developed a low cost system which will monitor the temperature, humidity, light intensity and soil moisture of crops and send it to an online server for storage and analysis, based on this data the system can control actuators to control the growth parameters. The three tier system architecture consists of sensors and actuators on the lower level followed by an 8-bit AVR microcontroller which is used for data acquisition and processing topped by an ESP8266 Wi-Fi module which communicates with the internet server. The system uses relay to control actuators such as pumps to irrigate the fields; online weather data is used to optimize the irrigation cycles. The prototyped system was subject to several tests, the experimental results express the systems reliability and accuracy which accentuate its feasibility in real-world applications.

VANETs and Internet of Things (IoT): A Discussion

Abstract: Recent advancement of wireless technology and Internet of Things (IoT) have brought a significant development in Vehicular Ad hoc Networks (VANETs). VANET and IoT are the key elements in the current Intelligent Transport System (ITS). Various research on VANET and IoT shows that both have substantial effects in smart transportation system. This paper aims to discuss and illustrate the main challenges and drawbacks; the routing protocols, security and privacy experienced by VANETs respectively. This paper also would like to address the importance of IoT based on VANET in traffic control management system to cope up with the new wireless technology era. Section I of this paper provides a brief explanation on VANETs and IoT, section II discusses the main challenges suffered by VANETs and IoT, section III covers on the existing applications of VANETs and IoT and section IV conclude the views.

Speech Emotion Recognition Using Deep Feedforward Neural Network

Abstract: Speech emotion recognition (SER) is currently a research hotspot due to its challenging nature but bountiful future prospects. The objective of this research is to utilize Deep Neural Networks (DNNs) to recognize human speech emotion. First, the chosen speech feature Mel-frequency cepstral coefficient (MFCC) were extracted from raw audio data. Second, the speech features extracted were fed into the DNN to train the network. The trained network was then tested onto a set of labelled emotion speech audio and the recognition rate was evaluated. Based on the accuracy rate the MFCC, number of neurons and layers are adjusted for optimization. Moreover, a custom-made database is introduced and validated using the network optimized. The optimum configuration for SER is 13 MFCC, 12 neurons and 2 layers for 3 emotions and 25 MFCC, 21 neurons and 4 layers for 4 emotions, achieving a total recognition rate of 96.3% for 3 emotions and 97.1% for 4 emotions. Speech emotion recognition (SER) is currently a research hotspot due to its challenging nature but bountiful future prospects. The objective of this research is to utilize Deep Neural Networks (DNNs) to recognize human speech emotion. First, the chosen speech feature Mel-frequency cepstral coefficient (MFCC) were extracted from raw audio data. Second, the speech features extracted were fed into the DNN to train the network. The trained network was then tested onto a set of labelled emotion speech audio and the recognition rate was evaluated. Based on the accuracy rate the MFCC, number of neurons and layers are adjusted for optimization. Moreover, a custom-made database is introduced and validated using the network optimized. The optimum configuration for SER is 13 MFCC, 12 neurons and 2 layers for 3 emotions and 25 MFCC, 21 neurons and 4 layers for 4 emotions, achieving a total recognition rate of 96.3% for 3 emotions and 97.1% for 4 emotions.

Implementation of Monitoring System for Air Quality using Raspberry PI: Experimental Study

Abstract: Because of rising dependency on fossil fuels, and rising amounts of toxic gases in the environment, it found that people are in need of a way to ensure the safety specifically those that live in cities. An approach is suggested in this paper, that is economical yet affords good detection, and can give accurate readings that can be analyzed and manipulated, and can even provide warnings through sending emails. These requirements are found in the Raspberry Pi when it hooked up to the sensors. This paper was focused on few dangerous gases such as Carbon Monoxide (CO), Nitrogen Dioxide (NO2) and other gases. The results in this paper showed that some gases, specifically CO, may be a problem in Kuwait as it is always slightly below the warning level. The success with the Raspberry Pi and the results were encouraging to open the way for much improvement in the future.

Development of English Handwritten Recognition Using Deep Neural Network

Abstract: Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and EMNIST, were selected to provide dataset for training and testing phase of DNN. Altogether, there are 10 digits [0-9] and 52 letters [a-z, A-Z]. The proposed DNN used stacked two autoencoder layers and one softmax layer. Recognition accuracy for English digits and letters is 97.7% and 88.8%, respectively. Performance comparison with other structure of neural networks revealed that the weighted average recognition rate for patternnet, feedforwardnet, and proposed DNN were 80.3%, 68.3%, and 90.4%, respectively. It shows that our proposed system is able to recognize handwritten English digits and letters with high accuracy.

Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm

Abstract: Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.

A survey on MANETs: architecture, evolution, applications, security issues and solutions

Abstract: Mobile ad hoc networks or MANETs, also referred to as mobile mesh networks at times, are self-configuring networks of mobile devices that are joined using wireless channels. These represent convoluted distributed systems comprising of wireless mobile nodes which are free to move and self-organise dynamically into temporary and arbitrary, ad hoc topologies. This makes it possible for devices as well as people to internetwork seamlessly in such regions that have no communication infrastructure in place. Conventionally, the single communication networking application following the ad hoc concept had been tactical networks. Lately, new technologies have been introduced such as IEEE 802.11, Hyperlan and Bluetooth that are assisting in the deployment of commercial MANETs external to the military realm. Such topical evolutions infuse a new and rising interest in MANET research and development. This paper provides an overview of the dynamic domain of MANETs. It begins with the discussion on the evolution of MANETs followed by its significance in various fields. Besides, the MANETs have been analysed from the security perspective, particularly the work performed in the node misbehaviour paradigm has been elaborated.

Celebrity Face Recognition using Deep Learning

Abstract: Face recognition is one of the well studied problems by researchers in computer visions. Among the challenges of this task are the occurrence of different facial expressions like happy or sad, and different views of the images such as front and side views. This paper experiments a publicly available dataset that consists of 200,000 images of celebrity faces. Deep Learning technique is gaining its popularity in computer vision and this paper applies this technique for face recognition problem. One of the techniques under deep learning is Convolutional Neural Network (CNN). There is also pre-trained CNN models that are AlexNet and GoogLeNet, which produce excellent accuracy results. The experimental results indicate that AlexNet is better than basic CNN and GoogLeNet for face recognition.

A Study of Some Iterative Methods for Solving Fuzzy Volterra-Fredholm Integral Equations

Abstract: This paper mainly focuses on the recent advances in the some approximated methods for solving fuzzy Volterra-Fredholm integral equations, namely, Adomian decomposition method, variational iteration method and homotopy analysis method. We converted fuzzy Volterra-Fredholm integral equation to a system of Volterra-Fredholm integral equation in crisp case. The approximated methods using to find the approximate solutions of this system and hence obtain an approximation for the fuzzy solution of the fuzzy Volterra-Fredholm integral equation. To assess the accuracy of each method, algorithms with Mathematica 6 according is used. Also, some numerical examples are included to demonstrate the validity and applicability of the proposed techniques. This paper mainly focuses on the recent advances in the some approximated methods for solvingfuzzy Volterra-Fredholm integral equations, namely, Adomian decomposition method, variational iterationmethod and homotopy analysis method. We converted fuzzy Volterra-Fredholm integral equation to asystem of Volterra-Fredholm integral equation in crisp case. The approximated methods using to find theapproximate solutions of this system and hence obtain an approximation for the fuzzy solution of the fuzzyVolterra-Fredholm integral equation. To assess the accuracy of each method, algorithms with Mathematica 6according is used. Also, some numerical examples are included to demonstrate the validity and applicabilityof the proposed techniques.

Survey On The Role Of IoT In Intelligent Transportation System

Abstract: Precise and appropriate traffic related data allows travellers to choose suitable travelling modes, travelling paths, and departure time, which is crucial for the success of Intelligent Transportation System (ITS). With the growth of vehicles, the rate of pollution and consumption of fuel has increased, it also creates traffic congestions. For the recent years there has been a rapid growth in technology, which can be explored to solve traffic issues. However, depending upon the available technologies each countries ITS research area may be different. The objective of this literature review is to integrate ITS with internet of things and it also discusses the prospect of clustering, controller system, location identification and resource privacy in ITS.

Modified AES for Text and Image Encryption

Abstract: Advanced Encryption Standard (AES) is one of the most frequently used encryption algorithms. In the study, the Advanced Encryption Standard is modified to address its high computational requirement due to the complex mathematical operations in MixColumns Transformation making the encryption process slow. The modified AES used Bit Permutation to replace the MixColumns Transformation in AES since bit permutation is easy to implement and it does not have any complex mathematical computation. Results of the study show that the modified AES algorithm exhibited increased efficiency due to the faster encryption time and reduced CPU usage. The modified AES algorithm also yielded higher avalanche effect which improved the performance of the algorithm.

Classification of leaf disease from image processing technique

Abstract: Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose.

Analysis Review on Public Key Cryptography Algorithms

Abstract: This paper presents several Public Key Cryptography (PKC) algorithms based on the perspective of researchers’ effort since it was invented in the last four decades. The categories of the algorithms had been analyzed which are Discrete Logarithm, Integer Factorization, Coding Theory, Elliptic Curve, Lattices, Digital Signature and Hybrid algorithms. This paper reviewed the previous schemes in different PKC algorithms. The aim of this paper is to present the comparative trends of PKC algorithms based on number of research for each algorithm in last four decades, the roadmap of PKC algorithms since they were invented and the most chosen algorithms among previous researchers. Finally, the strength and drawback of proposed schemes and algorithms also presented in this paper.

A Survey of Video Based Action Recognition in Sports

Abstract: Sport performance analysis which is crucial in sport practice is used to improve the performance of athletes during the games. Many studies and investigation have been done in detecting different movements of player for notational analysis using either sensor based or video based modality. Recently, vision based modality has become the research interest due to the vast development of video transmission online. There are tremendous experimental studies have been done using vision based modality in sport but only a few review study has been done previously. Hence, we provide a review study on the video based technique to recognize sport action toward establishing the automated notational analysis system. The paper will be organized into four parts. Firstly, we provide an overview of the current existing technologies of the video based sports intelligence systems. Secondly, we review the framework of action recognition in all fields before we further discuss the implementation of deep learning in vision based modality for sport actions. Finally, the paper summarizes the further trend and research direction in action recognition for sports using video approach. We believed that this review study would be very beneficial in providing a complete overview on video based action recognition in sports.

Determining Hotspots of Road Accidents using Spatial Analysis

Abstract: Road accidents continuously become a major problem in Malaysia and consequently cause loss of life or property Due to that, many road accident data have been collected by highway concessionaries or build–operate–transfer operating companies in the country meant for coming up with proper counter measures Several analyses can be done on the accumulated data in order to improve road safety In this study the reported road accidents cases in North South Expressway (NSE) from Sungai Petani to Bukit Lanjan during 2011 to 2014 period is analyzed The aim is to determine whether the pattern is clustered at certain area and to identify spatial pattern of hot spots across this longest controlled-access expressway in Malaysia as hotspot represents the location of the road which is considered high risk and the probability of traffic accidents in relation to the level of risk in the surrounding areas As no methodology for identifying hotspot has been agreed globally yet; hence this study helped determining the suitable principles and techniques for determination of the hotspot on Malaysian highways Two spatial analysis techniques were applied, Nearest Neighborhood Hierarchical (NNH) Clustering and Spatial Temporal Clustering, using CrimeStat® and visualizing in ArcGIS™ software to calculate the concentration of the incidents and the results are compared based on their accuracies Results identified several hotspots and showed that they varied in number and locations, depending on their parameter values Further analysis on selected hot spot location showed that Spatial Temporal Clustering (STAC) has a higher accuracy index compared to Nearest Neighbor Hierarchical Clustering (NNH) Several recommendations on counter measures have also been proposed based on the details results

A Smart Monitoring of a Water Quality Detector System

Abstract: The importance to monitor the water quality level is undeniable due to significant impact to human health and ecosystem. The project aims to develop a wireless water quality monitoring system that aids in continuous measurements of water conditions based on pH and turbidity measurements. These two sensors are connected to microprocessor and transmitted to the database by using a Wi-Fi module as a bridge. The developed system was successfully detect both the pH and turbidity values hence updating in IoT platform. Based on the results obtained, the test water sample can be classified to class IIB which is suitable for water recreational used body contact. Overall, the developed system offers fast and easy monitoring of pH and turbidity levels with IoT application for continuous maintenance of clean water. The work is just concern on the physical water parameters hence further extend to chemical parameter for verifying a better result in measuring the WQI value.

Deep Learning for Roman Handwritten Character Recognition

Abstract: The advantage of deep learning is that the analysis and learning of massive amounts of unsupervised data make it a beneficial tool for Big Data analysis. Convolution Neural Network (CNN) is a deep learning method that can be used to classify image, cluster them by similarity, and perform image recognition in the scene. This paper conducts a comparative study between three deep learning models, which are simple-CNN, AlexNet and GoogLeNet for Roman handwritten character recognition using Chars74K dataset. The produced results indicate that GooleNet achieves the best accuracy but it requires a longer time to achieve such result while AlexNet produces less accurate result but at a faster rate.

Chaos Based Image Encryption

Abstract: New method of secure image encryption and decryption scheme based on the chaos is proposed. There are two steps are followed after the preprocessing step in the proposed system namely, Encryption and Decryption. In preprocessing, images are denoised using median filter. Then the original input images will be encrypted by using the chaos mapping algorithm. At last the original images are retrieved back from the encrypted image by using the key that is specified during the encryption process for the decryption of the original images. Then the histogram mapping is done for the encrypted and the decrypted images. The proposed system is tested on well-known images like Lena, Mandrill, Clown and Barbara. The experimental results have demonstrated that the introduced image encryption scheme can achieve high security for practical image encryption.

SEC-TAED based Error Detection and Correction Technique for Data Transmission Systems

Abstract: In the OFDM communication system channel encoder and decoder is the part of the architecture. OFDM channel is mostly affected by Additive White Gaussian Noise (AWGN) in which bit flipping of original information leads to fault transmission in the channel. To overcome this problem by using hamming code for error detection and correction. Hamming codes are more attractive and it easy to process the encoding and decoding with low latency. In general the hamming is perfectly detected and corrects the single bit error. In this paper, design of single Error Correction-Triple Adjacent Error Detection (SEC-TAED) codes with bit placement algorithm is presented with less number of parity bits. In the conventional Double Adjacent Error Detection (DAED) and Hamming (13, 8) SEC-TAED are process the codes and detects the error, but it require more parity bits for performing the operation. The higher number of parity bits causes processing delay. To avoid this problem by proposed the Hamming (12, 8) SEC-TAED code, it require only four parity bits to perform the detection process. Bit-reordered format used in the method increases the probability detection of triple adjacent error. It is more suitable for efficient and high speed communication.

Compact Meander Line Telemetry Antenna for Implantable Pacemaker Applications

Abstract: The demand for health technology is increasing rapidly especially in telemetry applications. These applications generally use implanted antennas to be utilized for data transfer from patients to another reader device. This procedure can make the health care more efficient, since it provides fast diagnosis and treatment to the patient. This work presents a design of telemetry antenna to be used in Pacemaker application in Medical Implant Communication Services (MICS) (401 MHz-406 MHz). By introducing Compact Meander Line Telemetry Antenna (CMLTA), length (Ls) and width (Ws) of substrate have been reduced by 36.84% and 40% respectively. The proposed antenna offers advantages of easy fabrications, low cost and light weight with a 133 MHz bandwidth.