Chip equalized adaptive rake receiver for DS-CDMA UWB systems

Conventional Rake receiver is a popular and effective method of utilizing the diversity offered by a DS-CDMA and multipath communication channel. The proposed Rake receiver is useful for suppression of multiple access interference in a multipath channel. The receiver works on chip level equalization on each Rake finger to cancel multi-access interference. Simulation results show that the convergence, diversity gain and bit error probability performance of the proposed receiver is much better than conventional adaptive Rake receiver in multipath channels

Frequency and time hopping PPM UWB multiple access communication scheme

In this paper we propose frequency and time hopping pulse position modulation (FTH-PPM) ultra wideband (UWB) for multiple access communications.We have derived and investigated the bit error probability for the multi-user synchronous transmitter case in multipath channels with Additive White Gaussian Noise (AWGN). Simulation results show that bit error probability performance of FTH-PPM UWB out performs the time hopping pulse position modulated (TH-PPM) UWB system. It also show that multiuser capacity of FTH-PPM UWB system is much better than TH-PPM UWB system

An efficient adaptive fusion scheme for multifocus images in wavelet domain using statistical properties of neighborhood

In this paper we present a novel fusion rule which can efficiently fuse multifocus images in wavelet domain by taking weighted average of pixels. The weights are adaptively decided using the statistical properties of the neighborhood. The main idea is that the eigen value of unbiased estimate of the covariance matrix of an image block depends on the strength of edges in the block and thus makes a good choice for weight to be given to the pixel, giving more weightage to pixel with sharper neighborhood. The performance of the proposed method have been extensively tested on several pairs of multifocus images and also compared quantitatively with various existing methods with the help of well known parameters including Petrovic and Xydeas image fusion metric. Experimental results show that performance evaluation based on entropy, gradient, contrast or deviation, the criteria widely used for fusion analysis, may not be enough. This work demonstrates that in some cases, these evaluation criteria are not consistent with the ground truth. It also demonstrates that Petrovic and Xydeas image fusion metric is a more appropriate criterion, as it is in correlation with ground truth as well as visual quality in all the tested fused images. The proposed novel fusion rule significantly improves contrast information while preserving edge information. The major achievement of the work is that it significantly increases the quality of the fused image, both visually and in terms of quantitative parameters, especially sharpness with minimum fusion artifacts

Human-mobility-based sensor context-aware routing protocol for delay-tolerant data gathering in multi-sink cell-phone-based sensor networks

Ubiquitous use of cell phones encourages development of novel applications with sensors embedded in cell phones. The collection of information generated by these devices is a challenging task considering volatile topologies and energy-based scarce resources. Further, the data delivery to the sink is delay tolerant. Mobility of cell phones is opportunistically exploited for forwarding sensor generated data towards the sink. Human mobility model shows truncated power law distribution of flight length, pause time, and intercontact time. The power law behavior of inter-contact time often discourages routing of data using naive forwarding schemes. This work exploits the flight length and the pause time distributions of human mobility to design a better and efficient routing strategy. We propose a Human-Mobility-based Sensor Context-Aware Routing protocol (HMSCAR), which exploits human mobility patterns to smartly forward data towards the sink basically comprised of wi-fi hot spots or cellular base stations. The simulation results show that HMSCAR significantly outperforms the SCAR, SFR, and GRAD-MOB on the aspects of delivery ratio and time delay. A multi-sink scenario and single-copy replication scheme is assumed

Distributed execution of cognitive relaying with time incentive: multiple PU scenario

The prime focus of this study is in developing distributed algorithms for cognitive relaying with time incentive for multiple primary users (CRTI-M). CRTI-M is a symbiotic paradigm in which the incumbent primary users (PUs) of the spectrum, with weak transmission links, seek cooperation from the cognitive secondary user (SU) nodes in their vicinity, and in return reward them with an incentive time for the latter's own communication. When relaying through the SU network, each PU can either use its own spectrum or that of the other PUs. Cross-layer optimization problems are formulated to enable both these possibilities in a multi-hop multi-channel cognitive radio network with the objective of maximizing the cumulative time incentive for the SUs. Corresponding distributed algorithms are developed, which face the challenge of meeting the constraints of the formulated problems with only local information and the lack of a centralized controller. Further, to make the CRTI-M schemes practically realizable, a MAC scheduling protocol is suggested, which gives emphasis to the distributed implementation and provides a unified framework for the PUs and SUs. Simulation results are furnished to demonstrate the effectiveness of the proposed algorithms

Hierarchical fusion using vector quantization for visualization of hyperspectral images

Visualization of hyperspectral images that combines the data from multiple sensors is a major challenge due to huge data set. An efficient image fusion could be a primary key step for this task. To make the approach computationally efficient and to accommodate a large number of image bands, we propose a hierarchical fusion based on vector quantization and bilateral filtering. The consecutive image bands in the hyperspectral data cube exhibit a high degree of feature similarity among them due to the contiguous and narrow nature of the hyperspectral sensors. Exploiting this redundancy in the data, we fuse neighboring images at every level of hierarchy. As at the first level, the redundancy between the images is very high we use a powerful compression tool, vector quantization, to fuse each group. From second level onwards, each group is fused using bilateral filtering. While vector quantization removes redundancy, bilateral filter retains even the minor details that exist in individual image. The hierarchical fusion scheme helps in accommodating a large number of hyperspectral image bands. It also facilitates the midband visualization of a subset of the hyperspectral image cube. Quantitative performance analysis shows the effectiveness of the proposed method