Imtiaj Khan

Bio: Imtiaj Khan is an academic researcher from Bangladesh University of Engineering and Technology. The author has contributed to research in topics: Field-effect transistor & Carbon nanotube field-effect transistor. The author has an hindex of 3, co-authored 10 publications receiving 281 citations. Previous affiliations of Imtiaj Khan include Bangladesh University & Virginia Tech.

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Abstract: This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

Analysis and Mitigation of Power Quality Issues in Distributed Generation Systems Using Custom Power Devices

Abstract: This paper discusses the power quality issues for distributed generation systems based on renewable energy sources, such as solar and wind energy. A thorough discussion about the power quality issues is conducted here. This paper starts with the power quality issues, followed by discussions of basic standards. A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done in this paper. Power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied. Then, we analyze the methods of mitigation of these problems using custom power devices, such as D-STATCOM, UPQC, UPS, TVSS, DVR, etc., for micro grid systems. For renewable energy systems, STATCOM can be a potential choice due to its several advantages, whereas spinning reserve can enhance the power quality in traditional systems. At Last, we study the power quality in dc systems. Simpler arrangement and higher reliability are two main advantages of the dc systems though it faces other power quality issues, such as instability and poor detection of faults.

A comparative performance analysis of 10 nm Si nanowire and carbon nanotube field effect transistors

Abstract: Both Carbon Nanotube and Silicon Nanowire are emerging as promising materials for the development of next generation electronic devices. Our focus is mainly on the comparative study of Silicon Nanowire Field Effect Transistor (SiNW-FET) and Carbon Nanotube Field Effect Transistor (CNT-FET). In this work, we have simulated an n-type single walled CNT-FET and a SiNW-FET. A brief comparison between the transconductances of both types of devices due to the applied strain has been studied where SiNW-FET shows incremental change in transconductance which happens to decrease for CNT-FET for lower input voltage range. Afterwards, we have observed the velocity vs applied electric field curves for both CNT-FET and SiNW-FET. It has been shown that although SiNW-FET has lower saturation velocity than CNT-FET, it can be improved by applying tensile strain. Finally, the direct tunneling gate leakage currents for CNT-FET and SiNW-FET have been investigated, where CNT-FET has been proved to be a better choice for gate leakage reduction.

Transport characterization of silicon nanowire field effect transistors with strain and scattering

Abstract: In this paper, we have investigated the non-ballistic properties of Silicon Nanowire Field Effect Transistor (SiNW-FET) which has significant potential in future nanoelectronic applications. Our first goal is to study the effects of non-ballistic properties like elastic scattering and strain effect. The study reveals that elastic scattering decreases the drain current whereas tensile strain tends to increase it. Secondly, effects of change in drain voltage and temperature on SiNW-FET's non-ballistic I-V characteristics have been studied in this paper. It has been observed that the degree of ballisticity decreases with the increase of temperature for lower drain bias but increases with temperature for higher drain voltage. Finally, we have studied the ballisticity for Carbon Nanotube Field Effect Transistor (CNT-FET). It has been shown that SiNW-FET exhibits better performance than CNT-FET for the combined non-ballistic effects as the degree of ballisticity is higher for the former.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology

Abstract: Internet of Things (IoT) is one of the most rapidly used technologies in the last decade in various applications. The smart things are connected in wireless or wired for communication, processing, computing, and monitoring different real-time scenarios. The things are heterogeneous and have low memory, less processing power. The implementation of the IoT system comes with security and privacy challenges because traditional based existing security protocols do not suitable for IoT devices. In this survey, the authors initially described an overview of the IoT technology and the area of its application. The primary security issue CIA (confidentially, Integrity, Availability) and layer-wise issues are identified. Then the authors systematically study the three primary technology Machine learning(ML), Artificial intelligence (AI), and Blockchain for addressing the security issue in IoT. In the end, an analysis of this survey, security issues solved by the ML, AI, and Blockchain with research challenges are mention.

Blockchain for Future Smart Grid: A Comprehensive Survey

Abstract: The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.