In order to locate source signal more accurately in authorized frequency bands, a novel primary user localization algorithm based on compressive sensing (PU-CSL) in cognitive radio networks (CRNs) is proposed in this paper. In comparison to existing centroid locating algorithms, PU-CSL shows higher locating accuracy for integrally exploring correlation between source signal and secondary users (SUs). Energy detection is first adopted for collecting the energy fingerprint of source signal at each SU, then degree of correlation between source signal and SUs is reconstructed based on compressive sensing (CS), which determines weights of centroid coordinates. A weighted centroid scheme is finally utilized to estimate source position. Simulation results show that PU-CSL has smaller maximum error of positioning and root-mean-square error. Moreover, the proposed PU-CSL algorithm possess excellent location accuracy and strong anti-noise performance.

https://www.mdpi.com/1999-4893/9/2/25/pdf

Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks

Wireless sensor networks is a topic of much research and interest, because they can be widely applied in different areas such as surveillance, environmental monitoring, etc. For all these applications, localization is the most basic issue. The localization algorithms or techniques can be categorized into two: range-based and range-free. The range-free scheme uses the connectivity information between nodes. In the range-free scheme, nodes that are aware of their location are called anchors, while others that are not are called normal nodes. Anchors are fixed nodes, while the normal nodes are generally mobile. For estimation of their positions, the normal nodes initially gather the information of the positions of anchors as well as their connection, and then calculates its own positions. When compared with range-based techniques, the range-free techniques are more cost-effective, since no additional devices are required. As a result, this paper focuses on the study of two range-free localisation techniques namely, Approximate Point in Triangulation (APIT) and DV-Hop. Thus, we are interested in the investigation of wireless sensor networks on application of these two techniques under varying conditions and parameters.

Comparative Analysis of Approximate Point in Triangulation (APIT) and DV-HOP Algorithms for Solving Localization Problem in Wireless Sensor Networks

The wireless sensor network node positioning is critical and has a broad prospect in military and civilian fields. The current localization algorithm is mainly divided into two types, namely localization algorithm based on distance and distance irrelevant localization algorithm. The two types of algorithms have their own advantages and disadvantages. Consider the advantages and disadvantages of the two algorithms, this paper proposes a higher positioning accuracy, error smaller localization algorithm, this method first takes the maximum likelihood estimation method to locate the node for a rough estimation, then uses the improved centroid algorithm to locate the node coordinates to estimate refinement, further improves the positioning accuracy. Simulation results show that the proposed algorithm improves the positioning accuracy.

Improved Hybrid Localization Algorithm of Maximum Likelihood and Centroid Localization Based on RSSI

Wireless video capsule endoscope (VCE) is used to diagnose lesions along digestive tracts. For proper diagnosis, it is necessary to know the exact location of the lesions which may be estimated by localizing the VCE. In this paper, we propose a simple VCE localization approach using static and dynamic degree-based weighted centroid localization (WCL). In our proposed approach, a sensor array of eight receivers is used to estimate the distance of the moving capsule. The estimated distance is then raised to a higher degree to reduce the weight of the remote sensors marginally lower. We propose a suboptimal method of both static and dynamic degree calculation using the estimated distances. We also analytically compute the optimal values of the degree to set benchmark to compare the performance of our proposed suboptimal methods. We develop a 3 D simulation platform using MATLAB to show the results and to verify the accuracy. We use indices named localization error (LE), average localization error (ALE), standard deviation (STD) and the normalized error to evaluate the performance. Using static optimal degree, the ALE is 5.19 mm where ALE of 6.55 mm is reachable using the suboptimal method. For dynamic degree, ALE using optimal degree is 3.8 mm, while the ALE using suboptimal degree is 6.27 mm. Thus, our proposed algorithms approach benchmark accuracy even if we change the dimension of the sensor network. The performance is also compared to the existing algorithms in the literature which shows better performance using our proposed algorithms.

Degree-Based WCL for Video Endoscopic Capsule Localization

Grayscale is a series of shades of gray without apparent color. The total absence of transmitted or reflected light, which is the darkest shade, black. The total reflection or transmission of light at all observable wavelengths, which is nothing but lightest possible shade i.e., white. Intermediate shades of gray are characterized by equal brightness levels of the primary colors (red, green and blue) for transmitting light, or equal amounts of the three primary pigments (magenta,cyan, and yellow) for reflected light. This paper focuses mainly on measuring the properties of objects in a grayscale image using Regionprops to calculate the standard Deviation. This is achieved by segmenting a grayscale image to get objects of a binary image. Although, the common problem of including chromatic values to a grayscale image has objective solution,not exact, the present approach tries to provide an approach to help minimize the amount of human labor required for this task. We transfer the source’s whole color “mood” to the target image by matching texture information and luminance between the images rather than selecting RGB colors from a group of colors to an individual color components. We pick out to transfer only chromatic information and retain the target image’s original luminance values. Further, the procedure is improved by permitting the user to match areas of the two images with rectangular swatches. It is essential to develop grayscale image pixel value, resultant to each object in the binary image to inspect the original grayscale image.Based on the original grayscale image pixel values, the pixel value properties in grayscale image are used to do routine calculations.

/pdf/measuring-distinct-regions-of-grayscale-image-using-pixel-1e4f250n8u.pdf

Measuring distinct regions of grayscale image using pixel values

As one of the most important applications of wireless sensor network, positioning technology has become a extremely hot research filed. Taking into account of the position of beacon nodess, they make an effect on the positioning accuracy of unknown nodes. Therefore, in this paper, it proposes a new weighted centroid localization algorithm based on the traditional algorithm. For the selection of weight, the distance between beacon nodes and unknown nodes, and the slide length of the triangle are used to formed as the weighted factor. Experiment Simulation results show that this algorithm increased localisition accuracy than that of traditional algorithm.

http://www.sersc.org/journals/IJFGCN/vol6_no5/5.pdf

An Improved Weighted Centroid Localization Algorithm

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An Improved Weighted Centroid Localization Algorithm