Adaptive Transmission

In this paper, we present the design and implementation of a software defined orthogonal frequency division multiplexing (OFDM)-based underwater acoustic (UWA) communication system with link adaptation. Our system implementation is based on the customized versions of National Instruments Universal Software Radio Peripheral (USRP). The modified USRPs are interfaced with hydrophone front-ends for acoustic transmission. We investigate the performance of various adaptive algorithms where both modulation order/type and power on each subcarrier are selected based on channel conditions in order to maximize throughput. The experimental in-pool test results verify the superiority of adaptive transmission.

A tremendous rise in the use of Unmanned Aerial Vehicles (UAVs) in wireless communication is recently observed. Due to the useful features of UAVs, a new era of over-the-head wireless communication is finding its space at a rapid pace. A range of civilian and military applications is being tested using such UAV-assisted Flying Ad-hoc Networks. In such networks, the quality of communication is usually affected due to fading on channels and limited resources of the UAVs. In this work, we study the outage performance of dual-hop network over Rayleigh fading air-toground channels between source UAV and destination Ground Control Station and a group of intermediate UAV aerial relays. We focus on spatial diversity using cooperative relaying through relaying UAVs working in a cooperative manner to support the communication using maximum ratio combining at the destination. The best relay may be selected based on channel state information in terms of signal-to-noise ratio to make the system resource efficient. UAVs are assumed to work in Amplify and Forward mode in Half Duplexed downlink communication scenarios while deriving the closedform expression for the outage probability. Numerical results are validated using Monte Carlo Simulations to study the impact of various system parameters on performance. The results of proposed non-buffer aided scheme is compared with compulsory direct-only communication. The impact of number of intermediate relays on dual hop links, symmetric and asymmetric channels is studied. The results are compared by changing the data rate and SNR of the links. The results show that cooperative relaying decreases the outage probability. More relays in cooperation, improve the system performance but at the cost of the network increased resources.

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We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from practitioners. Pixels are considered as independent. Performance, measured by the ROC curve on the Compaq Database is impressive for such a simple model. However, single image examination reveals very irregular results. The second model is a Hidden Markov Model which includes constraints that force smoothness of the solution. The ROC curve obtained shows better performance than the baseline model. Finally, color gradient is included. Thanks to Bethe tree approximation, we obtain a simple analytical expression for the coefficients of the associated maximum entropy model. Performance, compared with previous model is once more improved.

For a long time pixel based segmentation methods were restricted to grayscale images due to the enormous computational costs when dealing with color. With the availability of more powerful computers it is nowadays possible to perform pixel based operations on real camera images even in the full color space. New adaptive classification tools like neural networks make it possible to develop special-purpose object detectors that can segment arbitrary objects in real images with a complex distribution in the feature space after training with one or several previously labelled image(s). The proposed adaptive segmentation method uses local color information to estimate the membership probability in the object resp. background class. The method is applied to the recognition and localization of human hands in color camera images of complex laboratory scenes. The paper focuses on the influence of the chosen color representation and a detailed comparison of the neural approach to standard statistical methods, a threshold filter, and a classifier based on normal distributions.

This paper presents a novel approach for video adult detection using face shape, skin threshold technique and neural network. The goal of employing skin-color information is to select the appropriate color model that allows verifying pixels under different lighting conditions and other variations. Then, the output videos are classified by neural network. The simulation shows that this system achieved 95.4% of the true rate.

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling's, Alicante, Essex, and Stegmann's) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions.

The Integration of Subcarrier index modulation with multiple-mode OFDM and SIM-OFDM are discussed in this paper. The other modulation schemes like OOK with SIM, PSK with subcarrier index modulation is discussed here and simulated by using MATLAB.A comparative analysis between OOK with SIM, PSK with SIM, SIM-OFDM & multiple modulations with SIM is performed and finally multiple-mode SIM OFDM shows BER in the order of 10-7 as compared to other modulation schemes associated with SIM.

Color detection in visible spectrum is a very challenging task as the color in an image is sensitive to various factors such as sensor characteristics, illumination, optics, and ethnicity. Many researchers emphasis that there is a relationship between surface colors and illumination which is closely related problem of color constancy. For skin color detection methods, color correction stage should applied first to get better classification rate. In order to map the observed color to the correct one under some canonical illumination, prior to applying skin detection technique, common and popular solutions of illumination adaptation approaches are investigated and compared like the gray world approach, the white patch, stretch, and neural network approaches. In this paper, three new approaches have been introduced to correct the color of the image that suffers from color unbalancing. The purpose of these approaches is to correct the color natural scene and also to be used to prepare the image prior to applying skin detection technique. The first introduced color correction approach is Modified Grey World (MGW) that uses a reference value calculated using the means of RGB color components. The second approach is MGWWP to use the new MGW approach followed by white patch approach. Also, some other new approaches to solve color constancy problem are proposed during experimental tests. In this paper also, a new approach named Illumination Reflection Problem Solution (IRPSS) has been proposed to correct the skin color in the image that suffers from illumination reflection problem. Some of the skin color detection techniques are investigated and tested to show their results. The proposed IRPS approach succeeded to correct color of 95% of affected pixels from illumination reflection problem.

Segmentation of non-rigid complex structures is of great significance in the field of Vision Based Applications. There are many techniques available to segment the non-rigid bodies like hand, face, etc. These techniques are very useful in applications of Human Computer Interaction, Virtualization, Sign Language Recognition Systems, etc. The results from segmentation are used to identify the regions that are further processed and analysed to fit their application needs. Segmentation techniques have been broadly classified into two types-Parametric & Non-Parametric. The aim of this paper is to provide a survey of the various techniques used for parametric pixel based skin colour segmentation. The paper also provides the understanding of the segmentation techniques that are particularly most suited in a particular scenario over the other techniques. I.

Abstract: This paper proposes a novel idea of combining the color spaces as a pair in getting the improved results in the human skin detection. Skin detection methods that can be used in various applications of image segmentation are evaluated. The performance of the three pixel based classification algorithms are analyzed. The color images of human faces are segmented into skin regions on the basis of piecewise linear decision boundary, Gaussian model to detect human skin regions, and finally, the concept of combinations of two color images are used to configure and analyze the effect of them on the segmentation of human skin regions. The extensive set of experiments is performed over the commonly used face color images using color spaces, namely YCbCr, YUV, YES, YIQ, and XYZ. The color spaces are combined together in using the Gaussian Color Model. The experimental results shows that the method of combining more than one color space with the Gaussian model produces better results ...

This paper proposes a novel idea of combining the color spaces as a pair in getting the improved results in the human skin detection. Skin detection methods that can be used in various applications of image segmentation are evaluated. The performance of the three pixel based classification algorithms are analyzed. The color images of human faces are segmented into skin regions on the basis of piecewise linear decision boundary, Gaussian model to detect human skin regions, and finally, the concept of combinations of two color images are used to configure and analyze the effect of them on the segmentation of human skin regions. The extensive set of experiments is performed over the commonly used face color images using color spaces, namely YCbCr, YUV, YES, YIQ, and XYZ. The color spaces are combined together in using the Gaussian Color Model. The experimental results shows that the method of combining more than one color space with the Gaussian model produces better results than the ordinary skin cluster and Gaussian model with highest skin detection rates of 100%.

this study aims to investigate implementing EM and FCM algorithm for skin color extraction. The capabilities of three well-known color spaces, namely, RGB, HSV and YCbCr for skin-tone extraction are assessed by using statistical modeling of skin-tones using EM and FCM algorithms. The results show that utilizing a Gaussian mixture model for parametric modeling of skin-tones using EM algorithm works well in HSV color space when all three components of color vector are used. In spite of discarding the luminance components in YCbCr and HSV color spaces, EM algorithm provides the best results. The results of detailed comparison are explained in conclusion.

Image based-corrosion detection has become a widespread practice for steel structures, but finetuning their model parameters is time-consuming. Alternatively, convolutional neural networks (CNNs) can also be trained fast and automatically, but they demand a huge training dataset. In this paper, a corrosion detection approach based on an artificial neural network (ANN) whose training dataset size is less than 0.1% of that of typical CNNs is introduced. The input layer of the proposed ANN consists of textural and color properties. In the present work, different color spaces and textural properties are examined for their impact on the robustness of the ANN. Results reveal that the best color channels can be achieved by combining CIE L*u*v* and YUV color spaces. Moreover, energy is selected as the best texture feature with respect to the ANN robustness. The proposed ANN outperforms an available image processing algorithm from the perspective of both speed and accuracy. In conclusion, this ANN can be used for actual applications after a fast and straightforward training step.

Hand Segmentation is the most important steps for hand gesture recognition. If the correctly detect the hand, the hand gesture recognition accuracy will increase. In our research, we used two different threshold based segmentation methods to detect the hand in various background conditions simplex and complex and various illumination conditions and compare the accuracy. The main objective of this paper is to investigate the best threshold values of CbCr threshold based method on the performance of hand segmentation process. The proposed system used the CbCr color threshold based method with various threshold values and otsu's method for skin color hand segmentation. Phyu Myo Thwe | Nwe Ni Kyaw | Kyaw Kyaw Naing "Hand Region Detection using CbCr Color Space and Otsu's Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26683.pdf

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling's, Alicante, Essex, and Stegmann's) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions.

Skin is arguably the most widely used primitive in human image processing research and computer vision with application ranging from face detection and person tracking to pornography filtering. It has proven to be useful and robust cue for detecting human parts in images since (i) it is invariant to orientation and size (ii) it gives extra dimension compared to gray scale methods and (iii) it is fast to process. The main problems with the robustness of skin color detection are however depends on illumination condition, it varies between individuals, many everyday life objects are skin color like and skin color is not unique. Environments comprehensive survey in this topic is missing. The work presented in this paper is a survey of the most frequently used methods and techniques and their numerical evaluation results. General Terms Image Processing, Face Detection.