Visual sensor network based early onset disease detection for strawberry plants

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

Aiming at the complexity of the network transmission environment and the decentralization of the monitoring sites in the middle route of the South to North Water Diversion Project, this article presents a water conservancy monitoring architecture based on the visual sensor networks. First, a network assessment method is proposed to evaluate the sensor networks condition, and the encoder is adjusted with the result of the sensor networks condition evaluation. Then, based on distributed database architecture and visualization system construction, a three-level structure of the water conservancy monitoring system is designed and built. The experiment results show that the water conservancy monitoring system proposed in this article meets the transmission requirement under the complex sensor networks condition of water conservancy project. The water conservancy monitoring system has been applied to the middle route of the South to North Water Diversion Project, which achieves the needs of engineering applications.

Prevention of Neonatal Group B Streptococcal Disease

Recently much research has been conducted in visual sensor networks. Compared to traditional sensor networks, vision networks differ in various aspects such as the amount of data to be processed and transmitted, the requirements on quality-of-service, and the level of collaboration among the sensor nodes. This paper deals with sensor fusion on visual sensor networks. We focus here on methods for fusing data from various distributed sensors and present a generic framework for fusion on embedded sensor nodes. This paper extends our previous work on distributed smart cameras and presents our approach toward the transformation of smart cameras into a distributed, embedded multisensor network. Our generic fusion model has been completely implemented on a distributed embedded system. It provides a middleware which supports automatic mapping of our fusion model to the target hardware. This middleware features dynamic reconfiguration to support modification of the fusion application at runtime without loss of sensor data. The feasibility and reusability of the I-SENSE concept is demonstrated with experimental results of two case studies: vehicle classification and bulk good separation. Qualitative and quantitative benefits of multilevel information fusion are outlined in this article.

An initial diagnosis of Parkinson's disease (PD) is challenging, especially in patients who have early onset and atypical disease. A genetic etiology for parkinsonism, when established, ends that diagnostic odyssey and may inform prognosis and therapy. The objective of this study was to elucidate the genetic etiology of parkinsonism in patients with early onset disease (age at onset <45 years). Whole-exome sequencing, copy number variability, and short tandem repeat analyses were performed. The analyses were focused on genes previously implicated in parkinsonism and dystonia in patients with early onset parkinsonism. Genotype-phenotype correlations were assessed using regression models. The patient cohort was characterized by early onset, slowly progressive parkinsonism with a mean age at onset of 39.2 ± 5.0 years (n = 108). By 10 years of disease duration, the mean Hoehn & Yahr stage was 2.6 ± 0.8, the mean Unified Parkinson's Disease Rating Scale, part III (motor part) score was 24.9 ± 12.1 (n = 83), and 30 patients were cognitively impaired at the last examination (Montreal Cognitive Assessment score ≤ 26). Ten patients with typical early onset PD harbored homozygous or compound heterozygous mutations phosphatase and tensin homolog-induced putative kinase 1 (PINK1) (n = 4), parkin (PRKN) (n = 3), or the leucine-rich repeat kinase 2 (LRRK2) c.6055 G to A transition (n = 3). In addition, 5 patients with more atypical disease were compound heterozygotes for the glucocerebrosidase gene (GBA) (n = 3) 1 was heterozygous for solute carrier family 2, member 1 (SLC2A1) and another carried a novel ataxin 2 (ATXN2) exon 1 duplication. In most patients, the cumulative mutational burden did not appear to contribute to age at onset or progression. In this clinical series, 15 patients (14%) carried mutations that were linked to monogenic parkinsonism. GBA carriers were most likely to suffer an earlier cognitive demise. Nevertheless, the etiology for most patients with early onset PD remains to be determined.

Visual Sensor Networks (VSNs), formed by large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Sensor collaboration is essential to VSNs and normally performed among sensors having similar measurements. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. In this paper, we propose the concept of forming semantic neighbors, where collaboration is only performed among geographically-close nodes that capture similar images, thus requiring image comparison as a necessary step. To avoid large amount of data transfer, we propose feature-based image comparison as features provide more compact representation of the image. The paper studies several representative feature detectors and descriptors, in order to identify a suitable feature-based image comparison system for the resource-constrained VSN. We consider two sets of metrics from both the resource consumption and accuracy perspectives to evaluate various combinations of feature detectors and descriptors. Based on experimental results obtained from the Oxford dataset and the MSP dataset, we conclude that the combination of Harris detector and moment invariants presents the best balance between resource consumption and accuracy for semantic neighbor formation in VSNs.

A prediction-based approach for features aggregation in Visual Sensor Networks

Both angiotensin-converting enzyme genotype and plasminogen activator inhibitor type 1 genotype have an effect on fibrinolytic components and hence, may increase risk or advance occurrence of coronary heart disease. We examined the association of the angiotensin-converting enzyme and the plasminogen activator inhibitor type 1 genotypes, and their combinations, with early onset of coronary heart disease in a cohort of 907 patients with coronary heart disease. All patients with a coronary heart disease (International Classification of Diseases, 9th Rev. pos. 410-414), aged 30-70 years and participating in an inpatient rehabilitation program between January 1999 and May 2000 in two clinical centres in Germany were enrolled. The plasminogen activator inhibitor type 1 and the angiotensin-converting enzyme genotypes were determined by polymerase chain reaction, and the distribution was compared between patients with early (< or =55 years) and late (>55 years) onset of coronary heart disease. A multivariate analysis was employed to adjust for potentially confounding factors. Of the 907 included patients, 408 (45.0%) developed coronary heart disease before the age of 55 years. For the 4G/4G genotype of plasminogen activator inhibitor type 1 the odds ratio for early onset of coronary heart disease was 1.68 [95% confidence interval (CI) 1.01-2.57] and for the D/D genotype of angiotensin-converting enzyme the odds ratio was 1.22 (95% CI 0.84-1.76) after adjustment for covariates. In multivariate analysis an odds ratio of 3.10 (95% CI 1.51-6.36) was found for the association between the combined homozygosity for both polymorphisms (plasminogen activator inhibitor type 1 genotype 4G/4G and angiotensin-converting enzyme genotype D/D) and onset of coronary heart disease before the age of 55 years after controlling for sex, age, smoking, diabetes, hypertension, hyperlipidemia and school education. The co-existence of the 4G/5G polymorphism of the plasminogen activator inhibitor type 1 gene and the I/D polymorphism of the angiotensin-converting enzyme gene increases the risk for early onset of coronary heart disease in this population.

The last decade has seen a significant advance in understanding about early lung disease in cystic fibrosis (CF). As studies that have measured lung function in preschool years are conducted in association with surveillance of infection, inflammation and early structural changes, and emerging longitudinal data become available, a better insight into the very early onset and nature of such lung disease is emerging. Interventions during the preschool years are increasingly viewed as being crucial to delaying and minimizing disease progression as this is the most important period of postnatal life in terms of lung development and airway remodelling. Lung function measurement in CF is potentially an important assessment tool and is used in routine clinical practice in several centres already. Results of studies from lung function tests that, on the basis of their underpinning physiology, are viewed as being best suited currently for the early detection of lung disease in CF are reviewed.

Modern developments in image technology enabled easy access to an innovative type of sensor-based networks, Camera or Visual Sensor Networks (VSN). Nevertheless, more sensor data sources bring about the problem of overload information. To solve this problem, some researchers have been carried out on the techniques to counteract the data overload caused by sensors without losing useful data. The aim of fusion in each application is to combine images from several sensors, which leads to the decreased amount of input image data, producing an image with more accurate data. This paper proposes a noisy feature removal scheme for multi-focus image fusion combining the decision information of optimized individual features. The proposed scheme is developed in two main steps. In the first step, the diverse types of features are extracted from each block of input blurred images. The useful information of these individual features indicates which image block is more focused among corresponding blocks in source images. After that, noisy features are removed using binary Genetic Grey wolf optimizer (GGWO) algorithm. The ensemble decision based on individual features is employed to fuse blurred images in the second step. The experimentation is evaluated on different multi-focus images and it reveals that GGWO based proposed method performs better visual quality than other methods.

Wireless Sensor Network future direction is going towards more complex sensor such as camera sensor. Therefore, a very active research field is Visual Sensor Network. This type of network brings new challenges such as processing and transmitting a massive amount of data generated by the camera sensor. The efforts into decreasing the amount of data to be transmitted are going towards two directions: data encoding and data filtering. This chapter introduces an algorithm for each direction. Visual data encoding is performed by means of Predictive Video Encoding using Phase-Only Correlation function to achieve motion estimation. Visual data filtering is done at the lowest level of abstraction and is performed in three phases: pixel classification, background update and detection. The algorithms involved in each phase are light in terms of complexity and memory resources.

The wide availability of embedded sensor platforms and low-cost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multi-node processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

Although flexible neural networks (FNNs) have been used more successfully than classical neural networks (CNNs), nothing is rigorously known about their properties. In fact, they are not even well known to the systems and control community. In this paper, theoretical evidence is given for their superiority over CNNs. Following an overview of flexible bipolar sigmoid functions (FBSFs), several fundamental properties of feedforward and recurrent FNNs are established. For the feedforward case, it is proven that similar to CNNs, FNNs with as few as a single hidden layer (SHL) are universal approximators. It is also proven that unlike irreducible SHL CBSNNs, irreducible SHL FBSNNs are nonuniquely determined by their input-output (I-0) maps, up to a finite group of symmetries. Then, recurrent FNNs are introduced. It is observed that they can be interpreted as a generalization of the conventional state-space framework. For the recurrent case, it is substantiated that similar to CBSNNs, FBSNNs are universal approximators. Necessary and sufficient conditions for the controllability and observability of a generic class of them are established. For a subclass of this class, it is proven that unlike CBSNNs, FBSNNs are nonuniquely determined by their I-O maps, up to a finite group of symmetries, and that every system inside this subclass is minimal. Finally, a new class of FNNs, namely, flexible bipolar radial basis neural networks (FBRBNNs) is introduced. It is proven that as in the case of classical radial basis neural networks (CRBNNs), feedforward SHL FBRBNNs are universal approximators.

Wireless sensor networks are an important research area that has attracted a lot of attention in the past few years. Most of this attention, however, has focused on wireless sensor network collect scalar data such as temperature and vibration. Scalar data is insufficient for many applications such as video surveillance and traffic monitoring. Whereas, camera sensors collect video data, which are rich in information and offer tremendous potential when used in wireless sensor networks. Video sensor networks continue to gain increase interest due to their ability to collect video information for a wide range of applications in recent years. However, knowledge about these types of networks is mostly related to visual algorithms, leaving the issues such as the coverage of video sensor network and the transmission of video data aside. In this paper, we first highlight the issues and opportunities of video sensor network. And then we discuss key research issue on video sensor network, specifically camera coverage problem, network architecture, and low-power video data processing and communication. Finally, we identify enabling approaches in this area.

Nowadays visual sensor networks are used in emergency or time of surveillance of remote area where human reach is not possible. There situations arise when mobility of nodes is required in visual sensor network. In this paper we considers mobility of visual sensor nodes and tries to maximize the lifetime of network using priority if nodes. We use hierarchical and heterogeneous architecture for deploying nodes. In this we use two types of nodes. On this architecture we use prioritized scheduling technique to maximize the lifetime of network. Result shows lifetime is increased compare to the stationary nodes as after same time we get more live sensors than stationary system.