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Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air–water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

A built environment, that also includes infrastructures, needs to be taken under control to prevent unexpected modifications, otherwise it could react as a loose cannon. Sensing techniques and technologies can come to the rescue of built environments thanks to their capabilities to monitor appropriately. This article illustrates findings related to monitoring a channel hydrodynamic behavior by means of sensors based on imaging and ultrasound. The ultrasound approach is used here to monitor the height of the water with respect to a maximum limit. Imaging treatment is here proposed to understand the flow velocity under the area to be considered. Since these areas can be covered by trash, an enhanced version of the particle image velocimetry technique has been implemented, allowing the discrimination of trash from water flow. Even in the presence of the total area occupied by trash, it is able to detect the velocity of particles underneath. Rainfall and hydraulic levels have been included and processed to strengthen the study.

This paper considers the fast and accurate estimation of motion variables of a rigid body object whose movement occurs from intermittent contacts with coordinating manipulators in nonprehensile manipulation tasks. The estimator operates under multiple sensory data including visual, joint torque, joint position and/or tactile measurements which are combined at the lower level to compensate for the latency and the slow sampling of the visual data. The estimator is real-time in the sense that it provides the motion data of the target object at the same fast sample rate of the servo controller without delay. The basic formulation is the multi-rate Kalman filter with the contact force vector as its process input, and the visual observation as its measurement output signal which is the down-sampled and delayed version of the configuration of the target object. Experimental tests are conducted for the case of planar object manipulation as well as the non-centroidal rotation under gravity using a robotic hand, and their results are presented to demonstrate the validity of the proposed estimation scheme. [ABSTRACT FROM AUTHOR]

This paper presents the design, modeling, force correction strategies and experimental validation of a force sensor designed for robotized medical applications. The proposed sensor offers a new solution for force measurement in the presence of specific constraints such as medical imaging transparency, limited size, satisfactory rigidity and measurement performance. More specifically, the presented prototype has been purposely adapted to comply with the requirements of needle insertion applications, in the context of interventional radiology. A systematic viscoelastic model identification method is discussed for choosing the best time-dependent model for the force sensor. A novel compensation law is proposed based on the chosen model to correct for the viscoelastic effects of the utilized polymer material. The developed compensation law is inexpensive, stable to noise and can be applied in real-time to the sensor signal. A comparative assessment of the experimental results, obtained from quasi-static to dynamic experiments including harmonic analysis, shows the efficacy of the proposed compensation law, as compared to calibration with static gain and without compensation. The improvement in the sensor response results in decreased hysteresis levels and increased bandwidth, which are improved by more than a factor of 4. [ABSTRACT FROM PUBLISHER]

Environmental informatics has experienced extraordinarily rapid progress in the past decade and has made an invaluable contribution to planning, design, and operations for waste management. In many cases, however, the roles of these information technologies have been limited to stand-alone projects without synergistic effects. This article presents a holistic view and an in-depth discussion of environmental informatics applied to solid and hazardous waste management from the onset to the present status, and to future trends aiming to advance the management potential. With regard to building, maintaining, and developing knowledge-based or artificial intelligence systems, the spectrum of environmental informatics for solid and hazardous waste management can be classified into five categories: database system, geographical information system, decision support system, expert system, and integrated environmental information system. Supporting technologies in the integrated environmental information system can be further divided into five classes in a logical order to enhance understanding: data acquisition, data communication, data and knowledge storage, data mining and knowledge discovery, and data and knowledge utilization. This critical review article may help create sustainable development strategies from a local solution to global opportunities that will elevate environmental informatics to a new level in dealing with more complex problems and large-scale applications for integrated solid and hazardous waste management. [ABSTRACT FROM AUTHOR]

Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air–water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas. [ABSTRACT FROM AUTHOR]

A built environment, that also includes infrastructures, needs to be taken under control to prevent unexpected modifications, otherwise it could react as a loose cannon. Sensing techniques and technologies can come to the rescue of built environments thanks to their capabilities to monitor appropriately. This article illustrates findings related to monitoring a channel hydrodynamic behavior by means of sensors based on imaging and ultrasound. The ultrasound approach is used here to monitor the height of the water with respect to a maximum limit. Imaging treatment is here proposed to understand the flow velocity under the area to be considered. Since these areas can be covered by trash, an enhanced version of the particle image velocimetry technique has been implemented, allowing the discrimination of trash from water flow. Even in the presence of the total area occupied by trash, it is able to detect the velocity of particles underneath. Rainfall and hydraulic levels have been included and processed to strengthen the study. [ABSTRACT FROM AUTHOR]