Thermal decomposition

In brewing industry, 80% of overall final energy consumption is used in the form of thermal energy where solar thermal can be used for brewing processes as below: steam generation, malting process, stopping germination of grains, air cooling, conservation with hot air, power supplying of washing machines, wither and kiln processes. Malting plants and breweries consume heat for their thermally driven processes at temperature level ranges between 25°C to 120°C. It seems feasible to supply heat at this

Recursive Subspace Identification Algorithm using the Propagator Based Method Irma Wani Jamaludin1 and Norhaliza Abdul Wahab2 1Department of Mechatronics, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2Control & Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor Bahru, Malaysia Article Info ABSTRACT Subspace model identification (SMI) method is the effective method in identifying dynamic

to help GREENoneTEC management to develop the company, we need to analyze the product and company first. The company is part of the solar industry and went from a one-man business into one of the industry leaders in manufacturing flat plate solar thermal collectors for water heating. In 2003, Kioto became GREENoneTEC’s sister company by manufacturing photovoltaics modules. The plan was to exploit all opportunities of the market. GREENoneTEC vision is that ''the sun is the most sustainable resource''

This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the proposed image fusion technique is divided in the following major phases:  preprocesing phase  Feature extraction based on the principal component analysis  The image fusion based on fuzzy set  Reconstruction final image The figure (1) shows the framework of the proposed image fusion and its phases. Fig. 1. The proposed approach of image fusion phases A. Preprocessing Phase This phase consists of three

Secure patients data transmission using XOR ciphering encryption and ECG steganography Shaheen S.Patel1 Prof. Dr. Mrs.S.V.Sankpal2 1 D.Y.Patil College of Engg and Technology, Kolhapur, Maharashtra 2 Asso. Prof . D.Y. Patil College of Engg and Technology, Kolhapur, Maharashtra. E-mails: 1shaheenpatel7860@gmail.com , 2sankpal16@yahoo.com Abstract :- As no of patients that are suffering from cardiac diseases are increasing very rapidly, it is important that remote ECG patient monitoring systems should

A LITERATURE STUDY OF ROBUST COLOR IMAGE WATERMARKING ALGORITHM PANKAJ SONI 1, VANDANA TRIPATI2, RITESH PANDEY3 1. Dept of ECE, ME student, G.N.C.S.G.I., JABALPUR, M.P., INDIA, 2-Dept of ECE, Asst. Prof., G.N.C.S.G.I., JABALPUR, M.P., INDIA, 2-Dept of ECE, Asst. Prof., G.N.C.S.G.I., JABALPUR, M.P., INDIA, ABSTRACT: Digital Watermarking is a technology which is used to identify the owner, distributor of a given image. In recent years, digital watermarking plays a vital role in providing the appropriate

algorithms can be categorized into three main groups: \begin{itemize} \item bin-by-bin, \item matrix inversion, and \item Bayesian methods. \end{itemize} The RooUnfold package supports bin-by-bin, unregularized matrix inversion, Singular Value Decomposition (SVD), TUnfold, and iterative Bayes (based on D'Agostini 1995

embedded arefindout. The embedding is accomplished after block dividing the cover picture and WI. The experimental effects display that the proposed technique is powerful. [35], DivjotKaurThind, et.al In this paper a ultra-modern digital video watermarking scheme is proposed which mixes DWT and SVD in which watermarking is achieved within the HF sub band and then various forms of attacks have been carried out. The watermark object has been embedded in each body of the original video. Since in

approximability of matrices. For each j, the number σj+1 equals the spectral-norm [5] discrepancy between Z and an optimal rank-j approximation. That is, σj+1 = min{kZ − Bk : B has rank j}. 3.2 Randomized Singular Value Decomposition (RSVD): As previously described the singular value decomposition (SVD) of a matrix Z∈Rm×n is defined as , Z=X ∑YT,……………(1) Where X and Y are orthonormal, and ∑ is a rectangular diagonal matrix whose diagonal entries are the singular values signified as σi. The column vectors

typically 90%. PCA allows us to retrieve the original data matrix by projecting the data onto the new coordinate system X′. The new data matrix X′ contains most of the information of the original X with a dimensionality reduction. Singular Value Decomposition (SVD) is a powerful technique for dimensionality reduction. It is a particular approach of Matrix Factorization which is related to PCA. SVD is essentially trying to reduce a rank R matrix to a rank K matrix. Because SVD allows to automatically