M. Khorsandi

Mohammad Amin Khorsandi, Nader Karimi, Shadrokh Samavi

Capsule endoscopy is a novel and non-invasive method for diagnosis, which assists gastroenterologists to monitor the digestive track. Although this new technology has many advantages over the conventional endoscopy, there are weaknesses that limits the usage of this technology. Some weaknesses are due to using small-size batteries. Radio transmitter consumes the largest portion of energy; consequently, a simple way to reduce the power consumption is to reduce the data to be transmitted. Many works are proposed to reduce the amount of data to be transmitted consist of specific compression methods and reduction in video resolution and frame rate. We proposed a system inside the capsule for detecting informative frames and sending these frames instead of several non-informative frames. In this work, we specifically focused on hardware friendly algorithm (with capability of parallelism and pipeline) for implementation of polyp detection. Two features of positive contrast and customized edges of polyps are exploited to define whether the frame consists of polyp or not. The proposed method is devoid of complex and iterative structure to save power and reduce the response time. Experimental results indicate acceptable rate of detection of our work.

M. A. Khorsandi, N. Karimi, S. Samavi

Radon transform is a type of transform which is used in image processing to transfer the image into intercept-slope coordinate. Its diagonal properties made it appropriate for some applications which need processes in different degrees. Radon transform computation needs a lot of arithmetic operations which makes it a compute-intensive algorithm. In literature an approximate algorithm for computing Radon transform is introduces which reduces the complexity of computations. But this algorithm is complex and need arbitrary accesses to memory. In this paper we proposed an algorithm which accesses to memory sequentially. In the following an architecture is introduced which uses pipeline to reduce the time complexity of algorithm.

Ebrahim Nasr-Esfahani, Nader Karimi, S. M. Reza Soroushmehr et al.

Hand gesture is one of the most important means of touchless communication between human and machines. There is a great interest for commanding electronic equipment in surgery rooms by hand gesture for reducing the time of surgery and the potential for infection. There are challenges in implementation of a hand gesture recognition system. It has to fulfill requirements such as high accuracy and fast response. In this paper we introduce a system of hand gesture recognition based on a deep learning approach. Deep learning is known as an accurate detection model, but its high complexity prevents it from being fabricated as an embedded system. To cope with this problem, we applied some changes in the structure of our work to achieve low complexity. As a result, the proposed method could be implemented on a naive embedded system. Our experiments show that the proposed system results in higher accuracy while having less complexity in comparison with the existing comparable methods.