EMITTER International Journal of Engineering Technology
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160 research outputs found
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Medical Health Record Protection Using Ciphertext-Policy Attribute-Based Encryption and Elliptic Curve Digital Signature Algorithm
No full textInformation on medical record is very sensitive data due to the number of confidential information about a patient's condition. Therefore, a secure and reliable storage mechanism is needed so that the data remains original without any changes during it was stored in the data center. The user must go through an authentication process to ensure that not an attacker and verify to ensure the authenticity and accuracy of the data received. In this research, we proposed a solution to secure medical data using the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Elliptic Curve Digital Signature Algorithm (ECDSA) methods. Our system can secure data centers from illegal access because the uploaded data has patient control over access rights based on attributes that have been embedded during the data encryption process. Encrypted data was added to the digital signature to pass the authentication process before being sent to the data center. The results of our experiments serve efficient system security and secure with low overhead. We compare the proposed system performance with the same CP-ABE method but don’t add user revocation to this system and for our computing times are shorter than the previous time for 0.06 seconds and 0.1 seconds to verify the signature. The total time in the system that we propose requires 0.6 seconds
Enhanced PEGASIS using Dynamic Programming for Data Gathering in Wireless Sensor Network
No full textA number of routing protocol algorithms such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) have been proposed to overcome the problem of energy consumption in Wireless Sensor Network (WSN) technology. PEGASIS is a development of the LEACH protocol, where within PEGASIS all nodes are active during data transfer rounds thus limiting the lifetime of the WSN. This study aims to propose improvements from the previous PEGASIS version by giving the name Enhanced PEGASIS using Dynamic Programming (EPDP). EPDP uses the Dominating Set (DS) concept in selecting a subset of nodes to be activated and using dynamic programming based optimization in forming chains from each node. There are 2 topology nodes that we use, namely random and static. Then for the Base Station (BS), it will also be divided into several scenarios, namely the BS is placed outside the network, in the corner of the network, and in the middle of the network. Whereas to determine the performance between EPDP, PEGASIS and LEACH, an analysis of the number of die nodes, number of alive nodes, and remaining of energy were analyzed. From the experiment result, it was found that the EPDP protocol had better performance compared to the LEACH and PEGASIS protocols in terms of number of die nodes, number of alive nodes, and remaining of energy. Whereas the best BS placement is in the middle of the network and uses static node distribution topologies to save more energy
Design and Analysis of the Voltage Controller for the Non Isolated Boost DC-DC Convertor
No full textIn this paper, a controller has been presented by the root locus method based on the state space average model of the boost switching regulator with all of the converter’s parameters and uncertainties. In this model, the load current is unknown and the inductor, capacitor, diode and active switch are non ideal and have an on-state resistance. Furthermore, an on-state voltage drop has been considered for diode and active switch. By neglecting the load current and assuming the ideal elements the simplified model of the regulator has been caddied out. By these complete and simplified models, a step by step method has been proposed to design a single input single output (SISO), second order controller based on roots locus method. In this regard the controller's electronic circuit has been introduced by operational amplifiers. At the end, by simulation of the complete closed-loop system in MATLAB Simulink environment and comparing its results by the results of the regulator and controller circuits in PLECS, the accuracy of the designed controller performance has been shown
A Full-Bridge Bidirectional DC-DC Converter with Fuzzy Logic Voltage Control for Battery Energy Storage System
No full textRenewable energy sources require an energy storage system because its are fluctuating and electricity producing at certain times, even sometimes not in accordance with the needs of the load. To maintain continuity of electricity, smart battery energy storage system is needed. Therefore, this paper of a full-bridge bidirectional DC-DC Converter (FB-BDC) with Fuzzy Logic Control (FLC) is designed and implemented for battery energy storage application. The FLC has error and delta error of voltage level as input and duty cycle of FB-BDC as output. The FB-BDC is controlled by a microcontroller ARM Cortex-M4F STM32F407VG for voltage mode control. The FB-BDC topology is selected becuase battery storage system needed isolated and need high voltage ratio both for step-up and step-down. The main purpose of FB-BDC to perform bidirectional energy transfer both of DC-Bus and battery. Moreover, FB-BDC controls the DC-Bus voltage according to referenced value. The power flow and voltage on DC-Bus is controlled by FLC with voltage mode control. The experiment result shows the ability of FLCÂ voltage mode control to control FB-BDC on regulate charging voltage with an error 1% and sharing voltage 1.5% form referenced value
Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project
No full textInternet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisecting K-means with the average of SSE is 93 percents better than K-means and bisecting K-means. The usage of big spark data is highly scalable for processing massive data attack
FPGA Based Design of Artificial Neural Processor Used for Wireless Sensor Network
No full textIn this paper, a simulation of artificial intelligent system has been designed for processing the incoming data of sensor units and then presenting proper decision. The Back-propagation Neural Network BPNN has been used as the proposed intelligent system for this work, whereas the BPNN is considered as a trained network in conjunction with an optimization method for changing the weights and biases of the overall network. The main two features of the BPNN are: high speed processing, and producing lowest Mean-Square-Error MSE ( cost function ) in few iterations. The proposed BPNN has used the linear activation functions 'Satlins' and 'Satline' for the hidden and output layer respectively, and has used the training function 'Traingda' ( which is gradient descent with adaptive learning rate) as a powerful learning method. It is worth to mention, that no previous research used these three functions together for such analysis. The MATLAB software package has been used for designing and testing the proposed system. An optimal result has been obtained in this work, where the value of Mean-Square-Error has reached to zero  in 87 epochs, and the real and desired outputs have been fitted. In fact, there is no previous work has reached to this optimal result. The proposed BPNN has been implemented in FPGA, which is fast, and low power tool
An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator
No full textThe traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm
Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation
No full textRiver water pollution is one of the environmental problems that occur in Surabaya. The amount of industrial waste and household waste makes Surabaya River water easily polluted every day, besides that there are also many people who are not aware about the quality of river water in Surabaya. In this paper, we present a new system to classify water quality of river in surabaya. The system involve a semantic visualization of risk-mapping for the river, so that the people of Surabaya are easier to get information about the quality of Surabaya River water. In this paper, we measured the water quality of Surabaya River using Horiba sensor measuring instruments using 5 parameters, namely temperature, PH, DO, Turbidity, TDS. These five parameters are input variables for calculating water quality with the methods applied in this research. We use the Storet Method to determine the quality of Surabaya River water. The results of the Storet Method explained that there were 0.03% of the data on lightly polluted water quality and there were 37.41% of the data being moderately polluted and there were 59.29% of the data heavily polluted. The results of the calculation using the Storet method concluded that the condition of Surabaya River water quality was not good. We also apply the rule of the Storet Method to the Neural Network by using Surabaya River water quality data as learning data and gave performance 70.02% accuracy
Automatic Detection of Wrecked Airplanes from UAV Images
No full textSearching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims
Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors
No full textDiabetes is a very serious chronic. Diabetes can occurs when the pancreas doesn't produce enough insulin (a hormone used to regulate blood sugar), cause glucose in the blood to be high. The purpose of this study is to provide a different approach in dealing with cases of diabetes, that's with data mining techniques mengguanakan logistic regression algorithm to predict and analyze the risk of diabetes that is implemented in the mobile framework. The dataset used for data modeling using logistic regression algorithm was taken from Soewandhie Hospital on August 1 until September 30, 2017. Attributes obtained from the Hospital Laboratory have 11 attribute, with remove 1 attribute that is the medical record number so it becomes 10 attributes. In the data preparation dataset done preprocessing process using replace missing value, normalization, and feature extraction to produce a good accuracy. The result of this research is performance measure with ROC Curve, and also the attribute analysis that influence to diabetes using p-value. From these results it is known that by using modeling logistic regression algorithm and validation test using leave one out obtained accuracy of 94.77%. And for attributes that affect diabetes is 9 attributes, age, hemoglobin, sex, blood sugar pressure, creatin serum, white cell count, urea, total cholesterol, and bmi. And for attributes triglycerides have no effect on diabetes