Post-loan technical assistance : helping create sustainable and healthy small businesses in central Vermont

The Post-Loan Technical Assistance Project, as reported on in this thesis, focussd on improving access to the Central Vermont Revolving Loan Fund by small businesses. The manuscript describes how business owners were assisted in developing the skills and knowledge necessary to improve their chances of obtaining start-up and operating capital. The manuscript provides specific recommendations on the types of resources that emerging entrepreneurs require in addition to financial, including improved business planning and networking. (Library-derived description)Kaminsky, E. J. (2003). Post-loan technical assistance: helping create sustainable and healthy small businesses in central Vermont. Retrieved from http://academicarchive.snhu.eduMaster of Science (M.S.)School of Community Economic Developmen

Chirp Signaling Offers Modulation Scheme for Underwater Communications

Information contained in the slope of chirp signals can be employed for digital underwater acoustic communications across a broad range of applications

Chirp Slope Keying for Underwater Communications

This paper presents a novel broadband modulation method for digital underwater communications: Chirp Slope Keying (CSK). In its simplest form, the binary information modulates the slope of a linear chirp, with up-chirps representing ones and down-chirps representing zeros. Performance evaluation in the form of probability of error vs. SNR show that the system performs as expected for AWGN environments and very well for more realistic models for underwater acoustical communications, such as the Raylegih channel with Doppler, delays, phase offset, and multipath

Determination of Head Kinematics from Impact Acceleration Test Data Using Neural Networks

This paper presents a study of feed-forward neural network (NN) systems developed to determine the head kinematics of subjects who are exposed to impact accelerations. The neural networks process accelerometer data collected during short-duration impact acceleration tests conducted at the National Biodynamcis Laboratory of the University of New Orleans. During an impact acceleration experiment, the subject sits on the sled chair and a piston gives impetus to the sled to travel down a track. Head data is gathered by an array of nine accelerometers. Two more accelerometers are mounted on the sled. The neural processing systems produce the history of the rotational and translational position, velocity, and acceleration of the origin of the accelerometer array mounted on the mouth. Output produced by a least squares algorithm that uses both photographic and accelerometer raw data are used as a baseline and to provide training data for the neural networks. The main disadvantages of the NNs are their speed, and that statistical information and accurate modeling of the testing system are not required. Results show that the neural networks provide accurate information about the kinematics of the subject even when no photographic data are used

Classification of Cylindrical Targets Buried in Seafloor Sediments

This paper presents the development and evaluation of a time-frequency processing technique for detection and classification of buried cylindrical targets from chirpbased parametric sonar data. The software is designed to discriminate between cylindrical targets —such as cables— of different diameters, which need to be identified as different from other strong reflectors or point targets. The method is evaluated on synthetic data generated with an acoustic scattering model for elastic cylinders for seven different diameters. The model generates characteristic responses of targets acquired by a parametric sonar system. The signal at the sonar receiver hydrophones is first windowed to reduce the data to the region of interest (buried target return). This return is then transformed using joint timefrequency transforms (we use the Wigner and Choi-Williams distributions) to produce a 2D image of the return. Dimensionality reduction and feature extraction are performed by singular value decomposition of this time-frequency image. Linear, quadratic, and Mahalanobis discriminant functions are then applied to the most significant singular values to produce the final classification. The study is carried out for various scenarios of free field response of targets as well as for responses from targets buried in sediment

TCM Decoding Using Neural Networks

This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude imbalance are analyzed

Chirp Slope Keying for Underwater Communications

This paper presents a novel broadband modulation method for digital underwater communications: Chirp Slope Keying (CSK). In its simplest form, the binary information modulates the slope of a linear chirp, with up-chirps representing ones and down-chirps representing zeros. Performance evaluation in the form of probability of error vs. SNR show that the system performs as expected for AWGN environments and very well for more realistic models for underwater acoustical communications, such as the Raylegih channel with Doppler, delays, phase offset, and multipath

Determination of Head Kinematics from Impact Acceleration Test Data Using Neural Networks

This paper presents a study of feed-forward neural network (NN) systems developed to determine the head kinematics of subjects who are exposed to impact accelerations. The neural networks process accelerometer data collected during short-duration impact acceleration tests conducted at the National Biodynamcis Laboratory of the University of New Orleans. During an impact acceleration experiment, the subject sits on the sled chair and a piston gives impetus to the sled to travel down a track. Head data is gathered by an array of nine accelerometers. Two more accelerometers are mounted on the sled. The neural processing systems produce the history of the rotational and translational position, velocity, and acceleration of the origin of the accelerometer array mounted on the mouth. Output produced by a least squares algorithm that uses both photographic and accelerometer raw data are used as a baseline and to provide training data for the neural networks. The main disadvantages of the NNs are their speed, and that statistical information and accurate modeling of the testing system are not required. Results show that the neural networks provide accurate information about the kinematics of the subject even when no photographic data are used

Pyrolysis of brominated feedstock plastic in a fluidised bed reactor

Fire retarded high impact polystyrene has been pyrolysed using a fluidised bed reactor with a sand bed. The yield and composition of the products have been investigated in relation to fluidised bed temperature. The bromine distribution between the products and a detailed analysis of the oils using GC-FID/ECD, GC-MS, FT-ir, and size exclusion chromatography has been carried out. It was found that the majority of the bromine transfers to the pyrolysis oil and the antimony was detected in both the oil and the char. Oil made up over 89.9% of the pyrolysis products. Over 30% of the oil consisted of benzene, toluene, ethylbenzene, styrene and cumene. The pyrolysis gases were mainly hydrocarbons in the C1-C4 range but some HBr and Br2 was detected