Energy and Trust Management Framework for MANET using Clustering Algorithm

In General, Mobile Ad-Hoc Network (MANET) has limited energy resources, and it cannot recharge itself. This research goal focuses on building a power management scheme that saves energy in the MANET. Due to power instability, there is a chance that cluster heads fail and function incorrectly in cluster-based routing. As a result, instability occurs with the cluster heads while collecting data and communicating with others effectively. This work focuses on detecting the unstable cluster heads, which are replaced by other nodes implementing the envisaged self-configurable cluster mechanism. A self-configurable cluster mechanism with a k-means protocol approach is proposed to designate cluster heads effectively. The proposed k-means procedure is based on periodic irregular cluster head rotations or altering the number of clusters. We also propose a trust management mechanism in this research to detect and avoid MANET vulnerabilities. Because of the continuously changing topology and limited resources (power, bandwidth, computing), the trust management algorithm should only use local data. Consequently, compared to traditional protocols, the proposed approach with the k-means procedure and its experimental results show lower power usage and provide an optimal system for trust management.

     

Image segmentation using a texture gradient based watershed transform

The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to segment such regions properly, the concept of the "texture gradient" is introduced. Texture information and its gradient are extracted using a novel nondecimated form of a complex wavelet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to segment the identified regions properly. The combined algorithm produces effective texture and intensity based segmentation for application to content based image retrieval.     

宫颈细胞的生物学特征-卷积神经网络联合分类方法

为了改善在宫颈细胞的分类工作中,出现的将异常的病变细胞与正常细胞判断混淆的误诊问题,提出了一种细胞生物学特征-卷积神经网络联合分类方法.首先,使用ResNet分类网络提取出特征向量,然后再将其与手动提取的DNA指数、细胞核/浆比特征一起输入到全连接层,并使用基于MSE损失值的逻辑回归分类,对宫颈细胞进行分类识别.使用5折交叉验证法在Heer数据集上的实验结果表明,这种将卷积神经网络与细胞生物学特征相结合的联合分类方法相较于ResNet卷积神经网络,分类结果的整体准确率提高4％,达到了95％;同时优化MSE损失函数的方法在准确率达到瓶颈的情况下,能够将严重错分率由2.10％降为0.248％,且保持了细胞的整体识别准确率.提出的方法进行计算机辅助检测,能够提升宫颈细胞分类工作准确率、降低误诊率.     

Mobility Tracking in Cellular Networks Using Particle Filtering

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.     

Sequential Monte Carlo methods for contour tracking of contaminant clouds

This paper addresses the problem of contour tracking for airborne emission of contaminant clouds. This is of particular relevance in the context of anti-terrorism and military applications. This problem is solved by estimating the contour boundary positions using a set of particle filters. The use of sequential Monte Carlo techniques enables the tracking to be performed when the measurements are noisy. The tracking results also include the estimation uncertainty. The proposed technique is illustrated for both SCIPUFF and model generated emission scenarios and simulation experiments demonstrate successful tracking throughout the tracking period for both simple and complex environments.     

A robust automatic clustering scheme for image segmentation usingwavelets

The optimal features with which to discriminate between regions and, thus, segment an image often differ depending on the nature of the image. Many real images are made up of both smooth and textured regions and are best segmented using different features in different areas. A scheme that automatically selects the optimal features for each pixel using wavelet analysis is proposed, leading to a robust segmentation algorithm. An automatic method for determining the optimal number of regions for segmentation is also developed.     

Robust rotation-invariant texture classification: wavelet, Gaborfilter and GMRF based schemes

Three novel feature extraction schemes for texture classification are proposed. The schemes employ the wavelet transform, a circularly symmetric Gabor filter or a Gaussian Markov random field with a circular neighbour set to achieve rotation-invariant texture classification. The schemes are shown to give a high level of classification accuracy compared to most existing schemes, using both fewer features (four) and a smaller area of analysis (16×16). Furthermore, unlike most existing schemes, the proposed schemes are shown to be rotation invariant demonstrate a high level of robustness noise. The performances of the three schemes are compared, indicating that the wavelet-based approach is the most accurate, exhibits the best noise performance and has the lowest computational complexity     

Lossless video compression based on backward adaptive pixel-based fast motion estimation

This paper presents a lossless video compression system based on a novel Backward Adaptive pixel-based fast Predictive Motion Estimation (BAPME). Unlike the widely used block-matching motion estimation techniques, this scheme predicts the motion on a pixel-by-pixel basis by comparing a group of past observed pixels between two adjacent frames, eliminating the need of transmitting side information. Combined with prediction and a fast search technique, the proposed algorithm achieves better entropy results and significant reduction in computation than pixel-based full search for a set of standard test sequences. Experimental results also show that BAPME outperforms block-based full search in terms of speed and entropy. We also provide the sub-pixel version of BAPME as well as integrate BAPME in a complete lossless video compression system. The experimental results are superior to the selected state-of-the-art schemes.