Deleterious effect of suboptimal diet on rest-activity cycle in Anastrepha ludens manifests itself with age.

Activity patterns and sleep-wake cycles are among the physiological processes that change most prominently as animals age, and are often good indicators of healthspan. In this study, we used the video-based high-resolution behavioral monitoring system (BMS) to monitor the daily activity cycle of tephritid fruit flies Anastrepha ludens over their lifetime. Surprisingly, there was no dramatic change in activity profile with respect to age if flies were consistently fed with a nutritionally balanced diet. However, if flies were fed with sugar-only diet, their activity profile decreased in amplitude at old age, suggesting that suboptimal diet affected activity patterns, and its detrimental effect may not manifest itself until the animal ages. Moreover, by simulating different modes of behavior monitoring with a range of resolution and comparing the resulting conclusions, we confirmed the superior performance of video-based monitoring using high-resolution BMS in accurately representing activity patterns in an insect model

Minería de datos con información de contexto para la clasificación de imágenes satelitalesData mining with context information for satellite image classifi cation

En este artículo se presenta un esquema de clasificación multi-modelos para imágenes satelitales apoyado con información de contexto con el que se mejora la precisión de una pre-clasificación obtenida con algoritmos paramétricos. El nuevo esquema utiliza una red semántica como representación de conocimiento que almacena patrones creados con un ensamble de árboles de decisión (alimentado con características espectrales, de textura y geométricas para describir a las regiones de interés) y por otro lado patrones espaciales creados a partir de una representación basada en grafos (con información de contexto a partir de relaciones espaciales entre las regiones de interés). Los resultados experimentales muestran que el esquema de clasificación propuesto mejora la precisión de la pre-clasificación de los algoritmos paramétricos al utilizar información de contexto.Abstract This paper presents a multi-model classification schema for satellite images supported with context information to enhance the accuracy of a pre-classification obtained with parametric algorithms. This new scheme uses a semantic network as knowledge representation that stores the patterns created with a decision tree ensemble (fed with spectral, texture and geometric descriptive characteristics to describe the regions of interest) and spatial patterns created with a graph-based representation (with context information obtained from spatial relations among regions of interest). Our experimental results show that the proposed classification scheme enhances the pre-classification accuracy obtained with parametric algorithms when we use context information

Improving Fingerprint Verification Using Minutiae Triplets

Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases

Recording Lifetime Behavior and Movement in an Invertebrate Model

Characterization of lifetime behavioral changes is essential for understanding aging and aging-related diseases. However, such studies are scarce partly due to the lack of efficient tools. Here we describe and provide proof of concept for a stereo vision system that classifies and sequentially records at an extremely fine scale six different behaviors (resting, micro-movement, walking, flying, feeding and drinking) and the within-cage (3D) location of individual tephritid fruit flies by time-of-day throughout their lives. Using flies fed on two different diets, full sugar-yeast and sugar-only diets, we report for the first time their behavioral changes throughout their lives at a high resolution. We have found that the daily activity peaks at the age of 15–20 days and then gradually declines with age for flies on both diets. However, the overall daily activity is higher for flies on sugar-only diet than those on the full diet. Flies on sugar-only diet show a stronger diurnal localization pattern with higher preference to staying on the top of the cage during the period of light-off when compared to flies on the full diet. Clustering analyses of age-specific behavior patterns reveal three distinct young, middle-aged and old clusters for flies on each of the two diets. The middle-aged groups for flies on sugar-only diet consist of much younger age groups when compared to flies on full diet. This technology provides research opportunities for using a behavioral informatics approach for understanding different ways in which behavior, movement, and aging in model organisms are mutually affecting

Minería de datos con información de contexto para la clasificación de imágenes satelitales / Data mining with context information for satellite image classifi cation

En este artículo se presenta un esquema de clasifi cación multi-modelos para imágenes satelitales apoyado con información de contexto conel que se mejora la precisión de una pre-clasifi cación obtenida conalgoritmos paramétricos. El nuevo esquema utiliza una red semántica como representación de conocimiento que almacena patrones creados con un ensamble de árboles de decisión (alimentado con características espectrales, de textura y geométricas para describir a las regiones de interés) y por otro lado patrones espaciales creados a partir de unarepresentación basada en grafos (con información de contexto a partir de relaciones espaciales entre las regiones de interés). Los resultados experimentales muestran que el esquema de clasifi cación propuesto mejora la precisión de la pre-clasifi cación de los algoritmos paramétricosal utilizar información de contexto

Human Gesture Recognition using Hidden Markov Models and Sensor Fusion

Considering the continued drive of human needs besides the constant improvement of technology, it is convenient to develop techniques that enhance the communication between computers and humans in the most intuitive ways as possible. The possibility to automatically recognize human gestures using artificial vision among other kind sensorsallows to explore a whole range of interaction applications to control and interact with environments. Nowadays, most of approaches for gesture recognition using sensors agree in the use of vision, myography and movement devices applied to robotic, medical and industrial applications. In the context of this work, we study the principles of using both vision andbody contact sensing applied to automatic classification of a human gesture set. For this, two different approaches are evaluated: Feed-forward Neural Networks and Hidden Markov Models. These models are studied and implemented for the recognition up to eight different human hand gestures commonly applied in collaborative robotics tasks. In our tests,we conclude the effectiveness of combining the information of two different sort of devices for human gesture recognition reaching accuracy rates up to 95.05% for a whole proposed Hand-gesture set

Shadow Effect for Small Insect Detection by W-Band Pulsed Radar

In radar entomology, one primary challenge is detecting small species (smaller than 5 cm) since these tiny insects reflect radiation that can be poorly observable and, therefore, difficult to interpret. After a literature search on radar entomology, this research found few works where it has been possible to sense insects with dimensions smaller than 5 cm using radars. This paper describes different methodologies to detect Mediterranean fruit flies with 5–6 mm sizes using a pulsed W-band radar and presents the experimental results that validate the procedures. The article’s main contribution is the successful detection of Mediterranean fruit flies employing the shadow effect on the backscattered radar signal, achieving an 11% difference in received power when flies are present. So far, according to the information available and the literature search, this work is the first to detect small insects less than 1 cm long using a pulsed radar in W-Band. The results show that the proposed shadow effect is a viable alternative to the current sensors used in smart traps, as it allows not only detection but also counting the number of insects in the trap

Deleterious effect of suboptimal diet on rest-activity cycle in Anastrepha ludens manifests itself with age.

Activity patterns and sleep-wake cycles are among the physiological processes that change most prominently as animals age, and are often good indicators of healthspan. In this study, we used the video-based high-resolution behavioral monitoring system (BMS) to monitor the daily activity cycle of tephritid fruit flies Anastrepha ludens over their lifetime. Surprisingly, there was no dramatic change in activity profile with respect to age if flies were consistently fed with a nutritionally balanced diet. However, if flies were fed with sugar-only diet, their activity profile decreased in amplitude at old age, suggesting that suboptimal diet affected activity patterns, and its detrimental effect may not manifest itself until the animal ages. Moreover, by simulating different modes of behavior monitoring with a range of resolution and comparing the resulting conclusions, we confirmed the superior performance of video-based monitoring using high-resolution BMS in accurately representing activity patterns in an insect model