Adaptive and reconfigurable data fusion architectures in positioning navigation systems

Dans les systèmes de positionnement de véhicules, à tout moment, n'importe lequel des détecteurs peut, temporairement ou de manière permanente, tomber en panne ou cesser d'envoyer des informations. Il s'ensuit alors des répercussions sur la sécurité, la santé, ainsi que des informations financières ou même légales. Bien que les nouvelles pratiques de conception aient tendance à réduire au minimum les défaillances des détecteurs, il est reconnu que de tels évènements peuvent quand même souvenir. Dans un tel cas, le détecteur défectueux doit être identifié et isolé afin d'éviter de corrompre les évaluations globales et, finalement, le système doit être capable de se reconfigurer afin de surmonter le carence causée par la défaillance. En bref, un système de navigation doit être robuste et adaptatif. Cette thèse propose plusieurs architectures de fusion de données capables de s'adapter suite à des défaillances de détecteurs. Les diverses approches utilisent un filtre Kalman en combinaison avec la détection de défauts pour produire des modules de positionnement robuste. Les modules devront être capables de fonctionner dans des situations telles que l'entrée GPS est corrompue ou non disponible, ou bien qu'un plusieurs détecteurs de position sont défectueux ou bloqués. Le principe de travail vise la modification des gains du filtre Kalman en se basant sur les erreurs normalisées entre les états estimés et les observations. Pour évaluer l'architecture proposée, divers défauts de détecteurs et diverses dégradations de performance ont été mis en oeuvre et simulés. Les expériences démontrent que les solutions proposées peuvent compenser la plupart des erreurs associées aux défauts des détecteurs ou aux dégradations de performance, et que l'exactitude de positionnement qui en découle est améliorée significativement

EEG-based emotion classification using spiking neural networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications

EEG-based emotion classification using a deep neural network and sparse autoencoder

Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN

Müller Cell Regulated Microglial Activation and Migration in Rats With N-Methyl-N-Nitrosourea-Induced Retinal Degeneration

During the pathogenesis of retinitis pigmentosa (RP), the roles of retinal microglial cells after activation have not been fully elucidated. Herein, experimental RP was induced in Sprague Dawley rats by intraperitoneal injection of N-methyl-N-nitrosourea (MNU) at 50 mg/kg, and the effects of MNU on the retinas were evaluated, respectively, by retinal histology and electroretinography recordings at serial time points. Time-dependent and gradual loss of photoreceptor cells, disrupted arrangement of the outer nuclear layer (ONL), and significant reductions in both a-wave and b-wave amplitudes were observed. Morphology changes were observed in retinal microglial cells; meanwhile, with time, the number of Iba1-positive microglia and their infiltration into the ONL gradually increased. Furthermore, physical interaction of microglial-Müller cell processes following microglial activation was observed after MNU injection. In addition, Müller cells increased CX3CL1 secretion, enhanced microglial cell migration, and upregulated the CX3CR1 expression of the latter. Our observations implied that, during the pathogenesis of RP by MNU, microglial cells exhibit a prominent morphology change and Müller cells can induce activated microglia infiltration by increasing secretion of the chemotaxis factor, CX3CL1, and promoting the migration of retinal microglial cells. This novel finding highlights a potential therapeutic target aimed at regulating the microglial response

Mechanistic insights into inositol-mediated rumen function promotion and metabolic alteration using in vitro and in vivo models

Inositol is a bioactive factor that is widely found in nature; however, there are few studies on its use in ruminant nutrition. This study investigated the effects of different inositol doses and fermentation times on rumen fermentation and microbial diversity, as well as the levels of rumen and blood metabolites in sheep. Rumen fermentation parameters, microbial diversity, and metabolites after different inositol doses were determined in vitro. According to the in vitro results, six small-tailed Han sheep fitted with permanent rumen fistulas were used in a 3 × 3 Latin square feeding experiment where inositol was injected into the rumen twice a day and rumen fluid and blood samples were collected. The in vitro results showed that inositol could increase in vitro dry matter digestibility, in vitro crude protein digestibility, NH3-N, acetic acid, propionic acid, and rumen microbial diversity and affect rumen metabolic pathways (p < 0.05). The feeding experiment results showed that inositol increased the blood concentration of high-density lipoprotein and IgG, IgM, and IL-4 levels. The rumen microbial composition was significantly affected (p < 0.05). Differential metabolites in the rumen were mainly involved in ABC transporters, biotin metabolism, and phenylalanine metabolism, whereas those in the blood were mainly involved in arginine biosynthesis and glutathione and tyrosine metabolism. In conclusion, inositol improves rumen function, affects rumen microorganisms and rumen and blood metabolites and may reduce inflammation, improving animal health

Adaptive and reconfigurable data fusion architectures in positioning navigation systems

Dans les systèmes de positionnement de véhicules, à tout moment, n'importe lequel des détecteurs peut, temporairement ou de manière permanente, tomber en panne ou cesser d'envoyer des informations. Il s'ensuit alors des répercussions sur la sécurité, la santé, ainsi que des informations financières ou même légales. Bien que les nouvelles pratiques de conception aient tendance à réduire au minimum les défaillances des détecteurs, il est reconnu que de tels évènements peuvent quand même souvenir. Dans un tel cas, le détecteur défectueux doit être identifié et isolé afin d'éviter de corrompre les évaluations globales et, finalement, le système doit être capable de se reconfigurer afin de surmonter le carence causée par la défaillance. En bref, un système de navigation doit être robuste et adaptatif. Cette thèse propose plusieurs architectures de fusion de données capables de s'adapter suite à des défaillances de détecteurs. Les diverses approches utilisent un filtre Kalman en combinaison avec la détection de défauts pour produire des modules de positionnement robuste. Les modules devront être capables de fonctionner dans des situations telles que l'entrée GPS est corrompue ou non disponible, ou bien qu'un plusieurs détecteurs de position sont défectueux ou bloqués. Le principe de travail vise la modification des gains du filtre Kalman en se basant sur les erreurs normalisées entre les états estimés et les observations. Pour évaluer l'architecture proposée, divers défauts de détecteurs et diverses dégradations de performance ont été mis en oeuvre et simulés. Les expériences démontrent que les solutions proposées peuvent compenser la plupart des erreurs associées aux défauts des détecteurs ou aux dégradations de performance, et que l'exactitude de positionnement qui en découle est améliorée significativement

Adaptive and Reconfigurable Data Fusion Architectures in Vehicle Positioning Navigation Systems

ABSTRACT In positioning navigation systems, at any time, any of the sensors can break down or stop sending information, temporarily or permanently. To ensure a practical solution for use in guidance and navigation systems, faulty sensors must be detected and isolated such that their erroneous data will not corrupt the global position estimates. It is well known that Kalman filter is usually being used for data fusion applications. An interesting novel alternative is to use it for fault detection architecture as well. This paper describes the research conducted to evaluate the potential of combining fault detection and data fusion into a single architecture to make a robust positioning navigation system

Molecular Mechanism Underlying Lymphatic Metastasis in Pancreatic Cancer

As the most challenging human malignancies, pancreatic cancer is characterized by its insidious symptoms, low rate of surgical resection, high risk of local invasion, metastasis and recurrence, and overall dismal prognosis. Lymphatic metastasis, above all, is recognized as an early adverse event in progression of pancreatic cancer and has been described to be an independent poor prognostic factor. It should be noted that the occurrence of lymphatic metastasis is not a casual or stochastic but an ineluctable and designed event. Increasing evidences suggest that metastasis-initiating cells (MICs) and the microenvironments may act as a double-reed style in this crime. However, the exact mechanisms on how they function synergistically for this dismal clinical course remain largely elusive. Therefore, a better understanding of its molecular and cellular mechanisms involved in pancreatic lymphatic metastasis is urgently required. In this review, we will summarize the latest advances on lymphatic metastasis in pancreatic cancer

Diallyl disulfide suppresses SRC/Ras/ERK signaling-mediated proliferation and metastasis in human breast cancer by up-regulating miR-34a.

Diallyl disulfide (DADS) is one of the major volatile components of garlic oil. DADS has various biological properties, including anticancer, antiangiogenic, and antioxidant effects. However, the anticancer mechanisms of DADS in human breast cancer have not been elucidated, particularly in vivo. In this study, we demonstrated that the expression of miR-34a was up-regulated in DADS-treated MDA-MB-231 cells. miR-34a not only inhibited breast cancer growth but also enhanced the antitumor effect of DADS, both in vitro and in vivo. Furthermore, Src was identified as a target of miR-34a, with miR-34a inhibiting SRC expression and consequently triggering the suppression of the SRC/Ras/ERK pathway. These results suggest that DADS could be a promising anticancer agent for breast cancer. miR-34a may also demonstrate a potential gene therapy agent that could enhance the antitumor effects of DADS