Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Towards the design of efficient nonbeacon-enabled ZigBee networks

This paper presents experimental results of the communication performance evaluation of a prototype ZigBee-based patient monitoring system commissioned in an in-patient floor of a Portuguese hospital (HPG – Hospital Privado de Guimar~aes). Besides, it revisits relevant problems that affect the performance of nonbeacon-enabled ZigBee networks. Initially, the presence of hidden-nodes and the impact of sensor node mobility are discussed. It was observed, for instance, that the message delivery ratio in a star network consisting of six wireless electrocardiogram sensor devices may decrease from 100% when no hidden-nodes are present to 83.96% when half of the sensor devices are unable to detect the transmissions made by the other half. An additional aspect which affects the communication reliability is a deadlock condition that can occur if routers are unable to process incoming packets during the backoff part of the CSMA-CA mechanism. A simple approach to increase the message delivery ratio in this case is proposed and its effectiveness is verified. The discussion and results presented in this paper aim to contribute to the design of efficient networks,and are valid to other scenarios and environments rather than hospitals.

Stress affects theta activity in limbic networks and impairs novelty-induced exploration and familiarization

Exposure to a novel environment triggers the response of several brain areas that regulate emotional behaviors. Here, we studied theta oscillations within the hippocampus (HPC)-amygdala (AMY)-medial prefrontal cortex (mPFC) network in exploration of a novel environment and subsequent familiarization through repeated exposures to that same environment; in addition, we assessed how concomitant stress exposure could disrupt this activity and impair both behavioral processes. Local field potentials were simultaneously recorded from dorsal and ventral hippocampus (dHPC and vHPC respectively), basolateral amygdala (BLA) and mPFC in freely behaving rats while they were exposed to a novel environment, then repeatedly re-exposed over the course of 3 weeks to that same environment and, finally, on re-exposure to a novel unfamiliar environment. A longitudinal analysis of theta activity within this circuit revealed a reduction of vHPC and BLA theta power and vHPC-BLA theta coherence through familiarization which was correlated with a return to normal exploratory behavior in control rats. In contrast, a persistent over-activation of the same brain regions was observed in stressed rats that displayed impairments in novel exploration and familiarization processes. Importantly, we show that stress also affected intra-hippocampal synchrony and heightened the coherence between vHPC and BLA. In summary, we demonstrate that modulatory theta activity in the aforementioned circuit, namely in the vHPC and BLA, is correlated with the expression of anxiety in novelty-induced exploration and familiarization in both normal and pathological conditions.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Remote Patient Monitoring Based on ZigBee: Lessons from a Real-World Deployment

AIM: This work presents detailed experimental performance results from tests executed in the hospital environment for Health Monitoring for All (HM4All), a remote vital signs monitoring system based on a ZigBee® (ZigBee Alliance, San Ramon, CA) body sensor network (BSN). MATERIALS AND METHODS: Tests involved the use of six electrocardiogram (ECG) sensors operating in two different modes: the ECG mode involved the transmission of ECG waveform data and heart rate (HR) values to the ZigBee coordinator, whereas the HR mode included only the transmission of HR values. In the absence of hidden nodes, a non-beacon-enabled star network composed of sensing devices working on ECG mode kept the delivery ratio (DR) at 100%. RESULTS: When the network topology was changed to a 2-hop tree, the performance degraded slightly, resulting in an average DR of 98.56%. Although these performance outcomes may seem satisfactory, further investigation demonstrated that individual sensing devices went through transitory periods with low DR. Other tests have shown that ZigBee BSNs are highly susceptible to collisions owing to hidden nodes. Nevertheless, these tests have also shown that these networks can achieve high reliability if the amount of traffic is kept low. Contrary to what is typically shown in scientific articles and in manufacturers' documentation, the test outcomes presented in this article include temporal graphs of the DR achieved by each wireless sensor device. CONCLUSIONS: The test procedure and the approach used to represent its outcomes, which allow the identification of undesirable transitory periods of low reliability due to contention between devices, constitute the main contribution of this work.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.