Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy to use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

Adaptive Steganography technique using DIDC Model

Digital media emergence has taken the world by storm, further with the growth of digital media also causes the high risk of security and it needs to be addressed. Data security is protecting the data, normally data protecting is parted into two category i.e. cryptography and stenography. Steganography provides the high-level security by hiding one data under the other; the data can be image, text or any other formats, image steganography is one of the highly research area and several researcher have proposed different technique. The main challenge in image steganography is to innocuous image without any suspicion, furthermore the existing steganography focuses only on minimizing the distortion function. In this paper, we aim to develop an adaptive steganography technique named DIDC, which is basically based on the DPEs that approaching the rate transformation bound under the Steganography algorithm. Furthermore, the DIDC is evaluated by considering the two feature set SRM and max SRMd2 and error detection rate as the parameter, the comparison analysis shows that DIDC model outperforms not only state-of-art but also existing NFM model. Further, we also plot the AUC and the observation suggest the remarkable result.

Fabric defect detection using the improved YOLOv3 model

To improve the detection rate of defect and the fabric product quality, a higher real-time performance fabric defect detection method based on the improved YOLOv3 model is proposed. There are two key steps: first, on the basis of YOLOv3, the dimension clustering of target frames is carried out by combining the fabric defect size and k-means algorithm to determine the number and size of prior frames. Second, the low-level features are combined with the high-level information, and the YOLO detection layer is added on to the feature maps of different sizes, so that it can be better applied to the defect detection of the gray cloth and the lattice fabric. The error detection rate of the improved network model is less than 5% for both gray cloth and checked cloth. Experimental results show that the proposed method can detect and mark fabric defects more effectively than YOLOv3, and effectively reduce the error detection rate.

Implementation of High Security Cryptographic System with Improved Error Correction and Detection Rate using FPGA

In this article, an encryption algorithm with an error detection technique is presented for highly secured reliable data transmission over unreliable communication channels. In this algorithm, an input data is mapped into orthogonal code first. After that the code is encrypted with the help of Linear Feedback Shift Register (LFSR). The technique has been successfully verified and synthesized using Xilinx by Spartan-3E FPGA. The results show that the error detection rate has been increased to 100% by proposed encryption scheme is effective and improves bandwidth efficiency.

Implementation of High Security Cryptographic System with Improved Error Correction and Detection Rate using FPGA

In this article, an encryption algorithm with an error detection technique is presented for highly secured reliable data transmission over unreliable communication channels. In this algorithm, an input data is mapped into orthogonal code first. After that the code is encrypted with the help of Linear Feedback Shift Register (LFSR). The technique has been successfully verified and synthesized using Xilinx by Spartan-3E FPGA. The results show that the error detection rate has been increased to 100% by proposed encryption scheme is effective and improves bandwidth efficiency.

Multi-Layer Model for Network Fault Detection Based on Artificial Immune

In order to reduce the fault detection rate and improve the self-adaptive capability in network fault detection, an artificial immune mechanism which is inspired by multi-layer defense of a biological immune system is proposed to perform network fault detection. The immune model is composed of three parts: the inherent detection layer, fuzzy judgment layer and adaptive detection layer. Dendritic cells can influence the reaction of coordinating T-cells, which can be activate or tolerate so that induce adaptive immune responses and affirm the type of adaptive response. Inherent detection layer and fuzzy judgment layer interact with each other to reduce the error detection rate, while the adaptive detection layer is capable of learning unknown fault patterns.

Review of Tech-Check-Tech

Objective: To explore the legal implications of the empiric literature and relevant legal literature on tech-check-tech. Methods: Two reviews were made: one of the states' and US holdings' laws regarding technician credentialing and tech-check-tech, and another of the empiric literature on tech-check-tech. Data Sources/Selection: The International Pharmacy Abstracts database and PubMed were searched for articles using the following key words: Tech* & accuracy, Tech* & check, Tech* & error, and tech-check-tech. When searching PubMed, the term “pharmacy” was added in each search. This search was verified July 2, 2002, to ensure that no articles were missed. Full articles and meeting abstracts were included if they contained data that allowed for comparisons between pharmacists' and technicians' accuracy or error rates in checking. Primary, secondary, and tertiary sources were used. All articles included were searched on the Web of Science to find other relevant articles. The bibliographies of the included articles were also checked. Data Synthesis: Thirty-five of the 53 US states and holdings (66%) either license, register, certify, require Pharmacy Technician Certification Board certification, and/or have technician training or educational requirements. Four states (7.5%) allow tech-check-tech in some form. Ten empiric studies were found that investigated tech-check-tech. The error detection rate was available in only 2 of the studies and showed nearly the same rates (technicians 97%, pharmacists 94%). Conclusions: This work found no evidence suggesting that tech-check-tech should not be adopted for unit-dose cart fill in every state. The credentialing of technicians varies greatly from state to state. Tech-check-tech procedures are sparsely used in the US. Training may improve accuracy, and the average error detection rate was higher for technicians than for pharmacists.

The effect of pretest method on error detection rates

Although pretesting is an essential part of the questionnaire design process, the range of methodological work on pretesting issues is limited. The present paper concentrates on the effect of the pretest survey method on error detection by contrasting respondents who are interviewed personally with those who receive an impersonal survey method. The interaction between survey method and respondent knowledge of the questionnaire topic is also considered. The findings show that the pretest method does have an effect on the error detection rate of respondents; however, the hypothesised interaction between method and knowledge was not unequivocally supported. The detailed results illustrate which error types are affected by the method used during pretesting. Implications for future research are considered.