生物组织散射元平均间距估计的一种新方法

生物组织散射元平均间中划描述生物组织微观结构特性和生物组织超微散射特性的重要参数。本文在对生物组织超声背向散射随机模的基础上,提出了基于生物组织超声背向散射信号突变点检测的工用射元平均间距估计的新方法。该方法是生物组织超声散射分析的有效方法。     

Discrimination of red porgy Pagrus pagrus (Sparidae) potential stocks in the south-western Atlantic by otolith shape analysis

Otolith shape analysis is a powerful method for fish stock identification. We compared the otolith shape of Pagrus pagrus (Linnaeus 1758) along with its distribution in four south-western Atlantic regions where it is commercially fished: Rio de Janeiro, Rio Grande do Sul in southern Brazil, the Argentine-Uruguayan Common Fishing Zone (UA) and the Argentinian Exclusive Fishing Zone (AR). Otolith shapes were compared by Elliptical Fourier and Wavelet coefficients among specimens in a size range with similar otoliths, morphometric parameters and ages. Four potential stocks were identified: one in the AR, a second along the UA which included specimens from southern Brazil with well-marked opaque bands in its otoliths (MRS), the third in southern Brazil with faint or absent opaque bands in its otoliths (FRS) and the fourth along Rio de Janeiro. The difference in the otolith shape among regions followed differences reported using other stock identification techniques. The similarity between otoliths from UA and MRS (ANOVA-like, P > 0.01) can be explained by seasonal short-range migrations. Otoliths shape differences between MRS and FRS (ANOVA-like, P < 0.01) suggest that P. pagrus does not form a homogeneous group in southern Brazil.     

ROI-Based Compression Strategy of 3D MRI Brain Datasets for Wireless Communications

ObjectivesBecause of the large amount of medical imaging data, the transmission process becomes complicated in telemedicine applications. Thus, in order to adapt the data bit streams to the constraints related to the limitation of the bandwidths a reduction of the size of the data by compression of the images is essential. Despite the improvements in the field of compression, the transmission itself can also introduce errors. For this reason, it is important to develop an adequate strategy which will help reduce this volume of data without having to introduce some distortion and resist the errors introduced by the channel noise during transmission. Thus, in this paper, we propose a ROI-based coding strategy and unequal bit stream protection to meet this dual constraint.Material and methodsThe proposed ROI-based compression strategy with unequal bit stream protection is composed of three parts: the first one allows the extraction of the ROI region, the second one consists of a ROI-based coding and the third one allows an unequal protection of the ROI bit stream.First, the Regions Of Interest (ROI) are extracted by hierarchical segmentation of these regions according to a segmentation method based on the technique of Marker-based-watershed combined with the technique of active contours by level set. The resulting regions are selectively encoded by a 3D coder based on a shape adaptive discrete wavelet transform 3D-BISK, where the compression ratio of each region depends on its relevance in diagnosis. These obtained regions of interest are protected with an error-correcting code of Reed-Solomon type with a code rate that varies according to the relevance of the region by an unequal protection strategy (UEP).ResultsThe performance of the proposed compression scheme is evaluated in several ways. First, tests are performed to study the impact of errors on the different bit streams. In the first place, these tests are carried out in order to study the effect of the variation of the compression rates on the different bit streams. Secondly, different Reed Solomon error-correcting codes of different code rates are tested at different compression rates on a BSC channel. Finally, the performances of this coding strategy are compared with those of SPIHT 3D in the case of transmission on a BSC channel.ConclusionThe obtained results show that the proposed method is quite efficient in transmission time reduction. Therefore, our proposed scheme will reduce the volume of data without having to introduce some distortion and resist the errors introduced by the channel noise in the case of telemedicine.     

Empirical Mode Decomposition and Wavelet Transform Based ECG Data Compression Scheme

ObjectiveIn health-care systems, compression is an essential tool to solve the storage and transmission problems. In this regard, this paper reports a new electrocardiogram (ECG) data compression scheme which employs sifting function based empirical mode decomposition (EMD) and discrete wavelet transform.MethodEMD based on sifting function is utilized to get the first intrinsic mode function (IMF). After EMD, the first IMF and four significant sifting functions are combined together. This combination is free from many irrelevant components of the signal. Discrete wavelet transform (DWT) with mother wavelet ‘bior4.4’ is applied to this combination. The transform coefficients obtained after DWT are passed through dead-zone quantization. It discards small transform coefficients lying around zero. Further, integer conversion of coefficients and run-length encoding are utilized to achieve a compressed form of ECG data.ResultsCompression performance of the proposed scheme is evaluated using 48 ECG records of the MIT-BIH arrhythmia database. In the comparison of compression results, it is observed that the proposed method exhibits better performance than many recent ECG compressors. A mean opinion score test is also conducted to evaluate the true quality of the reconstructed ECG signals.ConclusionThe proposed scheme offers better compression performance with preserving the key features of the signal very well.     

Modified Fuzzy Q Learning Based Classifier for Pneumonia and Tuberculosis

This work proposes reinforcement learning for correctly identifying pneumonia and tuberculosis (TB) using a repository of X ray images. To our knowledge, this is a first attempt at employing reinforcement learning for pneumonia and TB classification. In particular, modified fuzzy Q learning (MFQL) algorithm in conjunction with wavelet based pre-processing has been used to build a classifier for identifying pneumonia and tuberculosis's severity. Proposed classifier is a self-learning one and uses pneumonia dataset (no pneumonia, mild pneumonia and severe pneumonia) and tuberculosis dataset (TB present, TB absent) samples to classify X ray images of subjects. Results indicate that MFQL based approach achieves high accuracy and fares much better over contemporary Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classifiers. Proposed classifier can be a useful tool for pneumonia and tuberculosis diagnosis in a practical setting.     

Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks

Alternative or complementary medicine emphasizes therapies that are claimed to improve quality of life, prevent disease, and address conditions that conventional medicine has limited success in curing. There are many techniques which are prevalent in many countries and these can cause harm if not scientifically evaluated. The objective of this paper is to validate the use of iridology to diagnose kidney abnormalities. Two subject groups were evaluated: one was 168 subjects free from kidney disease and the other was 172 subjects with chronic renal failure. The procedure to acquire, process and classify the iris images was designed in such a way that avoids any dependency on the iridologists by using wavelet analysis and Adaptive Neuro-Fuzzy Inference System. The results show a correct classification for both subjects with kidney problems and normal subjects of 82% and 93%, respectively. The proposed technique conducted on a systemic disease with ocular manifestations showed encouraging results. However, it is necessary to perform extensive studies with diseases that do not have ocular manifestations according to conventional medicine in order to validate iridology as a valid scientific technique.     

Vegetation responses to landscape structure at multiple scales across a Northern Wisconsin,USA, pine barrens landscape

Increasing awareness of the importance of scale and landscape structure to landscape processes and concern about loss of biodiversity has resulted in efforts to understand patterns of biodiversity across multiple scales. We examined plant species distributions and their relationships to landscape structure at varying spatial scales across a pine barrens landscape in northern Wisconsin, U.S.A. We recorded plant species cover in 1×1 m plots every 5 m along a 3575 m transect, along with variables describing macro- and micro-landscape structure. A total of 139 understory plant species were recorded. The distributions of many species appeared to be strongly associated with landscape structural features, such as distinct management patches and roads. TWINSPAN and detrended correspondence analysis (DCA) identified three groups of species that overlapped extensively in the ordination, possibly reflecting the relatively homogeneous nature of disturbance in the pine barrens landscape. Distribution of understory plants did not reflect all of the patch types we identified along the transect; plot ordination and classification resulted in three to five plot groups that differed in niche breadth. Wavelet transforms showed varying relationships between landscape features and plant diversity indices (Shannon–Weiner, Simpson's Dominance) at different resolutions. Wavelet variances indicated that patterns of Shannon diversity were dominated by coarse resolutions ranging from 900–1500 m, which may have been related to topography. Patterns of Simpson's Dominance were dominated by 700 m resolution, possibly associated with canopy cover. However, a strong correspondence between overstory patch type and diversity was found for several patch types at ranges of scales that varied by patch type. Effects of linear features such as roads were apparent in the wavelet transforms at resolutions of about 5–1000 m, suggesting roads may have an important impact on plant diversity at landscape scales. At broad scales, landscape context appeared more important to diversity than individual patches, suggesting that changes in structure at fine resolutions could alter overall diversity characteristics of the landscape. Therefore, a hierarchical perspective is necessary to recognize potential large-scale change resulting from small-scale activities.     

Spatial patterns on the sagebrush steppe/Western juniper ecotone

Analysis of the spatial patterns of woody plants is important to better understand the ecological processes that govern the worldwide expansion of woody plants across semi-arid ecosystems. Second-order characteristics of a marked spatial point pattern of western juniper (Juniperus occidentalis subsp. occidentalis) were analyzed using Ripley’s K-functions and the pair-correlation function g. The marked point process of crown diameters was produced via two-dimensional wavelet analysis of a fine scale aerial photograph at the woodland-steppe ecotone in the Reynolds Creek watershed in the Owyhee Mountains, southwestern Idaho. Colonization of J. occidentalis stems from mature juniper trees growing in rocky, fire resistant areas. Although these areas introduce components of natural heterogeneity within the landscape, the selected study area is situated within a single soil type, and we modeled the expansion of juniper plants into previously juniper-free sagebrush steppe as a homogeneous point process with constant intensity. Through this research we have identified two statistically significant spatial scales characteristic of J. occidentalis on the woodland/steppe ecotone: (1) We observed inhibition between J. occidentalis plants at distances <15 m, resulting in a regular pattern, rather than clumped or random. This short-distance inhibition can be attributed to competition for water and other resources. Recruitment of young J. occidentalis occurs significantly more often in a direction away from older plants, maximizing the utilization of water and light resources, and perpetuating the spread of the species into previously juniper-free shrub-steppe. (2) J. occidentalis on the ecotone exhibits significant clustering within a 30–60 m radius. Bivariate point pattern analyses provide evidence that, within a distance of 50–70 m, there is a spatial dependence in tree size such that medium trees are more likely than small trees to be close to large trees. We attribute these phenomena to the fact that juniper seeds are commonly dispersed by berry-eating birds with small territories (0.3–1 ha). Beyond a distance of 50–70 m, juniper plants are randomly distributed, suggesting that additional long-range seed dispersal processes are at work. We further acknowledge the importance of including a reference to spatial scale when formulating hypotheses in statistical analysis of spatio-temporal point patterns.     

基于小波特征分析的手指动作识别研究

目的:本文利用表面肌电(sEMG)信号来研究多种手指组合动作的识别问题。方法:在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机(SVM)分类算法来实现手指组合动作的识别。结果:动作识别率最高达到100%。结论:所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。