The Experts below are selected from a list of 55938 Experts worldwide ranked by ideXlab platform
Kallepalli Sravankumar - One of the best experts on this subject based on the ideXlab platform.
-
A Novel Micro grid Operation for a Low Voltage Network with Renewable Energy Source
International Journal of Research, 2018Co-Authors: Kamaraju, S. Venkatesh, P. Jithender, Kallepalli SravankumarAbstract:This paper present brief description of a Microgrid development project currently in progress. Initially, then Malaysian renewable energy policy is briefly highlighted. The objective of having Microgrid is discussed. The main components of Microgrid and the technique on how the Microgrid central controller was developed is also explained.The result from simulation study of Microgrid system in the project is presented to demonstrate the workability of the concept. The modeling and simulation is performed in DIgSILENT and MATLAB/SIMPOWERSYSTEM software.The simulation shows quite promising results. The Microgrid controller managed to control the voltage and frequency in both grid connected and islanding mode as well as maintained them within mandatory Operational limit of respective Operation Code in Malaysia. An active power loss is also significantly reduced.
Bernard Dieny - One of the best experts on this subject based on the ideXlab platform.
-
A novel architecture of non-volatile magnetic arithmetic logic unit using magnetic tunnel junctions
Journal of Physics D: Applied Physics, 2014Co-Authors: Wei Guo, Guillaume Prenat, Bernard DienyAbstract:Complementary metal–oxide–semiconductor (CMOS) technology is facing increasingly difficult obstacles such as power consumption and interconnection delay. Novel hybrid technologies and architectures are being investigated with the aim to circumvent some of these limits. In particular, hybrid CMOS/magnetic technology based on magnetic tunnel junctions (MTJs) is considered as a very promising approach thanks to the full compatibility of MTJs with CMOS technology. By tightly merging the conventional electronics with magnetism, both logic and memory functions can be implemented in the same device. As a result, non-volatility is directly brought into logic circuits, yielding significant improvement of device performances and new functionalities as well. We have conceived an innovative methodology to construct non-volatile magnetic arithmetic logic units (MALUs) combining spin-transfer torque MTJs with MOS transistors. The present 4-bit MALU utilizes 4 MTJ pairs to store its Operation Code (opCode). Its Operations and performances have been confirmed and evaluated through electrical simulations.
Nadra Guizani - One of the best experts on this subject based on the ideXlab platform.
-
Detecting Malware on X86-Based IoT Devices in Autonomous Driving
IEEE Wireless Communications, 2019Co-Authors: Xiaosong Zhang, Xiaojiang Du, Teng Hu, Nadra GuizaniAbstract:X86-based software runs on some mainstream autonomous driving systems to perform intelligent Operations and help to significantly improve driving safety. However, vulnerabilities of software in autonomous driving can lead to vehicle components and systems being attacked, which ultimately affects the work of the autonomous vehicle. Although many scholars have detected malware on X86-based Internet of Things (IoT) devices through static or dynamic analysis, these methods cannot be directly ported to X86-based IoT devices in autonomous driving because of hardware and software capabilities and real-time requirements. In this work, we propose the detection of malware on X86-based IoT devices in an autonomous driving approach combining fusion features from static analysis and machine learning to solve problems of resource overhead for dynamic analysis and low accuracy of the static analysis. First, a feature extraction model based on the level of Operation Code is designed. Then fusion features at three different levels are extracted through unpacking programs. Finally, a detection model based on Extreme Gradient Boosting (XGBoost) is used to discover malware on X86-based IoT devices in autonomous driving. On this basis, a malware detection approach based on fusion features is demonstrated. After that, we compare our approach with other identification approaches on a dataset consisting of 4169 samples, which includes 2379 malware and 1790 benign programs. Experimental results show that fusion features can significantly increase the recognition rate. Moreover, XGBoost has a higher recognition accuracy than other mainstream classification algorithms.
Kamaraju - One of the best experts on this subject based on the ideXlab platform.
-
A Novel Micro grid Operation for a Low Voltage Network with Renewable Energy Source
International Journal of Research, 2018Co-Authors: Kamaraju, S. Venkatesh, P. Jithender, Kallepalli SravankumarAbstract:This paper present brief description of a Microgrid development project currently in progress. Initially, then Malaysian renewable energy policy is briefly highlighted. The objective of having Microgrid is discussed. The main components of Microgrid and the technique on how the Microgrid central controller was developed is also explained.The result from simulation study of Microgrid system in the project is presented to demonstrate the workability of the concept. The modeling and simulation is performed in DIgSILENT and MATLAB/SIMPOWERSYSTEM software.The simulation shows quite promising results. The Microgrid controller managed to control the voltage and frequency in both grid connected and islanding mode as well as maintained them within mandatory Operational limit of respective Operation Code in Malaysia. An active power loss is also significantly reduced.
Jixin Zhang - One of the best experts on this subject based on the ideXlab platform.
-
a malware variants detection methodology with an opCode based feature method and a fast density based clustering algorithm
International Conference on Natural Computation, 2016Co-Authors: Cheng Wang, Jixin ZhangAbstract:Malware is one of the most terrible and major security threats facing the Internet today. In practice, the most widely used malware detection method is static detection. Static detection is effective for many types of malware. Operation Code (opCode) sequences is one of the most important malware features for static analysis. In this paper, our goal is to optimize the accuracy and performance based on opCode features. Due to the diversity of the Operation Code, resulting in a large dimensions of feature of the malware, which will lead to low performance. We propose an information entropy based feature extraction method to extract a few but very useful information as representation of malware instances. At the same time, because of the low performance of the machine learning algorithm and the large set of features in the training and detection phase. We propose a generic Fast Density-Based Clustering algorithm for fast and accurately clustering malware instances. And our experiments demonstrate that our automated malware variant detection methodology is able to achieve high accuracy with significant speedup comparing with the other state-of-art approaches.
-
ICNC-FSKD - A malware variants detection methodology with an opCode based feature method and a fast density based clustering algorithm
2016 12th International Conference on Natural Computation Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016Co-Authors: Cheng Wang, Jixin Zhang, Zheng Qin, Hui YinAbstract:Malware is one of the most terrible and major security threats facing the Internet today. In practice, the most widely used malware detection method is static detection. Static detection is effective for many types of malware. Operation Code (opCode) sequences is one of the most important malware features for static analysis. In this paper, our goal is to optimize the accuracy and performance based on opCode features. Due to the diversity of the Operation Code, resulting in a large dimensions of feature of the malware, which will lead to low performance. We propose an information entropy based feature extraction method to extract a few but very useful information as representation of malware instances. At the same time, because of the low performance of the machine learning algorithm and the large set of features in the training and detection phase. We propose a generic Fast Density-Based Clustering algorithm for fast and accurately clustering malware instances. And our experiments demonstrate that our automated malware variant detection methodology is able to achieve high accuracy with significant speedup comparing with the other state-of-art approaches.