Study on optimum exploitation of silver carp (Hypophthalmichthys molitrix)

The increasing demands of the growing populations can be met by developing aquaculture. However in order to provide suitable grounds for consumption which is acceptable to different conditions and tastes, apart from producing a variety of products we also need to improve the methods of marketing and consumption. The silver carp (Hypophthalmichthys molitrix) comprises about 50 to 85% of the composition of fish species in the polyculture of warm water species in Iran. However the difficulty in pretreatment of this species and the presence of pin bones are among the main reasons to restrict the demand of this species in the domestic markets, particularly in non-coastal provinces. In this project different aspects of silver carp processing studied as follow: 1. Use of fish fillet residuals in preparation of snack, cheese and ice cream 2. Use of fish meat in sausage and fish ball produce 3. Use of machinery to produce of without or low bones fillet and trimmed fillets. Fish sausages were studied in four experimental groups; Based on the results obtained it was evident that sausages in the experimental Group 1 (65% minced fish & 12 % soy bean oil) showed better taste and flavor as compared to those in the other groups. Fish ball were prepared using starch from four different sources (wheat, corn, potato and tapioca). Thirty different trials were tested by adding each starch source either individually or by mixing equal proportions of two sources of starch at a rate of 5, 10 and 15% to 80% ground meat of silver carp. Among trials containing two sources of starch, mixed trials with potato and wheat (5% potato + 5% wheat) were rated higher in organoleptic tests as compared to the other trials. Fifteen formulas were worked for preparing of fish cheese. The quality assessment of product showed that fish cheese in zero and 30 days after storage in 4֯ C were good and medium, respectively. Quantity enrichment of corn snack with FPC until 33% in fish snack preparation, have a best results among with other experiments. Fish ice cream made from fish protein concentrate type a that produced from silver carp in three steps by the extraction with isopropyl alcohol solvent and heat. The result showed that FPC replaced with 30%milk in ice cream formula has a best quality score. On the basis of recent findings, the deep part and a upper part on the back of the fish, like a relatively narrow band, are considered as boneless parts in silver carp. The aims of trimming project introduce best type of fillet trimming and machine for Silver Carp fillet. The results show percentage of waste in trimming by machinery line processing was less than handing, and fillet trimming speed by hand was most than machinery .Also final results of this study show machinery method or complex of handing and machinery for Silver carp fillet trimming are the best .speed rate in pine bone removing from silver carp fillet in handy method with using of transparent table and machinery method were 30-40 min per fish and 15 second per fish, respectively. Removing efficiency of bones from fillets in handy and machinery method were 50-60 and 90 %, respectively

A Structured Low Rank Matrix Pencil for Spectral Estimation and System Identification

In this paper we propose a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interests in many signal processing applications, such as analysis of NMR data and system identification. The proposed algorithm estimates the signal parameters using a matrix pencil constructed from the measured data. To reduce the noise effect, rank deficient Hankel approximation of prediction matrix is used. We show that the performance of the estimation can be significantly improved by structured low rank approximation of the prediction matrix. Computer simulations also show that the noise threshold of our new matrix pencil algorithm is significantly low than those of the existing algorithms

A Super-Resolution Parameter Estimation Algorithm for Multi- Dimensional NMR Spectroscopy

In this paper, we will propose a super-resolution scheme for the parameter estimation of multi-dimensional (M-D) NMR spectroscopy. M-D NMR signals can be modeled as the summation of M-D damped sinusoids. The frequencies and the damping factors of M-D damped sinusoids play important roles in protein structure determination using M-D NMR spectroscopy. We will develop a super-resolution frequency and damping factor estimation algorithm-damped MUSIC (DMUSIC) algorithm. Since the DMUSIC algorithm makes full use of the rank-deficiency and the Hankel property of the data matrix composed of the M-D NMR data, compared with other NMR data analysis algorithms, it can resolve the spectrum using very few data points. The performance of the DMUSIC algorithm is demonstrated by computer simulations

Spectral estimation based on structured low rank matrix pencil

This paper proposes a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interest in many signal processing applications, like analysis of NMR data and system identification. The new algorithm estimates the signal parameters using a matrix pencil constructed from the measured data. To reduce the noise effect, rank deficient Hankel approximation of prediction matrix is used. The performance of the new algorithm is significantly improved by structured low rank approximation of prediction matrix. Computer simulations show that the noise threshold of the new algorithm is significantly better than the existing algorithms. 1

A Structured Low Rank Matrix Pencil for Spectral Estimation and System Identification

In this paper we propose a new matrix pencil based method for estimating parameters (frequencies and damping factors) of exponentially damped sinusoids in noise. The proposed algorithm estimates the signal parameters using a matrix pencil constructed from measured data. We show that the performance of the estimation can be significantly improved by the combination of our proposed matrix pencil algorithm and the structured low rank approximation of the data matrix. Comparison of our matrix pencil method to existing matrix pencil methods as well as to polynomial methods show that our matrix pencil method is more accurate in estimating the signal parameters. It is found through computer simulations that, for exponentially damped sinusoids, our matrix pencil method is less sensitive to noise and has a lower signal-to-noise ratio (SNR) threshold. Keywords: Parameter Estimation, Hankel Approximation, Matrix Pencil. This work was supported by the NIH grant 1R01GM49707. y This work was do..

Improved Parameter Estimation Schemes for Damped Sinusoidal Signals Based on Low-Rank Hankel Approximation

The parameter estimation of damped sinusoidal signals is an important issue in spectral analysis and many applications. The existing algorithms, such as the KT algorithm[8] and the TLS algorithm[13], are based on the low-rank approximation of prediction matrix, which ignores the Hankel property of the prediction matrix, We will prove in this paper that the performance of parameter estimation can be improved if both rank-deficient and Hankel properties of the prediction matrix are exploited in the matrix approximation. Based on this idea, a modified KT (MKT) algorithm and a super-resolution algorithm--damped MUSIC (DMUSIC) algorithm are proposed. Computer simulation results demonstrate that, compared with the original KT algorithm, the MKT and DMUSIC algorithms have about 5dB lower noise threshold and can estimate the parameters of signal with larger damping factors. SP EDICS: SP3.1.1 Spectral Analysis: Single-channel Time Series; SP3.6.1 Parameter Estimation: Single-channel Time Serie..

A Super-Resolution Parameter Estimation Algorithm for Multi-Dimensional NMR Spectroscopy

A super-resolution parameter estimation scheme for multi-dimensional nuclear magnetic resonance spectroscopy is presented in this paper. Multi-dimensional nuclear magnetic resonance signals can be modeled as the summation of multi-dimensional damped sinusoids. The frequencies and the damping factors of multi-dimensional damped sinusoids play important roles in protein structure determination using multi-dimensional nuclear magnetic resonance spectroscopy. A super-resolution frequency and damping factor estimation algorithm is proposed. Since this algorithm makes full use of the rank-deficiency and Hankel properties of the data matrix composed of multi-dimensional nuclear magnetic resonance data, compared with other nuclear magnetic resonance data analysis algorithms, it has the ability to resolve the spectrum using very few data points. The performance of the new algorithm is demonstrated by computer simulation, and its validation is confirmed by experiment using real two-dimensional n..