Image-based control for cable-based robots

Some human robot interactive applications involved in tele-robotics, remote supervisory and unmanned systems require specific capabilities. This demand has promoted various interactive modes and high-level control techniques such as tele-manipulation, speech, vision, gesture, etc. Among these interactive modes, the image based control which is often named point and click control has proven to be the most appropriate one that offers multiple advantages. This mode consists of only and simply pointing in an appearing object of an image received from a remote site, to convert this click into a robot command towards the corresponding location in the real world space. This mode is suitable for remote applications, frees the human operator from being involved into the loop enabling him/her to use commands in the sense of click and forget. This paper presents, firstly, the design and the realization of an experimental planar cable-based robot constituted of four cables. Secondly, it presents the design and the implementation of a high-level image-based control. Some typical experiments which have been performed prove the simplicity and the effectiveness of the image-based control. Moreover, it opens perspectives for new applications with cable-based robots, particularity for rehabilitation applications.     

Some investigations into the optimal dimensional synthesis of parallel robots

In this paper, we will perform a comparison between two approaches of dimensional synthesis of parallel robots. The first one concerns the single-objective optimization approach; in this case, the dimensional synthesis is expressed by taking into account only one performance criterion but enables to get a final solution if it exists. The second one concerns the multi-objective optimization approach; it enables to simultaneously take into account several performance criteria. However, this approach appears to provide a set of solutions instead of a single expected final solution which should directly enable to carry out the structural synthesis. In fact, the search of a single final solution is postponed to a further step where the designers have to impose and/or restrict certain parameters. And we will establish if it is really necessary to make a multi-objective optimization approach or if a single-objective is sufficient to reach the objectives set in the specifications (user requirements). A discussion is proposed concerning the arising questions related to each approach and leading to the optimal dimensional synthesis. The PAR2 robot with two degree-of-freedom is used to exemplify the analysis and the comparison of the two approaches. The proposed comparison can be applied to any classes of parallel robots.     

Quasi-maximum likelihood estimation of periodic GARCH and periodic ARMA-GARCH processes

Abstract.  This article establishes the strong consistency and asymptotic normality (CAN) of the quasi-maximum likelihood estimator (QMLE) for generalized autoregressive conditionally heteroscedastic (GARCH) and autoregressive moving-average (ARMA)-GARCH processes with periodically time-varying parameters. We first give a necessary and sufficient condition for the existence of a strictly periodically stationary solution of the periodic GARCH (PGARCH) equation. As a result, it is shown that the moment of some positive order of the PGARCH solution is finite, under which we prove the strong consistency and asymptotic normality of the QMLE for a PGARCH process without any condition on its moments and for a periodic ARMA-GARCH (PARMA-PGARCH) under mild conditions.     

Projected block-wise SIC detection for communication systems with non-negativity constraints

Photonic Network Communications - Recently, a number of projected successive interference cancellation (PSIC) and projected parallel interference cancellation (PPIC) structures, which make use of...     

Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

Ecosystem matters: fish consumption, mercury intake and exposure among fluvial lake fish-eaters

Many studies use the number of fish meals as an estimate of Hg intake, although fish Hg concentrations, even within the same species, can greatly vary. Furthermore, most freshwater advisories only refer to local catch, while market fish advisories only focus on market fish, although both can contribute to Hg body burden. The present study, carried out in lakeside communities from 2 ecosystems in Quebec, Canada, sought to (i) estimate Hg intake from local freshwater sources, hunted waterfowl and market fish and seafood, and (ii) examine the relations between fish consumption, estimated Hg intake and biomarkers of exposure. A total of 238 adults (18-74 years), who had consumed local catch within the past three months, responded to an extensive interview-administered fish and waterfowl frequency questionnaire. Anthropometric measures were taken and a self-administered questionnaire was used to obtain socio-demographic information. Hg intake was estimated as µg Hg/kg body weight/day. Blood and hair samples were analyzed for Hg content. Results showed that persons from one ecosystem ate significantly more fish compared to those from the other (median: 52.1 g/day vs 38.9 g/day), but presented significantly lower concentrations of hair Hg (median: 448.0 ng/g vs 730.5 ng/g), blood organic Hg (median: 1.1 µg/L vs 3.4 µg/L) and inorganic Hg (median: 0.4 µg/L vs 0.8 µg/L). Median daily total Hg intake was 0.080 µg/kg bw/day for the former community and 0.141 µg/kg bw/day for the latter. Overall, 59.5% from the first ecosystem and 41.0% from the other, exceeded the US EPA RfD (0.1 µg/kg bw/day), while 13.2% and 6.0%, respectively, exceeded the Canadian tolerable daily intake (0.47 µg/kg bw/day) for adults. For the two groups, freshwater fish consumption frequency, but not total fish, was positively associated with bioindicators of Hg while estimated Hg intake from freshwater catch as well as from total fish consumption were positively related to Hg biomarkers. There was a positive relation between consumption and estimated Hg intake from freshwater fish and blood inorganic Hg. These findings indicate that the number of fish can be a poor surrogate for Hg exposure. The differences observed here for Hg intake and exposure reflect ecosystem disparities in fish diversity and Hg bioaccumulation. Studies and advisories need to consider Hg fish concentrations and fish-eating patterns in different ecosystems, as well as the contribution of market fish. The relation between fish consumption and inorganic Hg exposure, reported as well in other studies, needs to be further investigated.     

Deep Learning for Tomato Diseases: Classification and Symptoms Visualization

Several studies have invested in machine learning classifiers to protect plants from diseases by processing leaf images. Most of the proposed classifiers are trained and evaluated with small datasets, focusing on the extraction of hand-crafted features from image to classify the leaves. In this study, we have used a large dataset compared to the state-of-the art. Here, the dataset contains 14,828 images of tomato leaves infected with nine diseases. To train our classifier, we have introduced the Convolutional Neural Network (CNN) as a learning algorithm. One of the biggest advantages of CNN is the automatic extraction of features by processing directly the raw images. To analyze the proposed deep model, we have used visualization methods to understand symptoms and to localize disease regions in leaf. The obtained results are encouraging, reaching 99.18% of accuracy, which ourperforms dramatically shallow models, and they can be used as a practical tool for farmers to protect tomato against disease.     

Particle swarm optimization for bitmap join indexes selection problem in data warehouses

Data warehouses are very large databases usually designed using the star schema. Queries defined on data warehouses are generally complex due to join operations involved. The performance of star schema queries in data warehouses is highly critical and its optimization is hard in general. Several query performance optimization methods exist, such as indexes and table partitioning. In this paper, we propose a new approach based on binary particle swarm optimization for solving the bitmap join index selection problem in data warehouses. This approach selects the optimal set of bitmap join indexes based on a mathematical cost model. Several experiments are performed to demonstrate the effectiveness of the proposed method on the bitmap join index selection problem. Further testing of the method is performed using a database environment specific cost function. The binary particle swarm optimization is found to be more effective than both the genetic algorithm and data mining based approaches.     

A time-domain weighted linear successive interference cancellation detector for rapidly time-varying OFDMA communication systems

In this work, a weighted linear SIC (WLSIC) multi-user detector that is suitable for large-dimension rapidly time-varying communication systems is introduced. The proposed scheme exhibits low computational complexity and can converge to either the decorrelator or the LMMSE detector for two distinct values of the weighting factor. It uses a weighting factor to determine which average BER level the linear SIC converges to. We study the convergence behavior of the proposed scheme and prove that the latter is convergent if the weighting factor is between 0 and 1. Moreover, we propose a reduced complexity version of the WLSIC multi-user detector in which the complexity is reduced by 15% compared to the original WLSIC structure. Finally, simulation results obtained by applying the proposed WLSIC multi-user detector to cancel the inter-carrier interference (ICI) in an uplink OFDMA system coincide well with our theoretical findings.