NS-2 /MATLAB COMMUNICATION/NS3 Title lists
LITZ Tech is a company located in Coimbatore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)).
As a part of our vision to provide hands-on experience to the students we invite them from the stream of Electronics, Instrumentation, Computer Science & Information Science to carry out their academic project work at our facility under the guidance of industry experts.
The students will be working as Project Trainees. We offer them the necessary guidance & tools to help them to complete their academic projects in the most professional way
As a part of projects and development training, we offer projects keeping in view the latest emerging trends and training in software design and development which enables the students to meet the industrial requirements with a wider knowledge and a greater and a greater confidence..
Adaptive Progressive Motion Vector Resolution Selection Based on Rate-Distortion Optimization
In the state-of-the-art H.265/HEVC video coding standard, the motion vector (MV) resolution is fixed to be 1/4-pel for the entire video sequence, while the inherent video characteristics, e.g. texture complexities and motion activities have been largely ignored. Obviously such strategy may not suffice the demand of high-accuracy motion compensation. In this paper, we propose a specially designed rate-distortion model in terms of the MV resolution by taking the video characteristics into consideration. In particular, the MV resolution selection is formulated as a rate-distortion optimization problem by analyzing the rate-distortion cost of each MV resolution candidate. To further improve the coding performance, the progressive MV resolution strategy is employed, where the optimal progressive MV resolution is determined by decision trees constructed with the rate-distortion model. In this manner, a novel adaptive progressive motion vector resolution (APMVR) selection scheme can be realized and the MV resolution can be adaptively adjusted based on the properties of local content. Extensive experiments and comparisons show that the proposed algorithm significantly improves the coding performance, and 1.8% BD-rate gain on average has been achieved without introducing any noticeable computational complexity.
Haze Removal using the Difference-Structure-Preservation Prior
Fog cover is generally present in outdoor scenes, which limits the potential for efficient information extraction from images. In this paper, the goal of the developed algorithm is to obtain an optimal transmission map as well as to remove hazes from a single input image. To solve the problem, we meticulously analyze the optical model and recast the initial transmission map under an additional boundary prior. For better preservation of the results, the difference-structure-preservation dictionary could be learned such that the local consistency features of the transmission map could be well preserved after coefficient shrinkage. Experimental results show that the method preserves the natural appearance of the image.
Perceptual Adaptation of Image based on Chevreul-Mach Bands Visual Phenomenon
The Perceptual Adaptation of the Image (PAI) is introduced by inspiration from Chevreul-Mach Bands (CMB) visual phenomenon. By boosting the CMB assisting illusory effect on boundaries of the regions, PAI adapts the image to the perception of the human visual system (HVS) and thereof increases the quality of the image. PAI is proposed for application to standard images or the output of any image processing technique. For the implementation of the PAI on the image, an algorithm of morphological filters (MFs) is presented which geometrically adds the model of CMB effect. Numerical evaluation by improvement ratios of four no-reference image quality assessment (NR-IQA) indexes approves PAI performance where it can be noticeably observed in visual comparisons. Furthermore, PAI is applied as a post-processing block for Classical morphological filtering, weighted morphological filtering, median morphological filtering in cancellation of Salt & Pepper, Gaussian and Speckle noise from MRI images, where the above specified NR-IQA indexes validate it. PAI effect on image enhancement is benchmarked upon morphological image sharpening and high-boost filtering.
Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images
Joint spectral and spatial information should be fully exploited in order to achieve accurate classification results for hyperspectral images. In this paper, we propose an ensemble framework, which combines spectral and spatial information in different scales. The motivation of the proposed method derives from the basic idea: by integrating many individual learners, ensemble learning can achieve better generalization ability than a single learner. In the proposed work, the individual learners are obtained by joint spectral-spatial features generated from different scales. Specially, we develop two techniques to construct the ensemble model, namely, hierarchical guidance filtering (HGF) and matrix of spectral angle distance (mSAD). HGF and mSAD are combined via a weighted ensemble strategy. HGF is a hierarchical edge-preserving filtering operation, which could produce diverse sample sets. Meanwhile, in each hierarchy, a different spatial contextual information is extracted. With the increase of hierarchy, the pixels spectra tend smooth, while the spatial features are enhanced. Based on the outputs of HGF, a series of classifiers can be obtained. Subsequently, we define a low-rank matrix, mSAD, to measure the diversity among training samples in each hierarchy. Finally, an ensemble strategy is proposed using the obtained individual classifiers and mSAD. We term the proposed method as HiFi-We. Experiments are conducted on two popular data sets, Indian Pines and Pavia University, as well as a challenging hyperspectral data set used in 2014 Data Fusion Contest (GRSS_DFC_2014). An effectiveness analysis about the ensemble strategy is also displayed.
Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities
Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this work, we present a ubiquitous and quality computer aided blood analysis service for the detection and counting of white blood cells (WBC) in blood samples. WBCs also called leukocytes or leucocytes, are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time consuming. A mobile-cloud assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this research, we propose a smartphone based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to other state-of-the-art schemes.