Deep Quality: A Deep No-reference Quality Assessment System

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods

Deep Quality: A Deep No-reference Quality Assessment System

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods

Reduced Order Modeling & Controller Design for Mass Transfer in a Grain Storage System

This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations (PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization, the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed. Numerical results are presented to show the validity of the reduced model and possible further extensions are identified

Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500 nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345 nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345 nm for carbohydrates, 1180–1590 nm and 1860–2094 nm for fat, and 1700–2345 nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths

A Coding Theoretic Model for Error-detecting in DNA Sequences

AbstractA major problem in communication engineering system is the transmitting of information from source to receiver over a noisy channel. To check the error in information digits many error detecting and correcting codes have been developed. The main aim of these error correcting codes is to encode the information digits and decode these digits to detect and correct the common errors in transmission. This information theory concept helps to study the information transmission in biological systems and extend the field of coding theory into the biological domain. In the cellular level, the information in DNA is transformed into proteins. The sequence of bases like Adenine (A), Thymine (T), Guanine (G) and Cytosine (C) in DNA may be considered as digital codes which transmit genetic information. This paper shows the existence of any form error detecting code in the DNA structure, by encoding the DNA sequences using Hamming code

Determination of Chemical Properties of Desi Chickpea Flour (Besan) Using Near Infrared Spectroscopy and Chemometrics

A method was developed to determine the protein, carbohydrate, fat and moisture content of desi chickpea flour (besan) using Near Infrared Spectrometer [NIRS] and multivariate regression namely, Principal Component Regression and Partial Least Square Regression Analysis . Spectra of the samples was collected in reflectance mode using lab built pre dispersive filter based NIRS in the wavelength range of 700-2500 nm. Reference analysis was collected using the Association of official Analytical Chemists (AOAC) methods. NIR spectral data and reference data was used to develop regression models using Partial Least Square Regression and Principal Component Regression. Prediction performance of the models was compared on the basis of the coefficient of correlation [R2] and Root Mean Square Error [RMSE] for calibration and validation sets. The R2 c values for prediction of moisture, fat, protein and carbohydrate content from PLSR model were 0.9858, 0.9863, 0.9888, 0.9915 respectively. PCR model resulted in R2 c 0.9739 for protein, 0.9833 for carbohydrate,0.9795 for fat and 0.9655 for moisture content. PLSR and PCR models results were accurate enough for prediction of the parameters. This study showed that NIR can be used to determine the chemical parameters of food material

Prediction of properties of wheat dough using intelligent deep belief networks

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented