Proceeding of the Electrical Engineering Computer Science and Informatics
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Interference Management in Heterogeneous Network With Particle Swarm Optimization
Get PDFIn heterogeneous network, femtocell is deployedinside macrocell coverage. Femtocell and macrocell use thesame frequency as the resource. Thus, if the resources are notproperly allocated, the interference will arise. The higher levelof interference results in decreasing signal quality. Therefore,interference management is needed to increase networkcapacity and system performance. This research implementsparticle swarm optimization (PSO) algorithm to minimizeinterference on the heterogeneous network. Based on thesimulation results, it is shown that PSO algorithm worksefficiently to increase the throughput value of femto userequipment (femto UE) up to 62,2 Mbps by minimizing theinterference
Low-Power And High Performance Of An Optimized FinFET Based 8T SRAM Cell Design
Get PDFThe development of the nanotechnology leadsto the shrinking of the size of the transistors to nanometerregion. However, there are a lot of challenges due to sizescaling of the transistors such as short channel effects (SCEs)and threshold voltage roll-off issues. Fin-Type Field EffectTransistor (FinFET) is another alternative technology tosolve the issues of the conventional MOSFET and increasethe performance of the Static Random Access Memory(SRAM) circuit design. FinFET based SRAMs are faster andmore reliable which are often used as memory cache for highspeed operation. However, 6T SRAM cell suffers from accesstransistor sizing conflict resulting in a trade-off between readand write stability. This paper presents an investigation ofthe stability performance in retention, read and write modeof 22nm FinFET based 8T SRAM cell. The performancecomparison of 22nm FinFET based 6T and 8T SRAMs weremade. The simulation of the SRAM model are carried out inGTS Framework TCAD tool based on 22nm technology. In8T SRAM cell, two n-FinFETs are added to the conventional6T SRAM cell which will be controlled by the Read WordLine (RWL) to isolate the read and write operation path forbetter read stability. FinFET based 8T SRAM cell givesbetter performance in Static Noise Margin (SNM) and powerconsumption than 6T SRAM cells. The simulation resultsaffirms the proposed FinFET based 8T SRAM improvedread static noise margin by 166.67% and power consumptionby 76.13% as compared to the FinFET based 6T SRAM
Technologies, methods, and approaches on detection system of plant pests and diseases
Get PDFThis research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease
Smart Performance Measurement Tool in Measuring The Readiness of Lean Higher Education Institution
Get PDFThe development of autonomy University drives management innovation to increase the alternative sources of income with the purpose of the efficiency improvement and productivity of the institution. One of a management model that leads to increase productivity through cost reduction is Lean service. The implementation of Lean Higher Education Institution (LHEI) requires total involvement of organization maneuver, including social culture, infrastructure, and leadership support. Therefore, the readiness of the institution in welcoming Lean concepts becomes significant. This article tried to develop a prototype of an intelligent performance measurement tool by analyzing the readiness indicators using the Analytical Hierarchy Process (AHP) method. This tool provided the classification of organizational readiness into five performances level. The measurement performed as a Decision Support System (DSS) to recommend University management level in making a decision and correcting action towards the optimal execution of Lean service. As a case study, this prototype system has been tested with Black Box and User Acceptance Test (UAT) in Indonesia Islamic Higher Education Institution. The finding reveals that the prototype system can be used as a performance measurement tool in measuring the readiness of Lean's service in Islamic Higher Education Institution
Securing IoT Network using Lightweight MultiFog (LMF) Blockchain Model
Get PDFSecurity is one of the most important issues in the Internet of Things (IoT). The Mirai botnet case in September 2016 revealed a serious vulnerability in IoT devices. Researchers try to mitigate the issues using several approaches. One of them uses Blockchain for the solution. At first, the integration of the Blockchain on IoT seems promising. However, there are problems in resource consumption and latency. Several solutions emerge to make Blockchain uses low resource consumption i.e., LSB and FogBus. Unfortunately, each solution has its weaknesses. FogBus has a weakness in integrity, whereas LSB has a weakness in its availability when an attack occurs on a broker. We introduce Lightweight Multi-Fog (LMF) Blockchain Model to increase availability in the LSB model. The main idea is increasing the integrity availability by splitting location based on Broadcast Domains while using Fog Computing on each Broadcast Domain. An attack in some Broadcast Domain cannot impact transactions and process in other Broadcast Domain and each Broadcast Domain have its separate transaction and process. LMF enhances the integrity and availability of the Light Blockchain Model. However, it still requires simulations in the future to get a better understanding of LMF performance, resource consumption, and latenc
Deep Learning Approaches for Big Data Analysis
Get PDFGood representations of data eliminate irrelevant variability of the input data, while preserving the information that is useful for the ultimate task. Among the various ways for learning representation is using deep learning methods. Deep feature hierarchies are formed by stacking unsupervised modules on top of each other, forming multiple non-linear transformations to produce better representations. In this talk, we will first show how deep learning is used for bioactivity prediction of chemical compounds. Molecules are represented as several convolutional neural networks to predict their bioactivity. In addition, a new concept of merging multiple convolutional neural networks and an automatic learning features representation for the chemical compounds was proposed using the values within neurons of the last layer of the CNN architecture. We will also show how the concepts of deep learning is adapted into a deep belief network (DBN) to enhance the molecular similarity searching. The DBN achieves feature abstraction by reconstruction weight for each feature and minimizing the reconstruction error over the whole feature set. The DBN is later enhanced using data fusion to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors. Secondly, we will show how we used deep learning for stock market prediction. Here, we developed a Deep Long Short Term Memory Network model that is able to forecast the crude palm oil price movement with combined factors such as other commodities prices, weather and news sentiments and price movement of crude palm oil. We will also show how we combined stock markets price and financial news and deployed the Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), and Word 2 Vector (Word2Vec) to project the stock prices for the following seven days. Finally, we will show how we exploited deep learning method for the opinion mining and later used it to extract the product's aspects from the user textual review for recommendation systems. Specifically, we employ a multichannel convolutional neural network (MCNN) for two different input layers, namely, word embedding layer and Part-of-speech (POS) tag embedding layer. We will show effectiveness of the proposed model in terms of both aspect extraction and rating prediction performance
MAC for Internet of Things (IoT)
Get PDFInternet of Things (IoT) networks are expected to consist of a large number of resource constrained devices that gather data by sensing their environment and communicate dynamically with access points or neighboring devices to communicate these small amount of location specific delay-sensitive data. A IoT MAC protocol must be able to support the high-intensity and short-lived demands of these IoT networks. The basic design questions to be addressed are, one, why endure a high-overhead and large-delay MAC protocol in IoT networks when only a few intermittent packets need to be sent and received? Two, how to ensure energy efficiency even when energy harvesting is available? Three, what kind of access technique should be employed; grant based or grant free? In this talk, we take a look at how existing wireless MAC protocols are being adapted to cater to the specific needs of IoT networks which is imperative to address the basic design questions. Recent research proposals for IoT MAC protocols that endeavor to address the needs shall also be examined for their efficacy and promise
Determination of Appropriate Overhead Line Insulator in Sumatra due to Contamination Severity
Get PDFInsulator is one of the important equipment to support electrical power delivery which flow through the transmission line. Considering its very important role, the selection of insulators must be certainly based on deep analysis so that the insulator we choose works properly. There are several standards that can be used in selecting isolators, but in this paper the standards that will be used for case study analysis are IEC and IEEE standards. Case studies that will be used for the selection of insulators are for Sumatra that located in Indonesia which is a tropical country and certainly has special environmental characteristics that can influence the selection parameters of an insulator. There are several parameters that are commonly used in selecting overhead isolators those are power frequency voltage, environmental condition (contamination), switching over voltage, and lightning over voltage. Using environmental condition, it is found that the pollution category of Sumatra area is heavy, which influence the selection of insulation material
Appropriate Sets of Criteria for Innovation Adoption of IS Security in Organizations
Get PDFDetermining sets of criteria and alternatives becoming main priorities is essential to guarantee the success of innovation adoption of Information System (IS) security. The goal of this research was to select and determine important entities as representation of each criterion for managers in making decisions of innovation adoption of IS security. This research applied Technology-Organization-Environment (TOE) Framework, and Human-Organization-Technology-Fit (HOT-Fit) Model to map relative importance variables of criteria and alternatives. AHP Approach was applied for computation simulation to determine priorities of criteria and alternatives. Results show that a principal criterion is manpower of organizations. The eigen factor score is 4.398. Moreover, alternatives covering complexity, financial resources, intensity of competition, and CIO innovativeness have these respective eigen factors scores: 4.326, 9.307, 4.376, and 4.545
Self-Efficacy a Critical Factor of Information System: An Investigation using DeLone McLean
Get PDFThis paper examines what influences Attitude towar usage (ATU) as the expectation of the application of Employee Information System (EIS) with high success rate, this research is observed through cognitive value using approach theory of information system success model DeLone and McLean Model (DMM) and affective value using the Human Resource quality theory (HRQ) and the Self-Efficacy (SE) model. The overall data was obtained by providing questionnaires to employees at the Higher Education and using WarpPLS and SEM as a method of analysis. This study found that, because the EIS was positioned as a compulsory system, our study showed that only Self-Efficacy would affect Attitudes toward the Use of EIS. The quality of human resources, as other Affective factors, has no effect on Attitudes towards usage of EIS