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The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component's life evaluation is inevitably related to the presence of flaws in it. The detection a...
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The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component's life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non- contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks. Currently in laser thermographic inspection lacks a compromising data processing algorithm which is necessary for the fast crack detection and also the analysis of data is done as part of post processing. In this work we introduced a raw data based image processing algorithm which results precise, better and fast crack detection. The algorithm we developed gives better results in both experimental and modeling data. By applying this algorithm we carried out a detailed investigation variation of thermal contrast with crack parameters like depth and width. The algorithm we developed is applied for various surface temperature data from the 2D scanning model and also validated credibility of algorithm with experimental data.
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SAR raw data generation using inverse chirp scaling and inverse omega-k algorithms is a computationally efficient technique as compared to the traditional temporal simulation. However, the simulation of raw data from a reflectivit...
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SAR raw data generation using inverse chirp scaling and inverse omega-k algorithms is a computationally efficient technique as compared to the traditional temporal simulation. However, the simulation of raw data from a reflectivity map requires the inclusion of coherent information. This paper describes this critical step and presents some results for a static scene as well as a moving object.
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In this paper, a SAR raw data simulation method for urban scene is presented based on the hypothesis that urban area is a set of vertical buildings placed over a random rough dielectric terrain. Facet model and Kirchhoff approach ...
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In this paper, a SAR raw data simulation method for urban scene is presented based on the hypothesis that urban area is a set of vertical buildings placed over a random rough dielectric terrain. Facet model and Kirchhoff approach appropriately including multiple-scattering effects are adopted here to compute scattering coefficients in the scattering model, which operates in two-dimensional Fourier transformed domain. Methods of computing the scattering coefficients in different conditions are discussed in detail. Subsequently, the computational formulas and steps are also provided. It is known that the scattering model can effectively simulate the urban scene. The proposed simulation method of SAR raw echo turns out to be valid through simulation and analysis of the imaging to raw echo.
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In this paper, a SAR raw data simulation method for urban scene is presented based on the hypothesis that urban area is a set of vertical buildings placed over a random rough dielectric terrain. Facet model and Kirchhoff approach ...
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In this paper, a SAR raw data simulation method for urban scene is presented based on the hypothesis that urban area is a set of vertical buildings placed over a random rough dielectric terrain. Facet model and Kirchhoff approach appropriately including multiple-scattering effects are adopted here to compute scattering coefficients in the scattering model, which operates in two-dimensional Fourier transformed domain. Methods of computing the scattering coefficients in different conditions are discussed in detail. Subsequently, the computational formulas and steps are also provided. It is known that the scattering model can effectively simulate the urban scene. The proposed simulation method of SAR raw echo turns out to be valid through simulation and analysis of the imaging to raw echo.
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In this paper, we present the modeling and implementation of a FPGA-based Digital Band-Pass FIR (DBPF) filter to reduce the undesired noise levels in raw ultrasound data. The cascaded tapped delay line FIR filter was built within ...
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In this paper, we present the modeling and implementation of a FPGA-based Digital Band-Pass FIR (DBPF) filter to reduce the undesired noise levels in raw ultrasound data. The cascaded tapped delay line FIR filter was built within the Simulink environment combined with the DSP Builder toolbox, allowing easy and automatic generation of synthesizable VHDL code. In order to demonstrate the feasibility and flexibility of our design, we employed eight symmetrical 8-tap cascaded FIR filter structures to implement a 64-tap DBPF filter. The fractional coefficient values were obtained by the equiripple design method assuming a pass-band frequency between 1.4 and 5 MHz, stop-band of -50 dB and sampling frequency of 40 MHz. The experimental implementation was done on an Intel Stratix IV FPGA by using a linear chirp signal and real raw data added with DC and low- and high-noise frequency components. The accuracy of the model was analyzed by using the normalized root mean square error (NRMSE) cost function for comparison with a reference filter structure designed with FDATool and exported to Simulink. An excellent agreement was achieved between the simulation and experimental results. The overall FPGA utilization was less than 5% and the calculated NRMSE was 0.013%, corroborating the effectiveness of the proposed hardware architecture.
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Big data analytics is one of the IT industries trending and popular field. As the population is increasing at a constant rate, it will also in turn lead to increase in the volume of the data which we refer it as big data. Nowadays...
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Big data analytics is one of the IT industries trending and popular field. As the population is increasing at a constant rate, it will also in turn lead to increase in the volume of the data which we refer it as big data. Nowadays, various organizations or industries (like banking, e-commerce, insurance, etc.) are working on analyzing this big data by dividing the raw data in several fractions so that these sectors can facilitate their customers by analyzing each fraction efficiently. Analysis of big data help these industries in various fields like, money movements, threats, disasters, etc. Nowadays, online banking is one of the general service which was provided to all the customer. As the e-transactions are increasing due to online banking facilities risks of fraud is also increased, so big data analytics also helpful in determining the detection of fraud. Every banking industry generates crucial data from the customer which needs to be stored and analyzed effectively using big data analytic methods to get the necessary insights for banking organizations. Nowadays banking industry start working on analyzing big data to attain goals of marketing. Hence, use of BDA in India would help banks in generating the actions which would help in making the future decisions more effective and stay at peak in business and competition.
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摘要 :
Big data analytics is one of the IT industries trending and popular field. As the population is increasing at a constant rate, it will also in turn lead to increase in the volume of the data which we refer it as big data. Nowadays...
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Big data analytics is one of the IT industries trending and popular field. As the population is increasing at a constant rate, it will also in turn lead to increase in the volume of the data which we refer it as big data. Nowadays, various organizations or industries (like banking, e-commerce, insurance, etc.) are working on analyzing this big data by dividing the raw data in several fractions so that these sectors can facilitate their customers by analyzing each fraction efficiently. Analysis of big data help these industries in various fields like, money movements, threats, disasters, etc. Nowadays, online banking is one of the general service which was provided to all the customer. As the e-transactions are increasing due to online banking facilities risks of fraud is also increased, so big data analytics also helpful in determining the detection of fraud. Every banking industry generates crucial data from the customer which needs to be stored and analyzed effectively using big data analytic methods to get the necessary insights for banking organizations. Nowadays banking industry start working on analyzing big data to attain goals of marketing. Hence, use of BDA in India would help banks in generating the actions which would help in making the future decisions more effective and stay at peak in business and competition.
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The compression of raw SAR data has been a topic of interest to researchers for many years. The goal of such compression is to transmit the highest fidelity data from the radar satellite within the downlink bandwidth constraints. ...
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The compression of raw SAR data has been a topic of interest to researchers for many years. The goal of such compression is to transmit the highest fidelity data from the radar satellite within the downlink bandwidth constraints. The necessity of new compression techniques is further emphasized as advancements in the design of radar technologies are close to surpassing the capabilities of current compression techniques. Developing only a theoretical compression technique is not a complete solution to the problem as the technique must be realizable in the hardware available for placement on the radar satellite. The implementation must not only be low in computational complexity, but must also be able to handle the ever increasing throughput demands of the radar. Bit-planes have been used successfully with several other compression techniques, and are extremely amenable to hardware due to their inherent parallelism. This paper shows that the bit-plane segmentation and simple coding transformation of raw SAR data can reduce the entropy of a single plane by 59%.
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Automated all source data fusion primarily fuses analyst generated messages. These messages represent a small portion, albeit a highly reliable segment, of the available information resources. Task saturation and computational lim...
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Automated all source data fusion primarily fuses analyst generated messages. These messages represent a small portion, albeit a highly reliable segment, of the available information resources. Task saturation and computational limitations often prevent volumes of raw collection data from reaching an analyst in a timely manner. Valuable intelligence information that at least corroborates an important time critical target may be present in unanalyzed raw data files. Analyst generated messages are shown to direct a focused search for additional corroborating evidence in a small spatial-temporal segment of raw data. Automatic corroboration processing simply confirms or denies the presence of a feature in a particular location. Corroboration is a much simpler process than automatic target recognition and requires significantly less processing and fidelity since other information products detect and identify the potential presence of a target or event and focus raw data processing. The new approach transforms previously disregarded raw data into associated corroborative information without increasing analyst tasking. Existing software fuses the corroborative information with analyst messages. An example demonstrates raw data corroboration using imagery. The approximate location, time, and target identity are determined using two associated analyst messages. Raw imagery processing confirms the target and associates an additional message. The fused target priority is amplified by the corroborative message.
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摘要 :
Automated all source data fusion primarily fuses analyst generated messages. These messages represent a small portion, albeit a highly reliable segment, of the available information resources. Task saturation and computational lim...
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Automated all source data fusion primarily fuses analyst generated messages. These messages represent a small portion, albeit a highly reliable segment, of the available information resources. Task saturation and computational limitations often prevent volumes of raw collection data from reaching an analyst in a timely manner. Valuable intelligence information that at least corroborates an important time critical target may be present in unanalyzed raw data files. Analyst generated messages are shown to direct a focused search for additional corroborating evidence in a small spatial-temporal segment of raw data. Automatic corroboration processing simply confirms or denies the presence of a feature in a particular location. Corroboration is a much simpler process than automatic target recognition and requires significantly less processing and fidelity since other information products detect and identify the potential presence of a target or event and focus raw data processing. The new approach transforms previously disregarded raw data into associated corroborative information without increasing analyst tasking. Existing software fuses the corroborative information with analyst messages. An example demonstrates raw data corroboration using imagery. The approximate location, time, and target identity are determined using two associated analyst messages. Raw imagery processing confirms the target and associates an additional message. The fused target priority is amplified by the corroborative message.
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