Data Alignment Technique Research Articles

Comparison of Perimeter Delineation Methods for Remote Sensing Fire Spot Data in Near/Ultra-Real-Time Applications

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.

Through their eyes: Multi-subject brain decoding with simple alignment techniques

Abstract To-date, brain decoding literature has focused on single-subject studies, that is, reconstructing stimuli presented to a subject under fMRI acquisition from the fMRI activity of the same subject. The objective of this study is to introduce a generalization technique that enables the decoding of a subject’s brain based on fMRI activity of another subject, that is, cross-subject brain decoding. To this end, we also explore cross-subject data alignment techniques. Data alignment is the attempt to register different subjects in a common anatomical or functional space for further and more general analysis. We utilized the Natural Scenes Dataset, a comprehensive 7T fMRI experiment focused on vision of natural images. The dataset contains fMRI data from multiple subjects exposed to 9,841 images, where 982 images have been viewed by all subjects. Our method involved training a decoding model on one subject’s data, aligning new data from other subjects to this space, and testing the decoding on the second subject based on information aligned to the first subject. We also compared different techniques for fMRI data alignment, specifically ridge regression, hyper alignment, and anatomical alignment. We found that cross-subject brain decoding is possible, even with a small subset of the dataset, specifically, using the common data, which are around 10% of the total data, namely 982 images, with performances in decoding comparable to the ones achieved by single-subject decoding. Cross-subject decoding is still feasible using half or a quarter of this number of images with slightly lower performances. Ridge regression emerged as the best method for functional alignment in fine-grained information decoding, outperforming all other techniques. By aligning multiple subjects, we achieved high-quality brain decoding and a potential reduction in scan time by 90%. This substantial decrease in scan time could open up unprecedented opportunities for more efficient experiment execution and further advancements in the field, which commonly requires prohibitive (20 hours) scan time per subject.

An 80-Gb/s PAM-4 Simultaneous Bidirectional Transceiver With Hybrid Adaptation Scheme

This paper presents a simultaneous bidirectional (SBD) transceiver with four-level pulse amplitude modulation (PAM-4), employing a novel hybrid adaptation scheme. The possibility of extending bandwidth is explored by applying PAM-4 signaling to SBD. Furthermore, a mismatch compensation method for a hybrid circuit is proposed, which is essential for removing the outbound signal in SBD. The hybrid adaptation is easily implemented by applying the locked condition of the Mueller M|ller phase detector (MMPD) with the data level adaptation. By sharing one error sampler with MMPD and adaptation engine, the presented SBD transceiver is efficient in terms of clocking power consumption and bandwidth of a receiver front-end. A wide linear range hybrid and a data alignment technique guarantee the robust PAM-4 SBD operation. Fabricated in the 28-nm CMOS, the 80-Gb/s SBD transceiver achieves a bit error rate (BER) of less than 10-12 with an energy efficiency of 2.65 pJ/b.

Web Data Extraction and Alignment Tools: A survey

Data extraction from the web pages is the process of analyzing and retrieving relevant data out of the data sources (usually unstructured or poorly structure) in a specific pattern for further processing, involves addition of metadata and data integration details for further process in the data workflow. This survey describes overview of the different web data extraction and data alignment techniques. Extraction techniques are DeLa, DEPTA, ViPER, and ViNT. Data alignment techniques are Pairwise QRR alignment, Holistic alignment, Nested structure processing. Query Result pages are generated by using Web database based on Users Query. The data from these query result pages should be automatically extracted which is very important for many applications, such as data integration, which are needed to cooperate with multiple web databases. New method is proposed for data extraction t that combines both tag and value similarity. It automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table. In which the data values from the same attribute are put into the same column. Data region identification method identify the noncontiguous QRRs that have the same parents according to their tag similarities. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs.

Natural progression of childhood asthma symptoms and strong influence of sex and puberty.

Asthma prevalence, onset, remission and relapse, and healthcare use have been intensively studied. However, asthma symptom progression through childhood and adolescence has not been well studied, in part due to the challenges in obtaining consistent and robust long-term follow-up data on a large series of subjects with asthma. To use the asthma diary symptom data of the Childhood Asthma Management Program placebo group (5 yr, 418 subjects, and total 564,518 records) to establish sex-specific high-resolution time courses of the natural progression of asthma symptoms through childhood and adolescence. We used the asthma diary symptom code as a measure of daily disease severity. Annual records of Tanner stage were used to determine the influence of puberty on severity. A data alignment technique was used to derive 13-year time courses of mean symptoms and mean Tanner stage. Data analyses showed three age- and sex-related phases of asthma symptom progression: Phase 1 (ages 5 and 6 yr)-greater severity in boys; Phase 2 (ages 7 to 9 yr)-no sex difference in severity; and Phase 3 (age 10-17 yr)-greater severity in girls. The continuous decline of symptoms in both sexes stops abruptly at the onset of puberty. The severity of asthma symptoms varies through childhood and adolescence, and patterns differ by sex. Puberty has a strong influence on symptom progression in both sexes. Progression of symptoms is a distinct aspect of asthma epidemiology.

Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation

This paper discusses the design of an inferential sensor for the online prediction of the end-quality of an industrial batch polymerization process. Owing to unequal batch speeds, measurement profiles must be synchronized before modeling. This makes profile alignment an integral part of any inferential sensor. In this work, a novel online hybrid derivative dynamic time warping data alignment technique is presented. The proposed technique allows for automatic adjustment of the warping resolution to achieve optimal alignment results for both slowly and rapidly varying parts of the measurement profiles. The proposed online data alignment technique is combined with a multiway partial least-squares black box model to yield online predictions of the final quality of a running batch process. It is demonstrated that this inferential sensor is capable of accurately predicting the quality online for an industrial polymerization process, even when the production process is only halfway, that is, well before lab measurements become available. As a result of this early warning, batches violating the quality specifications can be corrected or even stopped. This leads to fewer off-spec batches, saves production time, lowers operational costs, and reduces waste material and energy.

Induced EEG Gamma Oscillation Alignment Improves Differentiation Between Autism and ADHD Group Responses in a Facial Categorization Task

INTRODUCTION: Children diagnosed with an autism spectrum disorder (ASD) often lack the ability to recognize and properly respond to emotional stimuli. Emotional deficits also characterize children with attention deficit/hyperactivity disorder (ADHD), in addition to exhibiting limited attention span. These abnormalities may effect a difference in the induced EEG gamma wave burst (35-45 Hz) peaked approximately 300-400 milliseconds following an emotional stimulus. Because induced gamma oscillations are not fixed at a definite point in time post-stimulus, analysis of averaged EEG data with traditional methods may result in an attenuated gamma burst power. METHODS: We used a data alignment technique to improve the averaged data, making it a better representation of the individual induced EEG gamma oscillations. A study was designed to test the response of a subject to emotional stimuli, presented in the form of emotional facial expression images. In a four part experiment, the subjects were instructed to identify gender in the first two blocks of the test, followed by differentiating between basic emotions in the final two blocks (i.e. anger vs. disgust). EEG data was collected from ASD (n=10), ADHD (n=9), and control (n=11) subjects via a 128 channel EGI system, and processed through a continuous wavelet transform and bandpass filter to isolate the gamma frequencies. A custom MATLAB code was used to align the data from individual trials between 200-600 ms post-stimulus, EEG site, and condition by maximizing the Pearson product-moment correlation coefficient between trials. The gamma power for the 400 ms window of maximum induced gamma burst was then calculated and compared between subject groups. RESULTS AND CONCLUSION: Condition (anger/disgust recognition, gender recognition) × Alignment × Group (ADHD, ASD, Controls) interaction was significant at most of parietal topographies (e.g., P3-P4, P7-P8). These interactions were better manifested in the aligned data set. Our results show that alignment of the induced gamma oscillations improves sensitivity of this measure in differentiation of EEG responses to emotional facial stimuli in ADHD and ASD.

Communication-free data alignment for arrays with exponential references in parallelizing compilers for scalable parallel systems

In loops, some arrays are referenced with induction variables. To parallelize such kind of loops, those induction variables should be substituted. Thus, those array references that were substituted are formulated as nonlinear expressions. The goal of data alignment is to intelligently map the computations and data onto a set of virtual processors which are organized as a Cartesian grid (or a template in HPF terms), and to provide data locality for parallelizing compilers so that data access communication costs can be minimized. Most data alignment methods are mainly devised to align the referenced arrays using linear subscripts or quadratic subscripts with n loop index variables, and the methods are well developed. Seldom work, however, is researched on the nonlinear expressions of index variables. This paper proposes a new communication-free data alignment technique to align the referenced arrays using exponential subscripts with n loop index variables or other complex nonlinear expressions. The experimental results using SPEC95FP Benchmarks point out that the techniques proposed in the paper can improve the execution time of the subroutines in these benchmarks.

Augmenting loop tiling with data alignment for improved cache performance

Loop blocking (tiling) is a well-known compiler optimization that helps improve cache performance by dividing the loop iteration space into smaller blocks (tiles); reuse of array elements within each tile is maximized by ensuring that the working set for the tile fits into the data cache. Padding is a data alignment technique that involves the insertion of dummy elements into a data structure for improving cache performance. In this work, we present DAT, a technique that augments loop tiling with data alignment, achieving improved efficiency (by ensuring that the cache is never under-utilized) as well as improved flexibility (by eliminating self-interference cache conflicts independent of the tile size). This results in a more stable and better cache performance than existing approaches, in addition to maximizing cache utilization, eliminating self-interference, and minimizing cross-interference conflicts. Further, while all previous efforts are targeted at programs characterized by the reuse of a single array, we also address the issue of minimizing conflict misses when several tiled arrays are involved. To validate our technique, we ran extensive experiments using both simulations as well as actual measurements on SUN Sparc5 and Sparc10 workstations. The results on benchmarks exhibiting varying memory access patterns demonstrate the effectiveness of our technique through consistently high hit ratios and improved performance across varying problem sizes.

Communication-Minimal Partitioning and Data Alignment for Affine Nested Loops

Data alignment and computation domain partitioning techniques have been widely investigated to reduce communication overheads in distributed memory systems. This paper considers how these two techniques can be combined and applied to affine nested loops. Current data alignment techniques focus on individual entries of data arrays and, in general, cannot be used directly for cases when blocks of entries should be aligned collectively. This paper shows that existing data alignment techniques can be applied to partitioned algorithms if the null space of the data array indexing matrix is a boundary of computation blocks or the intersection of some computation block boundaries. These conditions can be used to generate several different partitionings and time-space transformations for a given target architecture. An example illustrates how it is possible to trade-off the number of communications and the memory space. Another example shows partitions of matrix-matrix multiplication that have smaller communication-computation ratios than Cannon's algorithm.