CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Exploring brain connectivity with two-dimensional neural maps

We introduce two-dimensional neural maps for exploring connectivity in the brain. For this, we create standard streamtube models from diffusion-weighted brain imaging data sets along with neural paths hierarchically projected into the plane. These planar neural maps combine desirable properties of low-dimensional representations, such as visual clarity and ease of tract-of-interest selection, with the anatomical familiarity of 3D brain models and planar sectional views. We distribute this type of visualization both in a traditional stand-alone interactive application and as a novel, lightweight web-accessible system. The web interface integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. Anecdotal and quantitative comparisons of the present method with a recently proposed 2D point representation suggest that our representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the 2D path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

Electrical properties of monoazacrown ether-substituted phthalocyanines

D.c. conductivities of polycrystalline monoazacrown ether-substituted phthalocyanines (M=2H, Ni, Zn, Pb, Cu) and diphthalocyanine (M=Lu) are measured as Au-MPc-Au sandwiches to be of the order 10^–10–10^–12 S m^–1. Chemical doping with oxidants (e.g. NOBF₄) and enhancing the stacking of planar phthalocyanine moieties through the formation of alkali metal adducts with sodium and potassium ions leads to increase in conductivity of the order 10¹–10². The low conductivity and the diamagneticity of the bis(phthalocyaninato)-lutetium can be ascribed to the lack of radical nature in LuH(Pc)₂. For the a.c. conductivities, lead and lutetium complexes form a group with higher conductivities and the rest show lower conductivity. The conduction activation energies calculated from Arrhenius plots exhibit the lowest value (0.40 eV) for the lutetium compound.Part of this work was presented at NATO-ASI on Semiconductor Materials and Processing Technologies, Erice, Sicily, 1–13 July 1991.     

印刷线路板电镀废水综合治理

简述线路板电镀废水中氰、铜氨、油墨废水治理方法和混合废水综合治理工艺流程及原理。     

多传感器数据融合技术的现状及展望

阐述了多传感器数据融合的原理，对数据融合算法的研究动态进行了评述，指出了数据融合技术存在的问题，对今后的发展趋势进行了展望。     

Effect of sodium monofluorophosphate treatment on microstructure and frost salt scaling durability of slag cement paste

Sodium-monofluorophosphate (Na-MFP) is currently in use as a surface applied corrosion inhibitor in the concrete industry. Its basic mechanism is to protect the passive layer of the reinforcement steel against disruption due to carbonation. Carbonation is known as the most detrimental environmental effect on blast furnace slag cement (BFSC) concrete with respect to frost salt scaling. In this paper the effect of Na-MFP on the microstructure and frost salt scaling resistance of carbonated BFSC paste is presented. The results of electron microscopy, mercury intrusion porosimetry (MIP) and X-ray diffraction (XRD) are discussed. It is found that the treatment modifies the microstructure and improves the resistance of carbonated BFSC paste against frost salt attack.     

On the Fatty Acid Composition of Turkish Vegetable Oils

Because of the variety of climate conditions very different oil plants except of some tropical kinds grow in Turkey. In this work four groups of raw material of Turkish vegetable oils were investigated. The first group consists of olive, sunflower and cotton, which are important raw materials in Turkey. About 97% of the oil produced in Turkey base on these three raw materials. The second group consists of sesame, poppy, linseed, hemp-seed, rape, soyabean, safflower, castor bean and groundnut, which are temporary of lower importance for Turkey. The third group consists of the non-traditional oil raw materials like tobacco seed, grape seed, scabious, fig seed, tomato seed, laurel seed, pistacia terebinthus, rice bran, maize germs, anise seed, peach-stones and pumpkin seed. The fourth group consists of fruits rich in oil like hazel-nuts, walnuts, almonds and pistachio-nuts, whose production is of great importance in Turkey.     

Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

Prioritized Mobile Robot Exploration Based on Percolation Enhanced Entropy Based Fast SLAM

The major aim in search and rescue using mobile robots is to detect and reach trapped survivors and to support rescue operations through disaster environments. Our motivation is based on the fact that a search and rescue (SAR) robot can navigate within and penetrate a disaster area only if the area in question possesses connected voids. Traversability or penetrability of a disaster area is a primary factor that guides the navigation of a search and rescue (SAR) robot, since it is highly desirable that the robot, without hitting a dead end or getting stuck, keeps its mobility for its primary task of reconnaissance and mapping when searching the highly unstructured environment. We propose a novel percolation guidance that collaborates with entropy based SLAM under a switching control setting the priority to either position or map accuracy. This newly developed methodology has proven to combine the superiority of both percolator guidance and entropy based prioritization so that the active SLAM becomes speedy, with high coverage rate of the area as well as increased accuracy in localization. Our percolator guidance stems from a frontier based conditioning of a-posteriori occurrences of upcoming connected voids that uses the fact that every obstacle partially seen at the frontier of the explored domain has a spatial continuity into the unexplored area. The developed modular architecture is introduced in details and demonstrative examples are provided and discussed.