Chittagong University of Engineering & Technology

About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Computer science & Renewable energy. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.

Detection of harmful food additives using highly sensitive photonic crystal fiber

Abstract: This paper presents three different structures of index guided hexagonal shaped Hollow Core Photonic Crystal Fiber (HC-PCF) specially designed for sensing harmful food additives like Saccharin, Sorbitol and Butyl Acetate. The proposed PCFs have three distinct combinations of hexagonal and/or circular airholes at the innermost cladding layer and in the core region. A comparative study among the proposed structures reveals that the introduction of the hexagonal airholes at the innermost cladding layer increases the sensitivity. Also, the addition of the large circular airholes at the outermost layer reduces the confinement loss in all three structures. The diameters of the airholes of the innermost and outermost layer of the cladding region are gradually varied to optimize the PCFs. The numerical inquiry reveals that the optimized best structure shows relative sensitivity of 88.75%, 87.37% and 86.72% for Saccharin, Sorbitol, and Butyl Acetate respectively at the operating wavelength of 1.33 μm. The performances of the proposed structures are also investigated using Ethanol as the sensed sample for the purpose of comparison with previously reported works. The comparison shows that the introduced fibers outperform most of the recent works. Numerical analyses of the proposed structures are conducted using Full Vectorial Finite Element Method (FV-FEM).

Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus

Abstract: Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from the diabetic patients. Extracting knowledge from such data can be useful to predict diabetic patients. In this work, we employ four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree, on adult population data to predict diabetic mellitus. Our experimental results show that C4.5 decision tree achieved higher accuracy compared to other machine learning techniques.

Demand-Side Management and Demand Response for Smart Grid

Abstract: Demand-side management (DSM) and a market mechanism involving demand response (DR) receive significant attention. The DSM is an emerging initiative which is one of the key elements of restructured power systems. An objective of any DSM program could be peak load clipping instead of adding generation supply, by simply shifting timing from the peak load period to off-peak period. The DR seeks to adjust load demand instead of adjusting generation supply. Different types of load shaping objectives, such as peak clipping, valley filling, load shifting, produce the DR. A compensation for the DR is triggered by diverse policies, market mechanism and implementation models. The integration of DR resources in electric power system becomes worldwide due to advent of communication technologies and metering infrastructure. With the evolving restructured electricity market, aggregator as a mediator between market operator and end-user customers. This chapter discusses six major DSM aspects: (1) the DR resources, (2) possible DR program models, (3) enabler technology framework and policy, (4) role of DR exchange (DRX) market, (5) optimization algorithms used and; (6) a few implementation issues like end-users engagement, privacy preservation, and DR rebounding. An optimization algorithm for specific DRX market structures and how the market participants interact is described in detail.