Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras

This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem, which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher' s Linear Discriminants and Gibbs Sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions, and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.     

Performance of an ultra-compact biofilm reactor treating domestic and synthetic wastewaters

The performance of an ultra-compact biofilm reactor (UCBR) treating domestic wastewater (DWW) collected from a local water reclamation plant; and gradually shifting to a mono-type carbon source synthetic wastewater (SWW) combined with DDW (CWW) and finally SWW; was investigated in this study. The total COD concentrations of influent DWW and CWW/SWW were 413.6 ± 80.8 mg/L and 454.9 ± 51.3 mg/L, respectively. The UCBR was able to achieve average total COD removal efficiencies of 70 ± 10% and 80 ± 4% for DWW and SWW respectively. The total COD concentrations of the effluent of DWW and CWW/SWW were 122.5 ± 44.4 mg/L and 89.7 ± 10.3 mg/L, respectively. These observations suggested that heterotrophs in the UCBR system were able to better assimilate and remove carbon of mono-type SWW compared to diverse carbon sources such as DWW; although the influent soluble COD concentrations of the SWW were higher than those of the DWW. However, the effluent NH(4)(+)-N concentrations for both types of wastewater were rather similar, <3.0 mg/L; although the influent NH(4)(+)-N concentrations of the DWW were 1.5 times those of the SWW.     

Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear

This study deals with modeling the flank wear of cryogenically treated AISI M2 high speed steel (HSS) tool by means of adaptive neuro-fuzzy inference system (ANFIS) approach. Cryogenic treatment has recently been found to be an innovative technique to improve wear resistance of AISI M2 HSS tools but precise modelling approach which also incorporates the cryogenic soaking temperature to simulate the tool flank wear is still not reported in any open literature. In order to obtain data for developing the ANFIS model, turning of hot rolled annealed steel stock (C-45) by cryogenically treated tools treated at various cryogenic soaking temperatures was performed in steady state conditions while varying the cutting speed and cutting time. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from experimental data. It was determined that the predictions usually agreed well with the experimental data with correlation coefficients of 0.994 and mean errors of 2.47%. The proposed model can also be used for estimating tool flank wear on-line but the accuracy of the model depends upon the proper training and selection of data points.     

Proton NMR studies of molecular-level mobility in vulcanised and epoxidised natural rubber

Proton spin-lattice relaxations in the laboratory and rotating frames, together with transverse relaxation (free induction decay), have been measured for natural and epoxidised rubber, plus a series of vulcanised samples. The data have been analysed by fitting procedures to give characteristic times which are discussed in terms of the mobility of the various parts of the samples, and of their compositions (including the effects of paraffin wax and oil additives). Interpretation has been aided by recording free induction decays following a period of spin-locking. Carbon-13 magic-angle spinning spectra have also been measured.     

Optimal Selective Count Compatible Runlength Encoding for SOC Test Data Compression

Test data volume amount is increased multi-fold due to the need of quality assurance of various parts of the circuit design at deep submicron level. Huge memory is required to store this enormous test data which not only increases the cost of the ATE but also the test application time. This paper presents an optimal selective count compatible run length (OSCCPRL) encoding scheme for achieving maximum compression for reduction of the test cost. OSCCPRL is a hybrid technique that amalgamates the benefits of other two techniques: 10 Coded run length (10 C) and Selective CCPRL (SCCPRL) proposed here. These techniques work on improvement of the 9 C and CCPRL techniques. In OSCCPRL, entire data is segmented in blocks and further compressed using inter block and intra block level merging techniques. SCCPRL technique is used for encoding the compatible blocks while the 10C is used to do encoding at sub block (half block length) level. In case, if no compatibility is found at block/sub block level then the unique pattern is held as such in the encoded data along with the necessary categorization bits. The decompression architecture is described and it is shown how by just the addition of few states of FSM, better test data compression can be achieved as compared to previous schemes. The simulation results performed for various ISCAS benchmarks circuits prove that the proposed OSCCPRL technique provides an average compression efficiency of around 80 %.     

A computer-based system for environmental impact assessment (EIA) applications to energy power stations in Turkey: ÇEDINFO

Environmental impact assessment (EIA) is a tool for decision makers to take into account the possible effects of a proposed project on the environment and is also a process for collecting the data related to a project design and project area. Different techniques are used for the EIA process. In recent years, including the design and development of databases, classification systems, computer models and expert systems have been used extensively in impact assessment studies. Knowledge-based systems referred to as expert systems and different computer-based systems are an emerging technology in information processing and are becoming increasingly useful tools in different applications areas including EIA studies. Their use for EIA has been quite limited in developing countries, because of the constraints on resources, particularly in expertise and data.

In this study, a knowledge-based software—ÇEDINFO—developed by authors was introduced. ÇEDINFO to be used for EIA practices on energy-generating stations was designed based on the legal EIA process in Turkey. According to the EIA Regulation enacted in Turkey in 1993, energy-generating stations (thermal power station, hydraulic station, nuclear station) in different categories require mandatory EIA reports duly approved by The Ministry of Environment and Forestry before their construction. ÇEDINFO primarily aims to provide educational support for EIA practices and decision-makers on energy-generating stations.     

DNA and quantum based algorithms for VLSI circuits testing

Testing of VLSI circuits is still a NP hard problem. Existing conventional methods are unable to achieve the required breakthrough in terms of complexity, time and cost. This paper deals with testing the VLSI circuits using natural computing methods. Two prototypical algorithms named as DATPG and QATPG are developed utilizing the properties of DNA computing and Quantum computing, respectively. The effectiveness of these algorithms in terms of result quality, CPU requirements, fault detection and number of iterations is experimentally compared with some of existing classical approaches like exhaustive search and Genetic algorithms, etc. The algorithms developed are so efficient that they require only N (where N is the total number of vectors) iterations to find the desired test vector whereas in classical computing, it takes N/2 iterations. The extendibility of new approach enables users to easily find out the test vector from VLSI circuits and can be adept for testing the VLSI chips.     

Synthesis of flexible magnetic nanowires of permanently linked core-shell magnetic beads tethered to a glass surface patterned by microcontact printing

We have developed an efficient, one-step method to create magnetic nanowires consisting of permanently linked chains of magnetic beads of varying flexibility tethered to a patterned glass surface using simple amidation chemistry. The flexibility of the nanowire was governed by the molecular weight of the molecule used to covalently link the beads and its length by the height of the microchannel in which it was synthesized. The nanowire diameter was determined both by the bead size and by the number of beads adhering to each dot in the microstamped, patterned array. Longer nanowires can form loops attached at two points on the glass surface. Both single flexible chains and flexible loops can adopt different configurations (straight, hairpin, S-shaped, etc.) when subjected to magnetic fields, the configurations depending on the directions of these fields. Shorter, less flexible nanowires align with the field always and do not exhibit the more exotic configurations seen for long, flexible chains and loops. These magnetic nanowires can have potential use in microfluidic pumping and mixing processes and in microparticle manipulation.