The array of this second prototype is simpler as it is composed o

The array of this second prototype is simpler as it is composed of two sub-arrays, one for each hand, of only eight elements each (see Figure 4). The force sensors are now of longitudinal shape (Interlink Electronics FSR 408 [15]) and are placed on the flat faces of an octagonal bar. It is sturdier as there is no soldering under the pressure sensitive area. Therefore, lifetime related to wear and tear, due to physical contact, must be similar to that of the force sensor, which is 10 million actuations. Moreover, since it has fewer force sensors this implementation is cheaper and has a quicker response time than the first one. An LCD was also added to show messages to the user.Figure 4.Second prototype of the proposed device.2.2. Control ElectronicsFigure 5 shows the schematic of the control electronics.

The rows of the matrix are connected to analog switches (ADG734, Analog Devices, Norwood, MA, USA) and the columns to transimpedance amplifiers (based on LMV324 operational amplifiers, Texas Instruments, Dallas, TX, USA). A microcontroller (PIC18F4680) scans the array by closing the switches sequentially through general purpose I/O ports. The addressed row is grounded while the other rows remain c
As the Geographic Information System (GIS) has been used for a wide range of transportation applications, positional errors inherent in spatial data become critical for ensuring spatial problem-solving and decision-making. However, GIS involves spatial data from multiple sources and different types. People are used to making decisions without knowledge of either positional errors in the data or their impact on output information.

In GIS for transportation, various data-collection methods or devices have been used to maintain and update a spatial database, of which the Global Positioning Carfilzomib System (GPS) provides a cost effective and efficient means of collecting spatial and non-spatial data along roadways. One emerging GPS-based method is to equip vehicles with Differential Global Positioning System (DGPS) receivers and numerous sensors [1�C3]. All data coming from the vehicles are spatially and temporally referenced, and therefore they are adaptable in GIS.However, positional uncertainties inevitably exist in GPS data points and roadway centerline maps.

Although numerous map-matching algorithms have been proposed to correctly integrate GPS data points with a roadway centerline map [4�C7], positional uncertainties still exist in snapped GPS-derived coordinates along roadway centerlines. These uncertainties increase and propagate to output products from GIS. Therefore, to make informed decisions, it is necessary to know the quality of output information associated with different levels of input data quality. Specifically, GIS applications should support optimum use of input data and, conversely, the optimum input for data use [8].

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