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: Renewable energy & Dielectric. 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.

Reconfigurable architecture design of FIR and IIR in FPGA

Abstract: At the modern world digital signal processing (DSP) has turned into an enormously vital subject. A fundamental aspect of the digital signal processing is filtering. Filtering is a selective system which passes a certain range of frequency and attenuating the others frequency. Digital filtering is a powerful sector of DSP related works. A digital filter is a system which performs mathematical operations on a sampled or discrete time variant signal to shrink or enhance assured aspects of that signal. Digital filters are classified into finite impulse response (FIR) and infinite impulse response (IIR), which are based on the duration of the impulse response. FIR is a stable system and there is no feedback, but IIR has a feed forward path. This paper mainly focuses on the design of FIR and IIR filters of different forms like direct form or transposed of direct form in FPGA. FPGA is a system which can be used to implement the logical function because of its cost-effective properties. The proposed design algorithm of FIR and IIR is modeled in HDL and after the logically verified synthesize in the XST synthesis tool. The design performance of FIR and IIR is analyzed through the timing diagrams summary (time delay, minimum execution period etc.), HDL synthesis report and device utilization summary.

Moving cast shadow detection and removal from Video based on HSV color space

Abstract: Shadow area causes false detection of moving objects during segmentation and tracking those objects properly. Shadows are also reason for the texture loss of background and false connectivity of independent blobs. Hence, we propose a simple method to detect moving object's cast shadow and then remove the shadow region from Video frames. We extract the moving object by subtraction algorithm based on the difference of pixels. Texture, variance property and intensity in HSV color space are used to detect the shadow region and shadow removal is based on the information from the reference frame. Background information is initially stored in reference frame. The next incoming frames with object are compared with this frame. Color information for both background subtraction and shadow detection to improve object segmentation are ensured in this paper. Experimental results show that our proposed method is easy to be understood, can detect and remove the shadow and extract the moving object properly.

Three new flavonol c-glycosides from sida cordifolia linn

Abstract: Three new flavonol C-glycosides: 3′-(3″,7″-dimethyl-2″,6″-octadiene)-8-C-β-D-glucosyl-kaempferol 3-O-β-D-glucoside (1), 3′-(3″,7″-dimethyl-2″,6″-octadiene)-8-C-β-D-glucosyl-kaempferol 3-O-β-D-glucosyl [1→4]-α-D-glucoside (2) and 6-(3″-methyl-2″-butene)-3′-methoxyl-8-C-β-D-glucosyl-kaempferol 3-O-β-D-glucosyl [1→4]-β-D-glucoside (3) have been isolated from 80% ethanolic extract of the aerial parts of Sida cordifolia Linn followed by partitioning with ethyl acetate. Structures were established by chemical and spectroscopic methods.

Injection-molded natural fiber-reinforced polymer composites–a review

Abstract: For the last couple of decades, researchers have been trying to explore eco-friendly materials which would significantly reduce the dependency on synthetic fibers and their composites. Natural fiber-based composites possess several excellent properties. They are biodegradable, non-abrasive, low cost, and lower density, which led to the growing interest in using these materials in industrial applications. However, the properties of composite materials depend on the chemical treatment of the fiber, matrix combination, and fabrication process. This study gives a bibliographic review on bio-composites specially fabricated by the injection-molding method. Technical information of injection-molded natural fiber reinforcement-based composites, especially their type and compounding process prior to molding, are discussed. A wide variety of injection-molding machines was used by the researchers for the composite manufacturing. Injection-molded composites contain natural fiber, including hemp, jute, sisal, flax, abaca, rice husk, kenaf, bamboo, and some miscellaneous kinds of fibers, are considered in this study.

Traditional Bangladeshi Sports Video Classification Using Deep Learning Method

Abstract: Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.