Material science,Nanotechnology, Non-linear optics, Optical Fiber Communication, Electromagnetic waves, Electronics

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Md. Nozibul Haque currently working as Professor in the department of Electrical and Electronic Engineering, Islamic University, Kushtia-7003, Bangladesh.

• Geoelectrical Resistivity • GIS • Satellite Remote Sensing • Hydrogeology • Climate.

https://www.researchgate.net/profile/Md_Haque133

Index J

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volume 4, P:701

Applied Geophysics, Environmental Science, Ground Water Modelling and Wireless Mobile Communication

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Mohammad Ruhul Amin Bhuiyan was born on 10th December 1974 in Bangladesh. He received the B.Sc. (Hons) and M.Sc. degree in Applied Physics and Electronic Engineering from Rajshahi University, Bangladesh in 1994 and 1995. The Ph.D. degree (worked in Experimental Physics Division, Bangladesh Atomic Energy Center) from Islamic University, Kushtia, Bangladesh in 2008. He is currently a Professor in the Department of Electrical and Electronic Engineering, Islamic University, Bangladesh. The post-doctoral research has been carried out in Electrical and Electronics Engineering at Cankiri Karatekin University and Manisa Celal Bayar University, Turkey. His current interest is development and characterization of nanostructure thin-film solar cell and thermoelectric materials. His work has produced two books and nearly 45 peer-reviewed scientific international and national research articles.

Renewable Energy, Thin Films Solar Cell, Nanostructure TE Materials

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https://scholar.google.com/citations?hl=en&user=4ua-ZDMAAAAJ

Science Direct

IF 6.019

IF 2.829

IF 0.822

Index Journal

IF 0.340

IF 0.822

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Index Journal

IF 3.83

IF 10.556

Index Journal

IF 0.975

Index Journal

Index Journal

IF 0.975

IF 0.860

IF 2.834

IF 0.480

IF 1.836

Science Direct

Science Direct

IF 5.353

IF 0.664

IF 2.829

A seasoned professor having 25 years experience of teaching and research in undergraduate and postgraduate levels in public university. Having wide experience of working at different levels of the university. Keynote speaker. Quality Assurance Expert, External examiner for PhD thesis for home and abroad. Reviewer for Elsevier journals. TPC for many IEEE sponsored international conferences. Editorial board member for national and international journals.

Wireless networking, Wireless communication. Image processing

https://www.researchgate.net/profile/Md_Ali15

https://scholar.google.com/citations?user=DWSxbuQAAAAJ&hl=en

Nanomaterials/nanocomposite (Organic/Inorganic) synthesis/processing via electrochemical and Sol Gel technique and their characterization/metrology for optoelectronic applications. Thin and thick film deposition via Physical Vapor Deposition (PVD), Pu

https://www.researchgate.net/profile/M_Alam10

https://scholar.google.com/citations?hl=en&user=H3lfBVkAAAAJ

M. Jalal Uddin received his Ph.D. degree in Electronics and Convergence Engineering from KwangWoon University, Seoul, Republic of Korea in 2019 and Master of Science (MS) degree in Nanomolecular Science from Jacobs University Bremen, Germany in 2013. Currently, he is researching as a Post-Doctoral Fellow in the Bio-IT Convergence Lab, KwangWoon University, Seoul, Republic of Korea. His research interests include Smart-biochips, Biosenors, and Wearable Devices for Point-of-Care (PoC) applications. His has nearly 25 peer-reviewed scopus-indexed research articles and four journal covers.

Research are includes Smart-biochips, Biosenors, and Wearable Devices for Point-of-Care (PoC) applications.

https://www.researchgate.net/profile/Jalal-Uddin-6

https://scholar.google.com/citations?hl=en&user=QknzDFQAAAAJ

I am Professor Humayun Kabir completed my PhD in Electronic Engineering from Kwangwoon University, Republic of Korea. Now serving in one of the prominent Public University in Bangladesh. I love teaching and do research on topics related to Electrical and Electronic Engineering

System Modeling; Artificial Intelligence/Machine learning; Context-Aware System ; Internet of Things(IoT); Wireless Communication; Embedded System

https://www.researchgate.net/profile/M_Humayun_Kabir2

https://scholar.google.com/citations?hl=en&view_op=list_works&gmla=AJsN-F5qnQz38q3YUqiBL2nVNpvRbjUbj-dpHnV9zs2bDzZfhRdwP5iYisKo9gU0Kp7W9R1VqOH4MckitNsffdDDegOdFPJ6KE6GYaTzTcHymXQ8BywU8rg&user=uPGKXvoAAAAJ

A smart space is embedded with several components such as sensors, actuators, and computing devices that enable the sensing and control of the environment, and the inhabitants interact with the devices in the smart space whenever they need to. To model a smart space, a dynamic relationship needs to be established among the elements of the space whereby the interactions with devices are considered a dynamic-process state. In this paper, a linear model of a smart space is presented using a state equation, where the two coefficient matrices and need to be defined to model the smart space, and the coefficient matrix is used to determine the states of the devices; similarly, the situation of the smart space is determined using coefficient . An algorithm is presented to make a linear model from the logical functions that are used to describe the system. This model is flexible in terms of the control of the smart-space environment because the environmental factors are represented by a matrix element. This linear smart-space model is helpful for the control of a context-aware system, and we use an example to illustrate the effectiveness of the proposed model.

Recognition of human activity is a key element for building intelligent and pervasive environments. Inhabitants interact with several objects and devices while performing any activity. Interactive objects and devices convey information that can be essential factors for activity recognition. Using embedded sensors with devices or objects, it is possible to get object-use sequencing data. This approach does not create discomfort to the user than wearable sensors and has no impact or issue in terms of user privacy than image sensors. In this paper, we propose a linear model for activity recognition based on the state-space method. The activities and sensor data are considered as states and inputs respectively for linear modeling. The relationship between the states and inputs are defined by a coefficient matrix. This model is flexible in terms of control because all the elements are represented by matrix elements. Three real datasets are used to compare the recognition accuracy of the proposed method to those of other well-known activity recognition model to validate the proposed model. The results indicate that the proposed model achieves a significantly better recognition performance than other models.

Context reasoning is an important issue for a context-aware system. Generally, context reasoning is adopted to deduce new context based on the available contexts. The rule-based reasoning is one of the most well-known methods for context reasoning. However, it is difficult for the rule-based algorithm to reason personalized context, because it requires a large number of rules to apply the user's preferences. To address this weakness, in this paper we suggest the Profile-Applied Reasoning Engine (PARE). PARE is an enhanced rule-based reasoning method which uses profiles while reasoning contexts. By using profiles, PARE can become aware of the context that is preferred by a specific individual. To validate the effectiveness of the proposed reasoning engine, we compared the reasoning result of PARE with traditional rule-based reasoning in smart home domain. PARE shows better outcome for reasoning the personalized contexts than the traditional rule-based reasoning. In addition, by using profiles, a significant number of rules have been omitted and consequently the running time is also decreased. Moreover, PARE occupies less memory space which is restricted with number of variables of a rule. Therefore, PARE optimizes both runtime and memory space, which is valuable when making embedded context-aware system.

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things, represented as any items from our daily life that are enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems has been the main concern for the research community. In this paper, different security threats of Zigbee networks in the IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office/corporate environment. Our proposed SIF can predict and protect against various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting against several potential security attacks efficiently, enabling a more secure way of intelligent monitoring in the IoT platform.

Energy harvesting from human-body-induced motion is mostly challenging due to the low-frequency, high-amplitude nature of the motion, which makes the use of conventional cantilevered spring-mass oscillators unrealizable. Frequency up-conversion by mechanical impact is an effective way to overcome the challenge. However, direct impact on the transducer element (especially, piezoelectric) increases the risk of damaging it and raises questions on the reliability of the energy harvester. In order to overcome this shortcoming, we proposed a transverse mechanical impact driven frequency up-converted hybrid energy harvester for human-limb motion. It utilizes the integration of both piezoelectric and electromagnetic transducers in a given size that allows more energy to be harvested from a single mechanical motion, which, in turn, further improves the power density. While excited by human-limb motion, a freely-movable non-magnetic sphere exerts transverse impact by periodically sliding over a seismic mass attached to a double-clamped piezoelectric bimorph beam. This allows the beam to vibrate at its resonant frequency and generates power by means of the piezoelectric effect. A magnet attached to the beam also takes part in generating power by inducing voltage in a coil adjacent to it. A mathematical model has been developed and experimentally corroborated. At a periodic limb-motion of 5.2 Hz, maximum 93 µW and 61 µW average powers (overall 8 µW·cm⁻³ average power density) were generated by the piezoelectric and the electromagnetic transducers, respectively. Moreover, the prototype successfully demonstrated the application of low-power electronics via suitable AC-DC converters.

Activities of Daily Livings (ADLs) refer to the activities that are carried out by an individual for everyday living. Recognition of ADLs is key element for building intelligent and pervasive environments. We propose a two-layer HMM to build a ADLs recognition model that can represent the mapping between low-level sensor data and high-level activity based on the binary sensor data. We used embedded sensor with appliances or object to get object used sequence data as well as object name, type, interaction time, and location. In the first layer, we use location data of object used sensor to predict the activity class and in the second layer object used sequence data to determine the exact activity. We perform comparison with other activity recognition models using three real datasets to validate the proposed model. The results show that the proposed model achieves significantly better recognition performance than other models.

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0https://www.researchgate.net/profile/Md-Abdullah-Al-Mashud

https://scholar.google.com/citations?user=DGSCFT0AAAAJ&hl=en

I believe that life is wonderful. Learning the way of facing bad times of life makes it more cheerful. I like to go through multidimensional activities with different types of people. I like to enjoy music, play guitar and singing, and playing football, cricket, badminton, and table tennis.

Narrowband Internet of Things (NB-IoT) Channel Estimation, Ad Hoc wireless network, and Electronic System Design.

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Md. Khalid Hossain Jewel

I supervised this work

I supervised this work

I supervised this work