A Model-Based Approach for Compression of Fingerprint Images

We propose a new fingerprint image compression scheme based on the hybrid model of an image. Our scheme uses the essential steps of a typical automated fingerprint identification system (AFIS) such as enhancement, binarization and thinning to encode fingerprint images. The decoding process is based on reconstructing a hybrid surface by using the gray values on ridges and valleys. In this compression scheme, the ridge skeleton is coded efficiently by using differential chain codes. The valley skeleton is derived from the ridge skeleton and the gray values along the ridge and valley skeletons are encoded using the discrete cosine transform. The error between the original and the replica is also encoded to increase the quality. One advantage of our approach is that original features such as end points and bifurcation points can be extracted directly from compressed image even for a very high compression ratio. Another advantage is that the proposed scheme can be integrated to a typical AFIS easily. The algorithm has been applied to various fingerprint images, and high compression ratios like 63:1 have been obtained. A comparison to wavelet/scalar quantization (WSQ) has been also made

Wake-up receivers for wireless sensor networks: benefits and challenges

For successful data delivery, the destination nodes should be listening to the medium to receive data when the sender node starts data communication. To achieve this synchronization, there are different rendezvous schemes, among which the most energy-efficient is utilizing wakeup receivers. Current hardware technologies of wake-up receivers enable us to evaluate them as a promising solution for wireless sensor networks. In this article the benefits achieved with wake-up receivers are investigated along with the challenges observed. In addition, an overview of state-of-the-art hardware and networking protocol proposals is presented. As wake-up receivers offer new opportunities, new potential application areas are also presented and discussed.Peer ReviewedPostprint (published version

Evolving Neural Networks Applied to Predator-Evader Problem

The creation of strategies to meet abstract goals is an important behavior exhibited by natural organisms. A situation requiring the development of such strategies is the predator-evader problem. To study this problem, Khepera robots are chosen as the competing agents. Using computer simulations the evolution of the adaptive behavior is studied in a predator-evader interaction. A bilaterally symmetrical multilayer perceptron neural network architecture with evolvable weights is used to model the “brains” of the agents. Evolutionary programming is employed to evolve the predator for developing adaptive strategies to meet its goals. To study the effect of learning on evolution a self-organizing map (SOM) is added to the architecture, it is trained continuously and all the predators can access its weights. The results of these two different approaches are compared

A Preliminary Study on The Chemical Structure of Vicia saliva L. Accessions Collected From Natural Flora of European Part of Turkey

The objective of this study was to determine chemical composition of 24 common vetch (Vicia sativa L.) accessions, collected from natural flora in European part of Turkey. The field experiment was carried out in the 2015-2016 growing season at field experimental area of Tekirdag Namik Kemal University, Agricultural Faculty, Field Crops Department in Tekirdag/Turkey. In this study, chemical structure (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), copper (Cu). zinc (Zn). iron (Fe), and manganese(Mn) content) of common vetch accessions were determined on hay. Chemical composition content was statistically significantly different (P <= 0.01) between accessions. According to the results obtained from field experiments, N, P, K, Ca, Mg, Cu, Zn, Fe, and Mn content of common vetch accessions varied between 0.95-3.14 %, 0.84-3.65 %, 0.22-2.44 %, 0.89-2.85 %, 0.23-0.74 %, 0.15-7.80 ppm, 0.10-5.30 ppm, 43.00-2295.20 ppm, 3.08-17.50 ppm, respectively. A wide variation was observed among common vetch accessions used in the study for N, P, K, Ca, Mg, Cu, Zn, Fe, and Mn content. Tetany and Ca/P rates of accessions changed from 0.13 - 1.54 and 0.28 - 2.19, respectively. The tetany and Ca/P rates of common vetch accessions are within suitable values for animal feeding. Ca/P ratio is above the limit value only in accessions 15-2 and 14O04. According to the correlation analysis, N was positively and significantly correlated with P, K, Mg, Cu and Fe. Similarly, P was positively and significantly correlated with N, K and Fe. Magnesium was positively and significantly correlated with N, Ca, Cu, Fe and Mn. Iron was positively and significantly correlated with N, P, Ca, Mg and Cu. As a result, accession 15K17 and 33 were identified as common vetch accessions with high mineral nutrition content.Scientific and Technological Research Council of Turkey (TUBITAK) [TOVAG-1130297]; Tekirdag Namik Kemal University Scientific Research Projects Coordinatorship [NKUBAP.00.24.AR.14.10]This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) Grant (TOVAG-1130297) and Tekirdag Namik Kemal University Scientific Research Projects Coordinatorship (NKUBAP.00.24.AR.14.10)

Tracking cells in Life Cell Imaging videos using topological alignments

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Image and video analysis techniques for cellular microscopy

Abstract from public.pdf.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Advances in automated digital microscopy imaging made it possible to produce multi-dimensional image data that can capture dynamic characteristics of sub-cellular and cellular structures. Biologists routinely produce large volumes of bioimage time lapse data that necessitates automated algorithms for unbiased and repeatable quantitative analysis. These algorithms are the stepping stones in bioimage informatics to turn the image data into biological knowledge. Unique challenges posed by different imaging modalities and cell dynamics require a combination of accurate detection, segmentation, classification and tracking approaches tailored to address and exploit particular image characteristics. In this dissertation, we present algorithms for the analysis of microscopy image sequences to address these challenges. We propose a level set active contour approach to address accurate segmentation in phase-contrast as well as brightfield microscopy imaging that utilizes edge profiles. Our approach significantly outperforms traditional level set approaches. We show the applications of our approach to cell spreading analysis and red blood cell analysis with robust solutions for cell detection to delineate clustered cells. We also present two studies for automated classification of cells in fluorescence microscopy emphasizing the importance of choosing image features for the specific problem. Lastly, we present a fully automated cell detection and tracking approach tailored for muscle satellite cells that enables efficient and unbiased analysis of factors that promote cell motility

Energy and delay optimized contention for wireless sensor networks

In wireless sensor network (WSN) studies, the main objective is minimizing the energy consumption so that the lifetime is maximized under the limited battery capacity constraints. Additionally, in most event-driven WSN applications, the end-to-end delay, and hence, the medium access delay should be minimized. Majority of the WSN MAC protocols are contention-based wherein contention window size setting involves an important trade-off between the collision probability and idle listening durations in contentions where both are aimed to be lowered for efficient network operation. In this paper, the energy optimizing and the delay optimizing contention window sizes are derived as a function of the number of contending nodes. For this purpose, we present separate analyses for the contention delay and for the energy consumed which are verified with detailed simulations. In order to obtain close to optimal performance values in a distributed manner, we propose a method for estimating the number of contending nodes since the individual wireless sensor nodes do not have this information readily. Simulations of an event-driven WSN application verify that the proposed method successfully improve both delay and energy efficiency of the contention-based medium access. The end-to-end network performance is also investigated by employing a geographical routing protocol. Results show that using the heuristic method proposed that use the optimum contention window size analyses presented, the overall network performance can be improved without incurring any overhead to the system.Peer ReviewedPostprint (published version

Overhead energy considerations for efficient routing in wireless sensor networks

Abstract—Energy is the most critical resource in the life of a wireless sensor node. Therefore, its usage must be optimized to maximize the network life. It is known that for higher path loss exponent values, utilizing shorter communication links reduces the transmitter energy, whenever the radio equipment has power adjustment capability. Although the transmitter energy is one of the major factors of total energy dissipation, neglecting the overhead energy could result in suboptimal energy usage. Routing algorithms should also be concerned about the overhead energy which is wasted at each hop of data transfer. In this paper, we investigate the use of multi-hop communication links and compare the amount of energy gain upon alternative routes using analytical techniques. We show that employing multi-hop links does not always result in energy gain, and try to quantify situations when it is advantageous. The analytical results are used in routing decisions and their effect in energy efficiency is validated using simulations. Moreover, we also quantify the gain achieved in terms of lifetime by considering overhead energy on power adjustable sensors for different environmental conditions. We show that the network lifetime can dramatically decrease, if the overhead energy component is neglected during routing decisions. Index Terms—wireless sensor networks, energy saving, multi-hop. I

Evolving Subtasks in Predator-Evader Problem

The creation of strategies to meet abstract goals is an important behavior exhibited by natural organisms. A situation requiring the development of such strategies is the predator-evader problem. To study this problem, Khepera robots are chosen as agents. Using computer simulations the evolution of the adaptive behavior is studied. Neural network architecture with evolvable weights is used as decision mechanism of the predator. Evolutionary programming is employed to evolve the predator for developing adaptive behavior to accomplish the task of catching prey. Then, this task is divided into two subtasks and the predator is evolved to accomplish these subtasks and the neural networks accomplishing subtasks are combined to form the predator. The results of these two approaches are compared