Relevance of statistically significant differences betweenreconstruction algorithms

When comparing reconstruction algorithms, differences in figures of performance merit that are too small to be of any practical relevance may still be statistically significant. We formalize the notion of "relevance" and propose an evaluation methodology in which statistical significance is retained for relevant improvements, but not for irrelevant ones.     

探索性数据分析在LoRa通信SNR性能的研究

为了降低数据传输信噪比,提高传输质量,在实际部署过程中需合理配置LoRa物理层参数。为此,基于探索性数据分析,提出一种优化配置参数选择的预测模型。首先,设置物理层参数配置,收集数据;其次,利用探索性数据分析剔除数据中的异常值,并计算各配置参数和信噪比的相关性;最后,通过ID3决策树算法构建预测模型,建立配置参数和SNR之间的关系,以此提高传输信噪比,改善数据传输质量。实验结果表明,预测模型可以有效地对LoRa通信技术中信噪比性能进行优化。     

Tracking the right clues with exploratory data analysis

This alternative to traditional statistical analyses is guided by clues uncovered in the course of an investigation. Exploratory data analysis can be likened to the examination of a crime scene. Just as a good detective approaches an investigation with an open mind and as few preconceived notions as possible, so, too, should data analysis be guided by clues uncovered in the course of the investigation. In contrast, the traditional statistical approach may fail to recognize unexpected behavior or anomalous data if the method of analysis turns out inconsistent with the data. In computer-based exploratory data analysis (EDA), linked tools serve to analyze the data in an interactive, free-flowing process of discovery, without constraint to a preconceived path. Open-ended exploration along these lines is often more fruitful than simply pouring data into a fixed analysis path designed to spit out “the answer” or churn out statistical summaries. The article discusses the essential features of exploratory data analysis. The technique is illustrated with reference to an automotive engineering project     

Comparison of two exploratory data analysis methods for fMRI: unsupervised clustering versus independent component analysis

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised Clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.     

Mining three-dimensional anthropometric body surface scanning data for hypertension detection.

Hypertension is a major disease, being one of the top ten causes of death in Taiwan. The exploration of three-dimensional (3-D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a prediction model for hypertension using anthropometric body surface scanning data. This research adopts classification trees to reveal the relationship between a subject's 3-D scanning data and hypertension disease using the hybrid of the association rule algorithm (ARA) and genetic algorithms (GAs) approach. The ARA is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. The proposed approach was experimented and compared with a regular genetic algorithm in predicting a subject's hypertension disease. Better computational efficiency and more accurate prediction results from the proposed approach are demonstrated.     

Towards a semi-automatic method for the statistically rigorous ageing of the human face

Forensic age progression for the purpose of ageing a missing child is a discipline currently dominated by artistic methodologies. In order to improve on these techniques, a statistically rigorous approach to the ageing of the human face is presented. The technique is based upon a principal component analysis and involves the definition of an ageing direction through the model space, using an age-weighted combination of model parameters. Pose and expression compensation methods are also incorporated, allowing faces at a wide variety of pose orientations and expressions to be aged accurately. Near photo-quality images are obtained quickly and the resultant ageing effects are realistic and plausible. As a quantitative check of the results, the root mean square error is calculated between the shape vector of the aged face and that of the target face, as well as between the aged face and faces of different identity at the target age. In general, this error is found to be smaller between the aged face and the target face, indicating that the face successfully retains its identity as it is aged. As a further test of the basic plausibility of our results, a regression analysis is performed between the shape model parameters and the age of each subject, assuming a linear relationship. The coefficient of determination is calculated to be r²=0.68 and the relationship between the variables is found to be significant at a level >0.99 upon performance of a standard F-test     

Rhythmogram-based analysis for continuous electrographic data of the human brain

Ecologically relevant stimuli are rarely used in scientific studies because they are difficult to control. Instead, researchers employ simple stimuli with sharp boundaries (in space and time). Here, we explore how the rhythmogram can be used to provide much needed rigorous control of natural continuous stimuli like music and speech. The analysis correlates important features in the time course of stimuli with corresponding features in brain activations elicited by the same stimuli. Correlating the identified regularities of the stimulus time course with the features extracted from the activations of each voxel of a tomographic analysis of brain activity provides a powerful view of how different brain regions are influenced by the stimulus at different times and over different (user-selected) timescales. The application of the analysis to tomographic solutions extracted from magnetoencephalographic data recorded while subjects listen to music reveals a surprising and aesthetically pleasing aspect of brain function: an area believed to be specialized for visual processing is recruited to analyze the music after the acoustic signal is transformed to a feature map. The methodology is ideal for exploring processing of complex stimuli, e.g., linguistic structure and meaning and how it fails, for example, in developmental dyslexia.     

电磁频谱数据的关联规则挖掘

为更合理利用频谱资源以及更好地评估各类电磁环境,本文提出一种基于关联规则挖掘的频谱数据挖掘方案.该方案首先基于一般挖掘流程获取频谱数据中的有用信息,包括异常信息、底噪信息、占用度信息和预定时间功率信息等;再将频谱信息作为关联分析对象,通过构建关联库,构建模糊集,基于模糊关联规则挖掘算法对频谱信息进行系统性的分析.本文对传统的算子选择策略加以改进,使用大尺度参数改进模糊隶属函数.通过实测数据集的验证分析,实验结果表明,频谱信息的强关联规则能反映各种信息之间隐含的关联性以及各种信息出现的频次;基于频谱信息的关联规则挖掘能有效地简化频谱挖掘工作,通过各种信息的关联性可以通过分析一部分频谱信息而得到另外的频谱信息.频谱信息的关联规则可以用于进行电磁无线电环境的评估,选择合适的频谱信息该方案可以应用于各类电磁环境的评估.     

Modeling the sleep mode for cellular digital packet data

Cellular digital packet data (CDPD) utilizes sleep mode mechanism to conserve the power of mobile end systems (M-ESs). A timer called T203 determines how quick an M-ES enters the sleep mode, and another timer called T204 determines how often the M-ES wakes up. A large T204 value and a small T203 value can effectively reduce the power consumption at the cost of degrading the frame transmission performance, i.e., more lost frames, longer frame waiting times, and larger waiting variance. When the power consumption budget is determined in a CDPD system design, our study provides guidelines to determine the timer values as well as the buffer size to optimize the frame transmission performance     

Audio analysis of statistically instantaneous signals with mixed Gaussian probability distributions

In this article, a novel method is proposed to measure the separation qualities of statistically instantaneous audio signals with mixed Gaussian probability distributions. This study evaluates the impact of the Probability Distribution Function (PDF) of the mixed signals on the outcomes of both sub- and super-Gaussian distributions. Different Gaussian measures are evaluated by using various spectral-distortion measures. It aims to compare the different audio mixtures from both super-Gaussian and sub-Gaussian perspectives. Extensive computer simulation confirms that the separated sources always have super-Gaussian characteristics irrespective of the PDF of the signals or mixtures. The result based on the objective measures demonstrates the effectiveness of source separation in improving the quality of the separated audio sources.     

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.     

Cartographic visualization of human trajectory data: overview and analysis

With the prevalence of mobile computing systems and location based services, large amounts of spatio-temporal data are nowadays being collected, representing the mobility of people performing various activities. However, despite the increasing interest in the exploration of these data, there are still open challenges in various application contexts, e.g. related to visualisation and human–computer interaction. In order to support the extraction of useful and relevant information from the spatio-temporal and the thematic properties associated with human trajectories, it is crucial to develop and study adequate interactive visualisation techniques. In addition to the properties of the visualisations themselves, it is important to take into consideration the types of information present within the data and, more importantly, the types of tasks that a user might need to consider in order to achieve a given goal. The understanding of these factors may, in turn, simplify the development and the assessment of a given interactive visualisation. In this paper, we present and analyse the most relevant concepts associated to these topics. In particular, our analysis addresses the main properties associated with (human) trajectory data, the main types of visualisation tasks/objectives that the users may require in order to analyse that data and the high-level classes of techniques for visualising trajectory data. In addition, this paper also presents an overview on a user study, conducted in function of this analysis, to compare two classes of visualisation techniques, namely static maps and space-time cubes, regarding their adequacy in helping users completing basic visualisation tasks.     

用于智能电表数据分析的大数据平台

智能电网所产生的海量计量数据对数据存储和数据处理的系统架构提出了苛刻的性能要求,为此,文中提出了一种以内核、代理和客户端为主的模块化的大数据平台架构。所提议的大数据平台是基于Hadoop平台扩展,针对TB级别的静态智能电表数据集的存储和查询。实验结果表明,提议的大数据平台能够为电力行业提供一个具有竞争力且易于操作的大型能源数据管理和可视化知识平台,具有支持数据密集型决策的潜力。     

Algebraic immunity for cryptographically significant Boolean functions: analysis and construction

Recently, algebraic attacks have received a lot of attention in the cryptographic literature. It has been observed that a Boolean function f used as a cryptographic primitive, and interpreted as a multivariate polynomial over F/sub 2/, should not have low degree multiples obtained by multiplication with low degree nonzero functions. In this paper, we show that a Boolean function having low nonlinearity is (also) weak against algebraic attacks, and we extend this result to higher order nonlinearities. Next, we present enumeration results on linearly independent annihilators. We also study certain classes of highly nonlinear resilient Boolean functions for their algebraic immunity. We identify that functions having low-degree subfunctions are weak in terms of algebraic immunity, and we analyze some existing constructions from this viewpoint. Further, we present a construction method to generate Boolean functions on n variables with highest possible algebraic immunity /spl lceil/n/2/spl rceil/ (this construction, first presented at the 2005 Workshop on Fast Software Encryption (FSE 2005), has been the first one producing such functions). These functions are obtained through a doubly indexed recursive relation. We calculate their Hamming weights and deduce their nonlinearities; we show that they have very high algebraic degrees. We express them as the sums of two functions which can be obtained from simple symmetric functions by a transformation which can be implemented with an algorithm whose complexity is linear in the number of variables. We deduce a very fast way of computing the output to these functions, given their input.     

Mining discriminative spatial cues for aerial image quality assessment towards big data

Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.     

数据挖掘在人力资源信息分析中的应用

企业的竞争归根结底是人才的竞争,如何全面掌握和合理利用人才资源是公司人力资源管理的首要问题。本文首先简单介绍了数据挖掘技术的背景、常用技术及运作流程,在此基础上阐述了数据挖掘技术在人力资源信息系统中的应用。     

Co-clustering: a versatile tool for data analysis in biomedical informatics.

Co-clustering has not been much exploited in biomedical informatics, despite its success in other domains. Most of the previous applications were limited to analyzing gene expression data. We performed co-clustering analysis on other types of data and obtained promising results, as summarized in this paper.     

Zen and the art of reporting differences in data that are notstatistically significant

Professional communicators reporting the results of experiments often need to express the fact that the differences in the data were not statistically significant and that the null hypothesis could not be rejected. It is suggested that communicators may find it difficult to express these results partly because failing to reject a null hypothesis is not the same as accepting a null hypothesis. Writers may report failing to reject a null hypothesis in any of five ways: directly and briefly; directly with the exact level of statistical probability stated; directly with the confidence interval specified; directly with an explanation of not claiming causation; and directly with a discussion of possible reasons     

Blind source separation for ambulatory sleep recording.

This paper deals with the conception of a new system for sleep staging in ambulatory conditions. Sleep recording is performed by means of five electrodes: two temporal, two frontal and a reference. This configuration enables to avoid the chin area to enhance the quality of the muscular signal and the hair region for patient convenience. The electroencephalopgram (EEG), eletromyogram (EMG), and electrooculogram (EOG) signals are separated using the Independent Component Analysis approach. The system is compared to a standard sleep analysis system using polysomnographic recordings of 14 patients. The overall concordance of 67.2% is achieved between the two systems. Based on the validation results and the computational efficiency we recommend the clinical use of the proposed system in a commercial sleep analysis platform.