57 research outputs found
Analysis of the impact of data compression on condition monitoring algorithms for ball screws
The overall equipment effectiveness (OEE) is a management ratio to evaluate the added value of machine tools. Unplanned machine downtime reduces the operational availability and therefore, the OEE. Increased machine costs are the consequence. An important cause of unplanned machine downtimes is the total failure of ball screws of the feed axes due to wear. Therefore, monitoring of the condition of ball screws is important. Common concepts rely on high-frequency acceleration sensors from external control systems to detect a change of the condition. For trend and detailed damage analysis, large amounts of data are generated and stored over a long time period (>5 years), resulting in corresponding data storage costs. Additional axes or machine tools increase the data volume further, adding to the total storage costs. To minimize these costs, data compression or source coding has to be applied. To achieve maximum compression ratios, lossy coding algorithms have to be used, which introduce distortion in a signal. In this work, the influence of lossy coding algorithms on a condition monitoring algorithm (CMA) using acceleration signals is investigated. The CMA is based on principal component analysis and uses 17 features such as standard deviation to predict the preload condition of a ball screw. It is shown that bit rate reduction through lossy compression algorithms is possible without affecting the condition monitoring - as long as the compression algorithm is known. In contrast, an unknown compression algorithm reduces the classification accuracy of condition monitoring by about 20 % when coding with a quantizer resolution of 4 bit/sample
Subspace methods for portfolio design
Financial signal processing (FSP) is one of the emerging areas in the field of signal processing. It is comprised of mathematical finance and signal processing. Signal processing engineers consider speech, image, video, and price of a stock as signals of interest for the given application. The information that they will infer from raw data is different for each application. Financial engineers develop new solutions for financial problems using their knowledge base in signal processing. The goal of financial engineers is to process the harvested financial signal to get meaningful information for the purpose.
Designing investment portfolios have always been at the center of finance. An investment portfolio is comprised of financial instruments such as stocks, bonds, futures, options, and others. It is designed based on risk limits and return expectations of investors and managed by portfolio managers. Modern Portfolio Theory (MPT) offers a mathematical method for portfolio optimization. It defines the risk as the standard deviation of the portfolio return and provides closed-form solution for the risk optimization problem where asset allocations are derived from. The risk and the return of an investment are the two inseparable performance metrics. Therefore, risk normalized return, called Sharpe ratio, is the most widely used performance metric for financial investments.
Subspace methods have been one of the pillars of functional analysis and signal processing. They are used for portfolio design, regression analysis and noise filtering in finance applications. Each subspace has its unique characteristics that may serve requirements of a specific application. For still image and video compression applications, Discrete Cosine Transform (DCT) has been successfully employed in transform coding where Karhunen-Loeve Transform (KLT) is the optimum block transform.
In this dissertation, a signal processing framework to design investment portfolios is proposed. Portfolio theory and subspace methods are investigated and jointly treated. First, KLT, also known as eigenanalysis or principal component analysis (PCA) of empirical correlation matrix for a random vector process that statistically represents asset returns in a basket of instruments, is investigated. Auto-regressive, order one, AR(1) discrete process is employed to approximate such an empirical correlation matrix. Eigenvector and eigenvalue kernels of AR(1) process are utilized for closed-form expressions of Sharpe ratios and market exposures of the resulting eigenportfolios. Their performances are evaluated and compared for various statistical scenarios. Then, a novel methodology to design subband/filterbank portfolios for a given empirical correlation matrix by using the theory of optimal filter banks is proposed. It is a natural extension of the celebrated eigenportfolios. Closed-form expressions for Sharpe ratios and market exposures of subband/filterbank portfolios are derived and compared with eigenportfolios.
A simple and powerful new method using the rate-distortion theory to sparse eigen-subspaces, called Sparse KLT (SKLT), is developed. The method utilizes varying size mid-tread (zero-zone) pdf-optimized (Lloyd-Max) quantizers created for each eigenvector (or for the entire eigenmatrix) of a given eigen-subspace to achieve the desired cardinality reduction. The sparsity performance comparisons demonstrate the superiority of the proposed SKLT method over the popular sparse representation algorithms reported in the literature
Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes
Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy
data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining
the quality
REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC
The recently developed Distributed Video Coding (DVC) is typically suitable for the
applications where the conventional video coding is not feasible because of its
inherent high-complexity encoding. Examples include video surveillance usmg
wireless/wired video sensor network and applications using mobile cameras etc. With
DVC, the complexity is shifted from the encoder to the decoder.
The practical application of DVC is referred to as Wyner-Ziv video coding (WZ)
where an estimate of the original frame called "side information" is generated using
motion compensation at the decoder. The compression is achieved by sending only
that extra information that is needed to correct this estimation. An error-correcting
code is used with the assumption that the estimate is a noisy version of the original
frame and the rate needed is certain amount of the parity bits. The side information is
assumed to have become available at the decoder through a virtual channel. Due to
the limitation of compensation method, the predicted frame, or the side information, is
expected to have varying degrees of success. These limitations stem from locationspecific
non-stationary estimation noise. In order to avoid these, the conventional
video coders, like MPEG, make use of frame partitioning to allocate optimum coder
for each partition and hence achieve better rate-distortion performance. The same,
however, has not been used in DVC as it increases the encoder complexity.
This work proposes partitioning the considered frame into many coding units
(region) where each unit is encoded differently. This partitioning is, however, done at
the decoder while generating the side-information and the region map is sent over to
encoder at very little rate penalty. The partitioning allows allocation of appropriate
DVC coding parameters (virtual channel, rate, and quantizer) to each region. The
resulting regions map is compressed by employing quadtree algorithm and
communicated to the encoder via the feedback channel. The rate control in DVC is
performed by channel coding techniques (turbo codes, LDPC, etc.). The performance
of the channel code depends heavily on the accuracy of virtual channel model that models estimation error for each region. In this work, a turbo code has been used and
an adaptive WZ DVC is designed both in transform domain and in pixel domain. The
transform domain WZ video coding (TDWZ) has distinct superior performance as
compared to the normal Pixel Domain Wyner-Ziv (PDWZ), since it exploits the
'
spatial redundancy during the encoding. The performance evaluations show that the
proposed system is superior to the existing distributed video coding solutions.
Although the, proposed system requires extra bits representing the "regions map" to be
transmitted, fuut still the rate gain is noticeable and it outperforms the state-of-the-art
frame based DVC by 0.6-1.9 dB.
The feedback channel (FC) has the role to adapt the bit rate to the changing
'
statistics between the side infonmation and the frame to be encoded. In the
unidirectional scenario, the encoder must perform the rate control. To correctly
estimate the rate, the encoder must calculate typical side information. However, the
rate cannot be exactly calculated at the encoder, instead it can only be estimated. This
work also prbposes a feedback-free region-based adaptive DVC solution in pixel
domain based on machine learning approach to estimate the side information.
Although the performance evaluations show rate-penalty but it is acceptable
considering the simplicity of the proposed algorithm.
vii
REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC
The recently developed Distributed Video Coding (DVC) is typically suitable for the
applications where the conventional video coding is not feasible because of its
inherent high-complexity encoding. Examples include video surveillance usmg
wireless/wired video sensor network and applications using mobile cameras etc. With
DVC, the complexity is shifted from the encoder to the decoder.
The practical application of DVC is referred to as Wyner-Ziv video coding (WZ)
where an estimate of the original frame called "side information" is generated using
motion compensation at the decoder. The compression is achieved by sending only
that extra information that is needed to correct this estimation. An error-correcting
code is used with the assumption that the estimate is a noisy version of the original
frame and the rate needed is certain amount of the parity bits. The side information is
assumed to have become available at the decoder through a virtual channel. Due to
the limitation of compensation method, the predicted frame, or the side information, is
expected to have varying degrees of success. These limitations stem from locationspecific
non-stationary estimation noise. In order to avoid these, the conventional
video coders, like MPEG, make use of frame partitioning to allocate optimum coder
for each partition and hence achieve better rate-distortion performance. The same,
however, has not been used in DVC as it increases the encoder complexity.
This work proposes partitioning the considered frame into many coding units
(region) where each unit is encoded differently. This partitioning is, however, done at
the decoder while generating the side-information and the region map is sent over to
encoder at very little rate penalty. The partitioning allows allocation of appropriate
DVC coding parameters (virtual channel, rate, and quantizer) to each region. The
resulting regions map is compressed by employing quadtree algorithm and
communicated to the encoder via the feedback channel. The rate control in DVC is
performed by channel coding techniques (turbo codes, LDPC, etc.). The performance
of the channel code depends heavily on the accuracy of virtual channel model that models estimation error for each region. In this work, a turbo code has been used and
an adaptive WZ DVC is designed both in transform domain and in pixel domain. The
transform domain WZ video coding (TDWZ) has distinct superior performance as
compared to the normal Pixel Domain Wyner-Ziv (PDWZ), since it exploits the
'
spatial redundancy during the encoding. The performance evaluations show that the
proposed system is superior to the existing distributed video coding solutions.
Although the, proposed system requires extra bits representing the "regions map" to be
transmitted, fuut still the rate gain is noticeable and it outperforms the state-of-the-art
frame based DVC by 0.6-1.9 dB.
The feedback channel (FC) has the role to adapt the bit rate to the changing
'
statistics between the side infonmation and the frame to be encoded. In the
unidirectional scenario, the encoder must perform the rate control. To correctly
estimate the rate, the encoder must calculate typical side information. However, the
rate cannot be exactly calculated at the encoder, instead it can only be estimated. This
work also prbposes a feedback-free region-based adaptive DVC solution in pixel
domain based on machine learning approach to estimate the side information.
Although the performance evaluations show rate-penalty but it is acceptable
considering the simplicity of the proposed algorithm.
vii
Contributions to unsupervised and supervised learning with applications in digital image processing
311 p. : il.[EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digital image processing point of view, we have focused on twobasic problems: Color Quantization and filter design. Both problems have beenaddressed from the context of Vector Quantization performed by CompetitiveNeural Networks. Processing of non-stationary data is an interesting paradigmthat has not been explored with Competitive Neural Networks. We have statesthe problem of Non-stationary Clustering and related Adaptive Vector Quantizationin the context of image sequence processing, where we naturally havea Frame Based Adaptive Vector Quantization. This approach deals with theproblem as a sequence of stationary almost-independent Clustering problems.We have also developed some new computational algorithms for Vector Quantizationdesign.The works on supervised learning have been sparsely distributed in time anddirection. First we worked on the use of Self Organizing Map for the independentmodeling of skin and no-skin color distributions for color based face localization. Second, we have collaborated in the realization of a supervised learning systemfor tissue segmentation in Magnetic Resonance Imaging data. Third, we haveworked on the development, implementation and experimentation with HighOrder Boltzmann Machines, which are a very different learning architecture.Finally, we have been working on the application of Sparse Bayesian Learningto a new kind of classification systems based on Dendritic Computing. This lastresearch line is an open research track at the time of writing this Thesis
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