Compressão auto-adaptativa de imagens coloridas

Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required

Uma investigação de algoritmos exatos e metaheurísticos aplicados ao nonograma

Nonogram is a logical puzzle whose associated decision problem is NP-complete. It has applications in pattern recognition problems and data compression, among others. The puzzle consists in determining an assignment of colors to pixels distributed in a N  M matrix that satisfies line and column constraints. A Nonogram is encoded by a vector whose elements specify the number of pixels in each row and column of a figure without specifying their coordinates. This work presents exact and heuristic approaches to solve Nonograms. The depth first search was one of the chosen exact approaches because it is a typical example of brute search algorithm that is easy to implement. Another implemented exact approach was based on the Las Vegas algorithm, so that we intend to investigate whether the randomness introduce by the Las Vegas-based algorithm would be an advantage over the depth first search. The Nonogram is also transformed into a Constraint Satisfaction Problem. Three heuristics approaches are proposed: a Tabu Search and two memetic algorithms. A new function to calculate the objective function is proposed. The approaches are applied on 234 instances, the size of the instances ranging from 5 x 5 to 100 x 100 size, and including logical and random Nonograms

Análise de desempenho de algoritmos de compressão de dados com perda para aplicações industriais

The great amount of data generated as the result of the automation and process supervision in industry implies in two problems: a big demand of storage in discs and the difficulty in streaming this data through a telecommunications link. The lossy data compression algorithms were born in the 90’s with the goal of solving these problems and, by consequence, industries started to use those algorithms in industrial supervision systems to compress data in real time. These algorithms were projected to eliminate redundant and undesired information in a efficient and simple way. However, those algorithms parameters must be set for each process variable, becoming impracticable to configure this parameters for each variable in case of systems that monitor thousands of them. In that context, this paper propose the algorithm Adaptive Swinging Door Trending that consists in a adaptation of the Swinging Door Trending, as this main parameters are adjusted dynamically by the analysis of the signal tendencies in real time. It’s also proposed a comparative analysis of performance in lossy data compression algorithms applied on time series process variables and dynamometer cards. The algorithms used to compare were the piecewise linear and the transforms.

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Avaliação da incontinência urinária de esforço em mulheres na pós-menopausa com e sem queixa de perda urinária através da aplicação do pad-test de 1 hora

Stress urinary incontinence (SUI) is defined as "involuntary loss of urine" due to several processes that alter the ability of the bladder to hold urine properly, regarded as a social and hygienic problem that adversely affects quality of life. In postmenopausal women, IU is associated with atrophy and weakness of the pelvic floor muscles. The objective this study was investigate, using the onehour pad test, stress urinary leakage (SUI), evaluate and compare their results in postmenopausal and premenopausal women. The survey was characterized as a cross-sectional study. The study consisted of 60 postmenopausal women were divided into GIU - consisting of 34 volunteers complaining of involuntary loss of urine during stress - and GSIU - consisting of 26 volunteers without complaints of loss of urine during stress, and 15 women, during the premenopausal (GPM), and ovulatory with normal menstrual cycle. All volunteers were evaluated clinically, subjected to one-hour pad test, after the biochemical evaluation of blood and sex hormones. Statistical analysis was performed by descriptive analysis, ANOVA, Turkey´s post-test and Pearson correlation. The results showed that 100% of postmenopausal patients had involuntary loss of urine during one hour pad test (GIU: 4.0 g; GSIU: 4.5 g). GPM remained continent after an hour pad test (GPM: 0.4 g). In addition, Pearson showed a strong correlation between urine loss with time since menopause (r = 0.8, p <0.01) and body mass index - BMI (r = 0.7; p = 0.01). These data suggest that the one-hour pad test is a useful test to assess and quantify urinary leakage, including those volunteers who had no previous complaint of SUI

When wolves cry: long distance calling by wild maned wolves

The maned wolf (Chrysocyon brachyurus Illiger 1815) is the biggest canid in South America and it is considered a “near threatened” species by IUCN. Because of its nocturnal, territorial and solitary habits, there are still many understudied aspects of their behavior in natural environments, including acoustic communication. In its vocal repertoire, the wolf presents a longdistance call named “roar-bark” which, according to literature, functions for spacing maintenance between individuals and/or communication between members of the reproductive pair inside the territory. In this context, this study aimed: 1) to compare four methods for detecting maned wolf’s roar-barks in recordings made in a natural environment, in order to elect the most efficient one for our project; 2) to understand the night emission pattern of these vocalizations, verifying possible weather and moon phases influences in roarbark’s emission rates; and 3) to test Passive Acoustic Monitoring as a tool to identify the presence of maned wolves in a natural environment. The study area was the Serra da Canastra National Park (Minas Gerais, Brazil), where autonomous recorders were used for sound acquisition, recording all night (from 06pm to 06am) during five days in December/2013 and every day from April to July/2014. Roar-barks’ detection methods were tested and compared regarding time needed to analyze files, number of false positives and number of correctly identified calls. The mixed method (XBAT + manual) was the most efficient one, finding 100% of vocalizations in almost half of the time the manual method did, being chosen for our data analysis. By studying roarbarks’ temporal variation we verified that the wolves vocalize more in the early hours of the evening, suggesting an important social function for those calls at the beginning of its period of most intense activity. Average wind speed negatively influenced vocalization rate, which may indicate lower sound reception of recorders or a change in behavioral patterns of wolves in high speed wind conditions. A better understanding of seasonal variation of maned wolves’ vocal activity is required, but our study already shows that it is possible to detect behavioral patterns of wild animals only by sound, validating PAM as a tool in this species’ conservation.

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.