By the term short-time motion tracking we refer to motions with durations up to few minutes, in which relative changes of altitude are to be tracked. Conversely, long-time motion tracking involves accurate tracking of absolute altitude over long time intervals. In human-centric applications, both tracking modes are involved: short-time tracking, e.g., fall detection; long-time tracking, e.g., personal navigation. Under conditions of short-time motion tracking, the time-varying mean is expected to vary very slowly, contributing an offset in the measured pressure altitude with no practical effect on the tracking accuracy. In those applications where accurate absolute altitude is needed, barometric altimeters in differential mode are perhaps the best option available to deal with slow changes of atmospheric pressure [11].

The proposed method of analysis decomposes the noise in three additive components with different physical origin and properties: a deterministic time-varying mean, correlated with slow pressure changes that can be involved in, e.g., local weather forecasting [13] and long-time tracking [14], whose modeling is beyond the scope of this paper; an exponentially time-correlated random process, modeled as a first-order Gauss-Markov (GM) process, that accounts for short-term, local environment changes, whose effect is prominent for short-time tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. We take the novel approach of using Gauss Markov (GM) random processes for modeling the short-time correlated component of altimeter noise.

The reason for this choice Carfilzomib is that GM random processes are mathematically simple, with only a limited number of parameters that need to be estimated [15]. Moreover, they seem to fit a wide variety of physical phenomena with reasonable accuracy [16]. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component.Experimental results obtained when the barometric altimeter is either motionless (for model identification) or moving (for tracking performance assessment) are presented. To this aim M-point moving average filters are applied to time-correlated pressure altitude signals, alone or cascaded with whitening filters learnt from ARMA model parameters. Advantages and disadvantages of removing the short-term correlation of the GM component by whitening filters are discussed.2.?Methods2.1. Measurement ProcessThe International Standard Atmosphere (ISA) model can be used to calculate the altitude from the output of an air pressure sensor [17].

8 �� 10? 5 N?s/m?2. is the equivalent spherical radius which can be calculated by R��=3R12L43. The radial displacement w of the shell is given by:w=FexKQe��xcos2��cos��t(2)where Fex is the amplitude of the exciti
There is a common interest on mapping and studying the bed constitution of natural water bodies, artificial harbours, or inland waterways for water management issues or navigability of shipping pathways, especially at the presence of a mud layer rich in fine-grained sediments. In the past, this non-consolidated, near-bottom mud layer was only assigned to few locations in channels, harbours and bays, but it is also a ubiquitous phenomenon in any natural water body [1]. It is present in any natural water body with sufficient supply of fine-grained sediment and periods of low flow velocity such as lakes and estuaries.

Acoustic techniques are extensively used in hydrographic surveys for lakebed mapping as they provide relatively rapid coverage of large lakebed areas compared to direct sampling methods [2,3]. But the inherent problem at the presence of a mud layer is the acoustic delineation and mapping of the lakebed surface. The mud density is slightly higher than that of water and increases gradually with depth [4], hence the impedance contrast offered to an acoustic wave by the water-mud-lakebed interface is less significant than by a water-lakebed interface. To overcome these difficulties of lakebed mapping McAnally et al. [4] emphasized the research need for improving or combining existing measurement techniques.

To support acoustic techniques for mud layer and lakebed mapping complementary methodologies Dacomitinib with a soil physical approach are recommendable and have already been applied [1,3,4]. However, these methods require intensive sampling effort. So far there is no common standardized method that delineates water, mud and consolidated lakebed sediments at the presence of a distinctive transition zone from water to lakebed. Many studies reported the development of sensors that combine cone penetrometer with water content measurement systems such as time domain reflectrometry (TDR) or time domain transmissometry (TRT) [5�C7]. All these presented probes and methods were developed for agricultural or mountainous forested soils [6,7], but not for surveying the challenging environment of a lake.

Therefore some inadequacies of these probes for the intended application were their standardization, lack of ruggedness, accuracy of penetration resistance PR measurement and obviously the very small maximal measurable depth of 40 to 60 cm.Thus, the purpose of the study was the adaptation of commonly used and well-known soil physical measurement techniques for the in situ delineation of mud and shallow lakebed-sediment layers within a hydrographic survey.

Another related approach relies on the use of a conditional particle filter to detect persons in motion [19].These previous methods do not use 3D information as input for their calculations, a restriction that limits their use for security applications, as they only detect moving objects at a predetermined height. This shortcoming was released by Tanner and Hartmann [9] by using a single time of flight (ToF) indoor camera. In the same line, Swadzba et al. [20] were able to track dynamic objects to reconstruct a static scene by using a ToF camera and a 6D data representation consisting of 3D sensor data and computed 3D velocities. Other options combine a 2D LIDAR scanner with a vertical servo to obtain 2.5D data of the environment (range images or point clouds). Using this combination, Ohno et al.

[21] were able to eliminate the moving objects from the scans of static scenes by comparing collision distances in the same area. More recently, Moosmann and Fraichard [22] have proposed a method consisting of deriving a dense motion field based exclusively on range images for performing object-class independent trajectory estimations.However, none of the previous approaches use full environment range images to effectively detect and track multiple dynamic objects from multiple robots. Herein, we develop two methods to detect moving objects from a robotic platform using range images. The first one is intended for static platforms and the second for dynamic ones. Furthermore, detection based on this type of data is followed by an effective tracking process using the generated dynamic objects lists.

1.2. Tracking of Dynamic ObjectsThere are several possibilities for tracking dynamic objects using a single robot. One of the most successful approaches [17] uses parameters, such as size and position, in a blob segmentation algorithm to characterize each detected object. These blobs are managed by creating a movement hypothesis with specific position and velocity data for each object. Each hypothesis is stored and updated with the estimated position and velocity of the objects, as well as with a weighting probability of the actual tracking of the moving object.In multi-robot systems, the information generated by each robot must be combined to enable better tracking. Stroupe et al.

[23] proposed two-dimensional Gaussian distributions to represent each observation of the object and a statistical procedure based on the Bayes rule and Kalman filters to combine two measurements. Another cooperative target tracking approach as proposed by Wang et al. [24] GSK-3 consists of a distributed Kalman filter to estimate the target position. Mazo et al. [25] proposed a hierarchical algorithm to locate and track a single dynamic object from data provided by a two-robot system.

On the Nexus, data was obtained from the tri-axial accelerometer, tri-axial magnetometer, tri-axial gyroscope, GPS, light and pressure sensor. On the smartwatch, data was collected from the tri-axial accelerometer, in part due to the fact that this was the only activity-related sensor available on this device. The authors used Purple Robot to gather data on both devices, as depicted in Figure 1. This Android application, developed by the Centre for Behavioural Interventions at Northwestern University, gives researchers access to dozens of underlying device sensors. Known by the term ��probes�� in Purple Robot terminology, these represent both physical and virtual sensors. Such probes can include accelerometers, gyros, and message and call statistics.

Purple Robot uses a store and forward mechanism, only uploading data to the Purple Robot warehouse server, when a suitable data connection becomes available. In our experiments, the Wi-Fi connection was used once data collection was complete to upload all sensor data pertaining to the study. Both devices were linked via a separate application running on the researcher’s phone. This application, called the Syncatronic, was used to annotate activities in the moment, and keep sensor data from both devices in sync for post hoc analysis.Figure 1.(a) Android application running on smartphone; and (b) smartwatch.4.?Signal ProcessingInitial data processing is undertaken to inter
Modern civilization is living on the brink of technological innovation. Never before have technological products evolved as much as in the last 15 to 20 years.

One of the reasons for this evolution leap was the introduction of consumer electronics, which allowed the common population to have easy access to advanced electronic devices. Nowadays, most people are used to owning and operating advanced systems [1]. Thus, society in general has taken on technological devices as an absolute common good, providing a shift in the way that electronic and digital tools are being used. For instance, we can observe the way that people use computers and smartphones, which have expanded beyond their initial purpose of work facilitators and communication devices to become complete and complex entertainment systems with games, music, and videos.Still another driving force in the technological area were evolutions in other domains which were only imminently technological, such as the medical field, engineering practice, and telecommunications.

They require massive investments, which led to cutting-edge technology solutions that are used to solve complex problems [2�C4]. Moreover, even the relatively minor GSK-3 developments played an important role, by inducing a technological development mentality that has shaped the world we know, and which is continuously and steadily progressing.

Dopamine-glutamate reciprocal modulations play a major integrative role in the striatum. Glutamate acts on two types of glutamatergic receptors, ionotropic glutamatergic receptors (NMDA, AMPA and kainate) and metabotropic glutamatergic receptors that are G-protein coupled. Ionotropic NMDA receptors are found postsynaptically on GABA neurons [19]. These receptors are also expressed presynaptically on dopaminergic terminals [20]. NMDA receptor activation has been shown to enhance stimulated dopamine release in slice preparations. This facilitating action was reversed by NMDA receptor antagonists and was resistant to TTX, indicating that the receptors being activated are presynaptically located [21, 22].

In contrast, previous voltammetric studies have shown that the activation of NMDA receptors inhibits dopamine release [23, 24].

Thus it seems that NMDA receptor activation can have both a facilitatory and inhibitory effect on dopaminergic transmission. Conversely dopamine has also been shown to modulate glutamate release. Dopamine D2-like receptors are involved in the presynaptic inhibition of glutamatergic transmission [25]. The general consensus is that the receptors involved in the control of glutamate release throughout the striatum belong to the D2-like [26], but not the D1-like receptor family. The presence and the function of D1-like receptors on corticostriatal terminals is still a matter of some debate.

A1 receptors located on corticostriatal terminals inhibit transmitter release through the blockade of Ca2+ Drug_discovery currents [27].

As A1 receptors are located on glutamatergic terminals, it has been suggested that the ability of A1 receptors to modulate dopamine release is secondary to their ability Anacetrapib to decrease glutamate release, resulting in a decrease in the activation of ionotropic glutamate receptors localized in dopaminergic terminals [12]. In the first section of the present study we investigate the role that NMDA receptor activation plays in the modulation of dopamine release and the influence of adenosine A1 receptors in this modulation.GABA plays a central role in the processing of information in the striatum. There are two neuronal sources of GABA in the striatum, spiny projection neurons and intrinsic GABAergic interneurons.

The spiny projection neurons are the prinicipal efferent cells of the striatum. These neurons receive excitatory input from motor cortices and thalamus and dopamine input from midbrain dopamine cells. Dopaminergic input is critical for the control of movement by the basal ganglia; its loss leads to the Site URL List 1|]# motor deficits observed in Parkinson’s disease.