K. M. George

Modeling and analysis of soft-test/repair for CCD-based digital X-ray systems

Modern X-ray imaging systems evolve toward digitization for reduced cost, faster time-to-diagnosis, and improved diagnostic confidence. For the digital X-ray systems, charge coupled device (CCD) technology is commonly used to detect and digitize optical X-ray image. This paper presents a novel soft-test/repair approach to overcome the defective pixel problem in CCD-based digital X-ray systems through theoretical modeling and analysis of the test/repair process. There are two possible solutions to cope with the defective pixel problem in CCDs: one is the hard-repair approach and another is the proposed soft-test/repair approach. Hard-repair approach employs a high-yield, expensive reparable CCD to minimize the impact of hard defects on the CCD, which occur in the form of noise propagated through A/D converter to the frame memory. Therefore, less work is needed to filter and correct the image at the end-user level while it maybe exceedingly expensive to practice. On the other hand, the proposed soft-test/repair approach is to detect and tolerate defective pixels at the digitized image level; thereby, it is inexpensive to practice and on-line repair can be done for noninterrupted service. It tests the images to detect the detective pixels and filter noise at the frame memory level and caches them in a flash memory in the controller for future repair. The controller cache keeps accumulating all the noise coordinates and preprocesses the incoming image data from the A/D converter by repairing them. The proposed soft-test/repair approach is particularly devised to facilitate hardware level implementation ultimately for real-time telediagnosis. Parametric simulation results demonstrate the speed and virtual yield enhancement by using the proposed approach; thereby highly reliable, yet inexpensive, soft-test/repair of CCD-based digital X-ray systems can be ultimately realized.

Performance evaluation and tuning for MapReduce computing in Hadoop distributed file system

This paper proposes a method to facilitate the identification process for a set of configuration parameters to achieve the optimal performance with respect to a benchmark program in HDFS in an automated manner. Performance optimization of Hadoop processes is a tedious yet challenging problem due to the complexity of the systems organization with an extensive list of configuration parameters to be considered. An Automated Benchmarking Configuration Method (ABCM) is developed in this work to facilitate the identification process for the set of configuration parameters that minimizes the execution time of a benchmark, namely TestDFSIO Write and Read in particular. A two-phased configuration parameters selection process with a simple sampling technique is proposed in order to mediate the exponential computation time otherwise. By using the proposed technique, we have automatically found the sets of top five selected optimal configuration parameters that reduced the average execution time by 32% compared to the execution time with the default set of Hadoop configuration parameters.

Tweet sentiment as proxy for political campaign momentum

Social media has become very popular over the past decade. There are millions of users across the world sharing information with each other instantaneously through several social media platforms. With these many users sharing huge volumes of data analysis of social media data has become a prominent area of research. Recent studies on the use of data from social media platforms such as Twitter for predicting political elections have raised many questions as well as created the interest in using Twitter data for predictive analysis. The overarching objective of this paper is to study the capability of Twitter data as an ex-ante indicator of event outcomes by modeling the momentum of political campaigns. Three indicators with predictive capability are proposed as measures of momentum of political campaigns. An asset price model is adapted to model momentum of candidates. Empirical validation is provided based on Twitter data from the 2014 US midterm election and the 2016 Presidential primary elections. Our results support the argument that data from social media can be considered as a reliable predictor of events in political campaigns.

Dynamic data rebalancing in Hadoop

Current implementation of Hadoop is based on an assumption that all the nodes in a Hadoop cluster are homogenous. Data in a Hadoop cluster is split into blocks and are replicated based on the replication factor. Service time for jobs that accesses data stored in Hadoop considerably increases when the number of jobs is greater than the number of copies of data and when the nodes in Hadoop cluster differ much in their processing capabilities. This paper addresses dynamic data rebalancing in a heterogeneous Hadoop cluster. Data rebalancing is done by replicating data dynamically with minimum data movement cost based on the number of incoming parallel mapreduce jobs. Our experiments indicate that as a result of dynamic data rebalancing service time of mapreduce jobs were reduced by over 30% and resource utilization is increased by over 50% when compared against Hadoop.

Off-device fault tolerance for digital imaging devices

Charged-coupled device (CCD) is one of the widely-used optical sensing device technologies for various digital imaging systems such as digital cameras, digital camcorders, and digital X-ray imaging systems. Pixels on a CCD may suffer from defective or faulty pixels due to numerous causes such as imperfect fabrication, excessive exposure to light radiation and sensing element aging to mention a few. As the use of high-resolution CCDs increase, defect and fault tolerance of such devices demands immediate attention. In this context, this paper proposes a testing and repair technique for defects/faults on such devices with inability of on-device fault tolerance, referred to as off-device fault tolerance. Digital image sensor devices such as CCD are by their nature, can not readily utilize traditional on-device fault tolerance techniques because each pixel on the device senses a unique image pixel coordinate. No faulty pixel can be replaced nor repaired by a sparse pixel as any displacement of an original pixel coordinate can not sense the original image pixel. Therefore, to effectively provide and enhance the reparability of such devices with inability of on-device fault tolerance, a novel testing and repair method for defects/faults on CCD is proposed based on the soft testing/repair method proposed in our previous work (Jin et al, 2003) under both single and clustered distribution of CCD pixel defects. Clustered fault model due to unwanted diffusion should be considered as practical model and for comparison purpose with single fault model. Also, a novel default/fault propagation model is proposed to effectively capture the on-device effects and faults off the device for an effectiveness and practicality of testing and repair process. The efficiency and effectiveness of the method is demonstrated with respect to the yield enhancement by the soft-testing/repair method under a clustered fault model as well as single fault model, as referred to as soft yield. Extensive numerical simulations are concluded.

Soft-test/repair of ccd-based digital x-ray instrumentation

Absfrncl Modern x-ray imaging systems evolve toward digifindim for reduced eost, foster time-todiagnosis ondimproved diagnostic eonfurmer. For the digitalx-ray systems, CCD (Charge Coupled Device) technology is eommanly used lo ddect and digitize optical x-ray i m g r This p p e r presents n novel sofr-lesUrepoir approach to overcome the defective pirelproblem in CCD (Charge Coupled Device)-based digital x-ray system through theoretical modeling and analysis of the testhepair process. There w e hw possible solulions lo cope with the defectivs pixel problem in CCD; one is the hard-repair approach mdanother is the proposed soJi-lesUrepoir oppronch. Hard-repair approach employs a high-yield, expensive reparable CCD lo minimize the impact of hard-defects on the CCD, which occur in the form of noise propagated through A/D converler lo LheJlome memory. Therefore, less work is neededlofilter andcorrect the image at the end-user level while if maybe exceedingly expensive lo proclice. On the other hand, theproposed sof-tesUrepoir approach is la detect ondtnlerote defective pixels a1 the digitized image level; thereby it is inexpensive lapmetice andon-line repair can be done for nom-interrupted service. I t tests the imnges to detect the deteclive pireis andfilter noire at the frame memory bve< and caches them in B flash memory in the conlroller for fulure repair, The controller cache keeps neeumubting all the nobe coordinates, and preprocesses the incoming image &la from the AID converter by repairing them. The proposed softlesffmpair approach *. pnrlierlarly devired to facilitate hnrdwore level implementalion uUimotdy for real-time telediagnosir. Porometrie simulation results demonslrale lhe speed and vinual yield enhancement by using lhe proposed opprooeh; thereby highly reliable, yet inexpensive sof-tesUmpoir of CCD-baseddigitalx-roy syrlems can be uUimotely realized.

Environmental based characterization of soc for stratified testing

A B Thispaperproposes II noref environmat&bmed mefhod for evaluating thegoodyield m e (CYR) of Systems-on-Chip (SoC) during fabrication Testing nndyield evoluotion at high confidence are MM of the most critical irsues for the fechnologifal SUECCSS of Soc. Since 4 SoC is designed and assembled using deeply embedded InteUecfuol Propem (IP) cores om SoC a rapidly expanding market with great potential

Application of Intervention Analysis on Stock Market Forecasting

In today’s financial market, different financial events have direct impact on stock market values. Even a slight change in those events may result a huge difference in stock prices. So consideration of those effects is very important in forecasting stock values. Most of the researches as of now only consider about forecasting but not these effects. This paper studies the effects of some of those events in financial market forecasting. In this paper, we focused our study on the effects of financial events such as GDP, Consumer Sentiments and Jobless Claims on stock market forecasting and analyze them. These events are considered as intervention effects. The intervention effect is described in this study as temporary but immediate and abrupt. So we have tried to estimate not only the period of effect of these events but also use intervening values on forecasting. These forecasted values are then compared to forecasted values obtained from fusion model based on Concordance and Genetic Algorithm (GA). The concept is validated using financial time series data (S&P 500 Index and NASDAQ) as the sample data sets. We also have analyzed how often our forecasting values have the same movement as that of actual market values. The developed tool can be used not only for forecasting but also for in depth analysis of the stock market.

Fault tolerant memory design for HW/SW co-reliability in massively parallel computing systems

A highly dependable embedded fault-tolerant memory architecture for high performance massively parallel computing applications and its dependability assurance techniques are proposed and discussed in this paper. The proposed fault tolerant memory provides two distinctive repair mechanisms: the permanent laser redundancy reconfiguration during the wafer probe stage in the factory to enhance its manufacturing yield and the dynamic BIST/BISD/BISR (built-in-self-test-diagnosis-repair)-based reconfiguration of the redundant resources in field to maintain high field reliability. The system reliability which is mainly determined by hardware configuration demanded by software and field reconfiguration/repair utilizing unused processor and memory modules is referred to as HW/SW Co-reliability. Various system configuration options in terms of parallel processing unit size and processor/memory intensity are also introduced and their HW/SW Co-reliability characteristics are discussed. A modeling and assurance technique for HW/SW Co-reliability with emphasis on the dependability assurance techniques based on combinatorial modeling suitable for the proposed memory design is developed and validated by extensive parametric simulations. Thereby, design and Implementation of memory-reliability-optimized and highly reliable fault-tolerant field reconfigurable massively parallel computing systems can be achieved.

Regressive testing for system-on-chip with unknown-good-yield

This paper presents a testing method for electronic devices with no a-priori yield information. This problem is referred to as the unknown-good-yield (UKGY) problem. The UKGY problem of systems-on-chip (SoC) is discussed in this paper as SoCs are in general built with embedded intellectual property (IP) cores, each of which is procured from IP providers with no information on known-good-yield (KGY). In general, partial testing is a practical choice for assuring the yield of the product under the stringent time-to-market requirement in todays high density/complexity electronic devices such as SoCs built with deep submicron or nano technology. Therefore, an efficient and effective sampling technique is a key to the success of high confidence testing. An experimental characterization-based testing (referred to as ET) method for SoC has been proposed prior to this work, in which a stratified sampling method was employed based on environmental-based characterization and an experimental design technique to enhance the confidence level of the estimation of yield. The proposed testing method, referred to as regressive testing (RegT), in this paper exploits another method by using parameters (referred to as assistant variables (AV)) free from UKGY that determines the criteria to sample and test SoCs, and employs the regression analysis method to evaluate the yield with regard to confidence interval. A numerical simulation is conducted to demonstrate the efficiency and effectiveness of the proposed RegT in comparison with generic random testing method.