Design and Evaluation of a Robust Optical Beam-Interruption-Based Vehicle Classifier System

This paper presents the design and development of a novel optical vehicle classifier system, which is based on interruption of laser beams, that is suitable for use in places with poor transportation infrastructure. The system can estimate the speed, axle count, wheelbase, tire diameter, and the lane of motion of a vehicle. The design of the system eliminates the need for careful optical alignment, whereas the proposed estimation strategies render the estimates insensitive to angular mounting errors and to unevenness of the road. Strategies to estimate vehicular parameters are described along with the optimization of the geometry of the system to minimize estimation errors due to quantization. The system is subsequently fabricated, and the proposed features of the system are experimentally demonstrated. The relative errors in the estimation of velocity and tire diameter are shown to be within 0.5% and to change by less than 17% for angular mounting errors up to 30 degrees. In the field, the classifier demonstrates accuracy better than 97.5% and 94%, respectively, in the estimation of the wheelbase and lane of motion and can classify vehicles with an average accuracy of over 89.5%.

Triarylborane-dipyrromethane conjugates bearing dual receptor sites: the synthesis and evaluation of the anion binding site preference

The synthesis and optical properties of four new triarylborane-dipyrromethane (TAB-DPM) conjugates (3a-d) containing dual binding sites (hydrogen bond donor and Lewis acid) have been reported. The new compounds exhibit a selective fluorogenic response towards the F-ion. The NMR titrations show that the anions bind to the TAB-DPM conjugates via the Lewis acidic triarylborane centre in preference to the hydrogen bond donor (dipyrromethane) units.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Studies and Evaluation of EIT Image Reconstruction in EIDORS with Simulated Boundary Data

Simulated boundary potential data for Electrical Impedance Tomography (EIT) are generated by a MATLAB based EIT data generator and the resistivity reconstruction is evaluated with Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS). Circular domains containing subdomains as inhomogeneity are defined in MATLAB-based EIT data generator and the boundary data are calculated by a constant current simulation with opposite current injection (OCI) method. The resistivity images reconstructed for different boundary data sets and images are analyzed with image parameters to evaluate the reconstruction.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Synthesis and evaluation of the biological activity of N `-2-oxo-1,2 dihydro-3H-indol-3-ylidene] benzohydrazides as potential anticancer agents

New N'-2-oxo-1,2-dihydro-3H-indol-3-ylidene]benzohydrazide derivatives were synthesized and evaluated for their cytotoxic properties against murine leukemia, L1210, human leukemia, REH and K562, human T-cell leukemia, CEM and human cervix carcinoma, HeLa cells. Among the tested compounds, the 3,4,5-trimethoxy-N'-5-methyl-2-oxo-1,2-dihydro-3H-indol-3-ylidene]ben zohydrazide derivative (5t) emerged as the most potent inhibitor against all the tumor cell lines evaluated. To investigate the mechanism of action, 5t was further studied by cell cycle analysis, mitochondrial membrane potential analysis, DNA fragmentation and Annexin V-FITC flow cytometric analysis, which suggested that 5t was able to induce apoptosis at submicromolar range.

Synthesis and evaluation of 2,5-di(4-aryloylaryloxymethyl)-1,3,4-oxadiazoles as anti-cancer agents

A series of 2,5-di(4-aryloylaryloxymethyl)-1,3,4-oxadiazoles 9a-j were obtained via multistep synthesis from hydroxybenzophenones 4a-e. The cytotoxicity of compounds 9a-j was evaluated against human leukemia cell lilies (K562 and CEM). The compounds exhibited moderate to good anti-cancer activity with compounds 9b and 9i having a chloro group exhibiting the best activity (IC50 = 10 mu M). Compound 9i exhibited activity against both the cell lines and 9b only exhibited activity against CEM. Further, a lactate dehydrogenase (LDH) assay and DNA fragmentation studies of the compounds 9a-j were also performed. (C) 2013 Elsevier Masson SAS. All rights reserved.

Design and Evaluation of Torsional Probes for Multifrequency Atomic Force Microscopy

Multifrequency atomic force microscopy is a powerful nanoscale imaging and characterization technique that involves excitation of the atomic force microscope (AFM) probe and measurement of its response at multiple frequencies. This paper reports the design, fabrication, and evaluation of AFM probes with a specified set of torsional eigen-frequencies that facilitate enhancement of sensitivity in multifrequency AFM. A general approach is proposed to design the probes, which includes the design of their generic geometry, adoption of a simple lumped-parameter model, guidelines for determination of the initial dimensions, and an iterative scheme to obtain a probe with the specified eigen-frequencies. The proposed approach is employed to design a harmonic probe wherein the second and the third eigen-frequencies are the corresponding harmonics of the first eigen-frequency. The probe is subsequently fabricated and evaluated. The experimentally evaluated eigen-frequencies and associated mode shapes are shown to closely match the theoretical results. Finally, a simulation study is performed to demonstrate significant improvements in sensitivity to the second-and the third-harmonic spectral components of the tip-sample interaction force with the harmonic probe compared to that of a conventional probe.

ANRC Hybrid Test Bed Implementation and an End-to-End Performance Characterization of Dynamic Spectrum Access

An abundance of spectrum access and sensing algorithms are available in the dynamic spectrum access (DSA) and cognitive radio (CR) literature. Often, however, the functionality and performance of such algorithms are validated against theoretical calculations using only simulations. Both the theoretical calculations and simulations come with their attendant sets of assumptions. For instance, designers of dynamic spectrum access algorithms often take spectrum sensing and rendezvous mechanisms between transmitter-receiver pairs for granted. Test bed designers, on the other hand, either customize so much of their design that it becomes difficult to replicate using commercial off the shelf (COTS) components or restrict themselves to simulation, emulation /hardware-in-Ioop (HIL), or pure hardware but not all three. Implementation studies on test beds sophisticated enough to combine the three aforementioned aspects, but at the same time can also be put together using COTS hardware and software packages are rare. In this paper we describe i) the implementation of a hybrid test bed using a previously proposed hardware agnostic system architecture ii) the implementation of DSA on this test bed, and iii) the realistic hardware and software-constrained performance of DSA. Snapshot energy detector (ED) and Cumulative Summation (CUSUM), a sequential change detection algorithm, are available for spectrum sensing and a two-way handshake mechanism in a dedicated control channel facilitates transmitter-receiver rendezvous.

Development and Evaluation of Prussian Blue Mediated Glucose Sensor for Reverse Iontophoresis Application

The screen printed electrochemical glucose sensor is developed suitable for revere iontophoresis (RI) application. Glucose oxidase is immobilized on screen printed sensor using crosslinking method. Electrochemical and material characterization studies are conducted on the developed sensor and the obtained results confirm the suitability of the developed sensor for RI application. The developed sensor is validated by conducting clinical investigations on 10 human subjects through RI. A correlation is established between the blood glucose and extracted glucose, and correlation is found to be 0.73. (C) 2015 The Electrochemical Society. All rights reserved.