A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

Theory of edge perception for computer vision feedback control

In this paper, the authors address the problem of edge-perception for its applications to vision-feedback control in robotic systems.In natural vision, the recognition of objects takes place through the process consisting of eye system, neural networks and cognition. The cognitive process, in turn yields a phenomenon known as perception. This is the phenomenon of perception of physical attributes, such as edges, color and texture, etc., which is responsible for the recognition of objects through the natural vision processes.In this paper, we make an attempt to postulate the theory of perception for gray-level images. The gray-level images, when going through the cognitive and perception processes, are contaminated by the uncertainty; here we call it cognitive uncertainty.The studies in this paper are confined to the phenomenon of edge-perception for two-dimensional gray-level images, however, these studies can be extended to other types of visual attributes both in two-dimensional and three-dimensional spaces. Indeed, the perception of these attributes, which attempts to emulate the human vision system, may help in the design of a truly robust computer vision-feedback control system for robotic applications.     

基于最大加权独立集的频谱分配算法

动态频谱接入技术允许认知用户接入未授权的频谱,可以有效地提高频谱资源的利用率。频谱分配算法的时间开销和公平性是算法优劣的主要评价标准。本文从图论着色模型出发,构建了着色算法的评价体系及优化目标。针对用户间的公平性与分配的时间开销问题,在极大独立集的基础上提出了基于加权最大独立集的着色算法,获得了接近于最优的用户公平性,且该算法的时间开销等于信道数,与认知用户的数目无关。仿真分析验证了算法的正确性。     

基于层次认知模型的大学计算机课程多元混合教学模式设计与实施

根据计算思维理论中从知识到能力转变的递进过程,构建了基于层次认知模型的多元混合式教学课程体系和教学模式,并以大学计算机课程为例,以学生自主学习和协作学习为教学出发点,开发、整合出优质教学资源,设计了多样化教学活动,以培养学生综合应用计算机的软硬件知识解决本专业的实际问题为目的,最终应用到“SPOC+翻转课堂”混合教学中,达到教学目标。     

Effects of Berry Anthocyanins on Cognitive Performance,Vascular Function and Cardiometabolic Risk Markers: A Systematic Review of Randomized Placebo-Controlled Intervention Studies in Humans

Supplementation with anthocyanins, which are a type of flavonoids mainly found in various berries, is hypothesized to be a promising approach to lower the risk of developing cognitive decline. The aim of this systematic review was to provide a comprehensive overview of dietary intervention trials describing effects of berry anthocyanins on cognitive performance in humans, while also addressing potential underlying mechanisms. A total of 1197 articles were identified through a systematic search, and 49 studies reporting effects on cognitive performance (n = 18), vascular function (n = 22), or cardiometabolic risk markers (n = 32) were included. Significant improvements were observed on memory, while some of the studies also reported effects on attention and psychomotor speed or executive function. Vascular function markers such as brachial artery flow-mediated vasodilation were also affected and consistent evidence was provided for the beneficial effects of berry anthocyanins on endothelial function. Finally, studies reported improvements in blood pressure, but effects on metabolic risk markers (e.g. carbohydrate and lipid metabolism) were less consistent. In conclusion, this review provides evidence for the beneficial effects of berry anthocyanins on cognitive performance as memory improved. Whether observed anthocyanin-induced improvements in vascular function and blood pressure underlie beneficial effects on cognitive performance warrants further study.     

Performance appraisal of software testers

ContextTo determine the effectiveness of software testers a suitable performance appraisal approach is necessary, both for research and practice purposes. However, review of relevant literature reveals little information of how software testers are appraised in practice.Objective(i) To enhance our knowledge of industry practice of performance appraisal of software testers and (ii) to collect feedback from project managers on a proposed performance appraisal form for software testers.MethodA web-based survey with questionnaire was used to collect responses. Participants were recruited using cluster and snowball sampling. 18 software development project managers participated.ResultsWe found two broad trends in performance appraisal of software testers – same employee appraisal process for all employees and a specialized performance appraisal method for software testers. Detailed opinions were collected and analyzed on how performance of software testers should be appraised. Our proposed appraisal approach was generally well-received.ConclusionFactors such as number of bugs found after delivery and efficiency of executing test cases were considered important in appraising software testers’ performance. Our proposed approach was refined based on the feedback received.     

Computational Problem Solving in Spatial Substrates -- A Cognitive Systems Engineering Approach

The ability to perform spatial tasks is crucial for everyday life and of great importance to cognitive agents such as humans, animals, and autonomous robots. A common artificial intelligence approach to accomplish spatial tasks is to represent spatial configurations and tasks in form of detailed knowledge about various aspects of space and time. Suitable algorithms then use the representations to compute solutions to spatial problems. In comparison, natural embodied and situated agents often solve spatial tasks without detailed knowledge about geometric, topological, or mechanical laws; they directly relate actions to effects that are due to spatio-temporal affordances in their bodies and environments. Accordingly, we propose a paradigm that makes the spatio-temporal substrate an integral part of the engine that drives spatial problem solving. We argue that spatial and temporal structures in body and environment can substantially support (and even replace) reasoning effort in computational processes: physical manipulation and perception in spatial environments substitute formal computation. While the approach is well known – for example, we employ diagrams as spatial substrate for geometric problem solving and maps for wayfinding – the underlying principle has not been systematically investigated or formally analyzed as a paradigm of cognitive processing. Topology, distance, and orientation constraints are all integrated and interdependent in truly 2- or 3-dimensional space. Exploiting this fact may not only help overcome the need for acquiring detailed knowledge about the interrelationships between different aspects of space; it also can point to a way of avoiding exploding computational complexity that occurs when we deal with these aspects of space in complex real-world scenarios. Our approach employs affordance-based object-level problem solving to complement knowledge-level formal approaches. We will assess strengths and weaknesses of the new cognitive systems paradigm.     

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls (NC), 16 early MCI, and 17 early stage AD—are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.     

认知无线电网络信道交汇研究综述

认知无线电技术被认为是解决目前频谱资源利用率低下问题最有前景的技术,基于该技术,认知无线电网络采用动态频谱接入方式有效地提高了授权频段的利用率.然而,动态变化的信道可用性极大地增加了认知无线电网络组网的难度.信道交汇旨在为用户通信提供公共传输媒介,是实现无线网络组网的基础.介绍了认知无线电网络信道交汇的基本概念和特点,并阐述了信道交汇策略设计面临的挑战以及应考虑的性能指标.提出了信道交汇策略的分类标准和系统模型,根据该分类标准,详细剖析了当前信道交汇策略相关的研究工作.最后,讨论了认知无线电网络信道交汇研究的开放性问题,以期为未来的研究指出可能的方向和重点.     

基于循环自相关的NC-OFDM信号参数的盲估计

在认知无线电(CR)环境中,频率资源不足的问题越来越严重,非连续正交频分复用（NC-OFDM）能够工作在非连续的频谱环境,针对NC-OFDM信号的参数估计重要问题,提出用循环自相关的方法。该方法首先分析循环平稳信号特点,然后根据NC-OFDM信号的循环自相关在循环频率α以及时延τ切面图具有离散谱线特征进行参数估计,最后对NC-OFDM信号的循环自相关进行数值仿真。仿真结果表明在低信噪比下,能有效估计NC-OFDM信号的参数及识别零前缀的正交频分复用信号（ZP-OFDM）,且实验证明关闭近90%的子载波数目,都能够实现NC-OFDM信号的参数的盲估计。