Novel Implementations of Clutter and Target Discrimination Using Threshold Skewness Method

Now a day’s defence applications associated to novel, army and military war fields are required wall imaging discrimination. As of now many wall-imaging techniques are designed but cannot discriminate the target and clutter with accurate working. Therefore, a novel advance wall image tracking method is required for differentiate the clutter and human target. In this research work single value decomposition technique is used to estimate the range bin behind the wall target. In order to track the target and clutter single-value-decomposition (SVD) is not sufficient, so that along this SVD, threshold skewness (TS) method has been presented. Combination of SVD-TS giving the accurate long range-bin sensing and directed the human’s targets. SVD-TS method is a statistical scheme, which can realise the amplitude ranges through large number of range-bin scans. This technique improves the accuracy by 98.6%, skewness by 8%, and normalised power by 98.9%. These SVD-TS method is more efficient and compete with existed techniques.

Sistem Rekomendasi Suku Cadang Berdasarkan Item Based Filtering

Replaceable spare part on workshop have many transaction and possibility thus recommender system is needed to simplify the selection process. We propose recommender system with item collaborative filtering, with high data sparsity. With Single Value Decomposition we reduce the matriks to improve the system and decrease “noise” value. Model will be evaluated using MAE, RMSE, and FCP metrics. The results of recommendation model are MAE = 1.2752, RMSE = 1.4882, dan FCP = 0.4947.

Abstract WP30: Delay and Dispersion Correction Leads to More Accurate Core Estimation for Endovascular Thrombectomy Patients

Objective: Endovascular thrombectomy (EVT) significantly improves clinical outcomes in acute ischemic stroke with large vessel occlusion. Clinical benefits are inversely proportional to size of the pre-treatment ischemic core. Therefore, accurate measurement of the size of core is critical in selecting patients for EVT. Different post-processing perfusion algorithms for automated core calculation on perfusion CT (CTP) are based on variations of deconvolution of the tissue concentration time curve with the arterial input function (single value decomposition, or SVD). In this study, we compared ischemic core estimated by two different CTP automated algorithms to the final infarct volume as demonstrated by follow up diffusion weighted imaging (DWI). Methods: We performed a retrospective analysis of patients who underwent EVT. Inclusion criteria were CT perfusion scan prior to EVT, successful EVT with mTICI 2b-3 reperfusion, and DWI scan 24-48 hours post-EVT. CTP data were processed by two different post-processing algorithms: ‘delay-insensitive’ single value decomposition (DISVD) and delay and dispersion corrected single value decomposition (ddSVD) using the respective commercially available automated CTP software. CTP core volumes from both methods were compared with DWI final infarct volumes using an independent software (MRIcron) for concordance. The agreement between a given algorithm and MRI was estimated using Lin’s concordance coefficient and further investigated using reduced major axis regression. Results: One hundred and three patients who underwent EVT and achieved successful mTICI 2b-3 reperfusion were included. Both algorithms had excellent agreement with MRI (Lin’s concordance coefficients: DISVD 0.8 (95% CI: 0.73; 0.87), ddSVD 0.92 (95% CI: 0.89; 0.95). Compared to ddSVD (reduced major axis slope = 0.95), DISVD exhibited larger extent of proportional bias (slope = 1.12). Conclusion: Both algorithms showed excellent agreement with FIV calculated on MRI but DISVD post-processing overestimated the larger ischemic cores, which may lead to unnecessary exclusion of patients from EVT due to a 'large core'.

Enhancement of Cloud Stationed Healthcare Information Security by Dimensionality Reduction

The security of healthcare information can be secured by the use of cloud environment, and takes finite estimating power. The security of patient’s data shared over the internet can be distressed by healthcare institutions because of growing high popularity. The Eigen decomposition (ED) and Single Value Decomposition (SVD) of a matrix are relevant to maintain the security and the study of Dimension Reduction and its advantages are also applicable. To reduce the data without loss, Principal Component Analysis (PCA) is used. Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed highdimensional features or complex distance functions into a lowdimensional space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernel based data The proposed method covers how to generalize locality-sensitive hashing and the implementation of Kernel PCA based methods for Dimensionality Reduction can be applied to Medical data provides high security and utilize the resources of the cloud to inhibit data efficiently.

Detection of copy-move image forgery using SVD and cuckoo search algorithm

Copy-move Copy move forgery (CMF) is one of the straightforward strategies to create forged images. To detect this kind of forgery one of the widely used method is single value decomposition (SVD). Few methods based on SVD are most acceptable but some methods are less acceptable because these methods highly depend on those parameters value, which is manually selected depending upon the tampered images. For different images, we require different parameter values. In this paper, we have proposed a novel method, which uses both copy-move forgery detection using SVD and Cuckoo search (CS) algorithm. It utilizes Cuckoo search algorithm to generate customized parameter values for different tampered images, which are used in copy-move forgery detection (CMFD) under block based framework.

Single-Frame Attitude Determination Methods for Nanosatellites

Single-frame methods of determining the attitude of a nanosatellite are compared in this study. The methods selected for comparison are: Single Value Decomposition (SVD), q method, Quaternion ESTimator (QUEST), Fast Optimal Attitude Matrix (FOAM) − all solving optimally the Wahba’s problem, and the algebraic method using only two vector measurements. For proper comparison, two sensors are chosen for the vector observations on-board: magnetometer and Sun sensors. Covariance results obtained as a result of using those methods have a critical importance for a non-traditional attitude estimation approach; therefore, the variance calculations are also presented. The examined methods are compared with respect to their root mean square (RMS) error and variance results. Also, some recommendations are given.

A mathematical analysis of world cup advertisements

Qualitative content analysis is the most common method to compare advertisements cross-culturally or cross-generationally. However, quantitative methods, such as chi-square or Fisher tests, can also be used. In this paper, we introduce results for Fisher tests, seriation and single-value decomposition that prove useful in determining similarities and differences in cultural appeals in advertising.