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  Classification of Diabietic Foot Thermograms using Deep Learning Matlab Abstract According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly compl

  Video Stabilization using point feature mapping Matlab Abstract: Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper an algorithm is proposed to stabilize jittery videos. A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video is identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances.   INTRODUCTION This example shows how to stabilize a video that was captured from a jittery platform. One way to stabilize a video is to track a salient feature in the image and

  Helmet detection using Deep Learning Matlab Abstract - Two-wheeler is the most popular modes of transport. Also, it is proved that one of every five bike riders who died on roads were not wearing helmet. This paper proposed a method for motorcycle detection and classification, helmet detection and license plate recognition to detect and identify the motorcyclists without helmet and report it to concerned authorities. Support Vector Machine (SVM) is used for vehicle classification. For helmet detection, CNN algorithms are applied to extract the image attributes, and the SVM classifier is used to classify the objects. For License plate Recognition, Optical Character Recognition (OCR) algorithm is used. The Simple Message Service (SMS) is sent to the helmet rule violators. The results are stored in the Database for further actions. INTRODUCTION Head injuries are the leading cause of death and major trauma for two- and three-wheel motor vehicle users. Travel on a motorcycle carries and a

  Gender Recognition using Convolutional Neural Network Abstract: The purpose of this paper is to demonstrate an innovative convolutional neural network (also known as CNN) methodology for real-time categorization of gender via face photos. The suggested CNN architecture boasts much reduced computational complexity than the  current  methodologies  used  in  pattern  recognition  applications.  By  combining  convolutional  and  subsampling  layers,  the  overall processing  layer  count  is  minimised  to  four.  Notably,  using  cross-correlation  versus  standard  convolution  tends  to alleviate  computing strain.  The  association  is  programmed  using  extended  worldwide  acquisition  frequencies  and  a  second-order  backpropagation  learning algorithmic framework. The demonstrated CNN approach has been examined using two freely downloadable facial statistics, SUMS and AT&T,  with  classification  accuracies  of  99.38%  and  98.75%,  respectively.  Furthermore,  the  neu

  Sign Langauage Gesture Recognition using Convolutional Neural Network: Abstract: Sign language is an indispensable communication means for deaf-mute people because of their hearing impairment. At present, sign language is not popular communications method among hearing people, so that most of the hearing are not willing to have a talk with the deaf-mute, or they must spend much time and energy trying to figure out what the correct meaning is. There has been various research work been done to find an optimal solution to the sign language recognition. This paper reviews one of such works for the sign language recognition using convolutional neural networks. INTRODUCTION Communication can comprehensively be characterized as trade of thoughts, messages and data between at least two people, through a medium, in a way that the sender and the recipient communicate the message in good judgment, that is, they create basic comprehension of the message. We convey through discourse, signals, non

  Audio Noise Supression using Magnitude Subtraction Method This paper proposes an efficient hardware architecture for the spectral subtraction algorithm applied to speech enhancement. Spectral subtraction algorithm is widely used in audio de-noising applications. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. After estimating the noise from the input speech the noise samples are subtracted, making it noise free. In this design we have two principal blocks, the noise estimation-subtraction block and the phase block, which are executed concurrently exploiting the parallel logic blocks of field programmable gate array (FPGA). We have implemented our design on Spartan6 LX45 FPGA, which also meets the high speed requirements. Resource utilization and delay information for the different blocks in our design are presented. Our proposed hardware implementation shows a better SNR value compared to the original software implementation. To

  Audio Watermarking by DWT-SVD-BFO  The main aim of this work is to develop a new watermarking algorithm within an existing discrete wavelet Transform (DWT) and singular value decomposition (SVD) framework. This resulted in the development of a combination of DWT-SVD-BFO (bacterial foraging optimization) watermarking algorithm. In this new implementation, the embedding depth was generated dynamically thereby rendering it more difficult for an attacker to remove, and watermark information was embedded by manipulation of the spectral components in the spatial domain thereby reducing any audible distortion. Further improvements were attained when the embedding criteria was based on bin location comparison instead of magnitude, thereby rendering it more robust against those attacks that interfere with the spectral magnitudes. The further aim of this thesis is to analyze the algorithm from a different perspective   Project Introduction   Technological advances in computing, communications,

  Automatic Digital Modulation Detection by Neural Network Automatic digital modulation detection is a technique used to identify the type of modulation used in a digital signal. One way to accomplish this is through the use of neural networks.   A neural network is a machine learning algorithm that is modeled after the structure of the human brain. It is made up of layers of interconnected nodes, or "neurons," which process information. In the case of modulation detection, a neural network can be trained to recognize the characteristics of different types of modulation, such as amplitude modulation (AM) and frequency modulation (FM). To train a neural network for modulation detection, a dataset of modulated signals must be collected. This dataset should include a variety of different modulation types and signal conditions. Once the dataset is collected, the neural network can be trained to recognize the patterns in the data that correspond to different modulation types. Once

  BAT Optimization Algorithm This code implements the BAT optimization calculation which is valuable in different straight, non-linear optimization problems. This store shares the free MATLAB code with full usage of this paper.        Project Description Step1. Firstly make the expression for Bat function   [bestfit,BestPositions,fmin,Convergence_curve]=BAT(N,Max_iter,lb,ub,dim,fobj) In above explanation the input parameter is mostly a benchmark function which is spoken to by a a ‘fobj’ and others are lb=lower bound limit and ub=upper bound limit , fmax is the maximum frequency and fmin is the minimum frequency , A= loudness of each Bat, r=pulse outflow pace of each Bat, alpha and gamma are the constants for the tumult and heartbeat emanation rate. The r0 is the initial pulse rate. Step2. After the statement call the initialization function. The script for the initialization function written separately. x=initialization(N, Max_iter, dim, ub, lb)

  Brain Tumor Classification using RST Features Medical image processing is the most testing and rising field these days. Handling of MRI images is one of the piece of this field. This work presents a presentation of the unpleasant set based ways to deal with take care of different issues in medical imaging, for example, medical imaging division, object extraction and image classification. Project Description   Brain tumor is a group of tissue that is prearranged by a slow addition of irregular cells. It occurs when cell get abnormal formation within the brain. Recently it is becoming a major cause of death of many people. The seriousness of brain tumor is very big among all the variety of cancers, so to save a life immediate detection and proper treatment to be done. Detection of these cells is a difficult problem, because of the formation of the tumor cells. It is very essential to compare brain tumor from the MRI treatment. It is very difficult to have vision about the abnormal stru