Current Issue : April - June Volume : 2014 Issue Number : 2 Articles : 4 Articles
Sensors deployed in natural environments, such as rivers, beaches and glaciers,\r\nexperience large forces and damaging environmental conditions. Sensors need to be robust,\r\nsecurely operate for extended time periods and be readily relocated and serviced. The\r\nsensors must be housed in materials that mimic natural conditions of size, density, shape and\r\nroughness. We have developed an encasement system for sensors required to measure large\r\nforces experienced by mobile river sediment grains. Sensors are housed within two discrete\r\ncases that are rigidly conjoined. The inner case exactly fits the sensor, radio components\r\nand power source. This case can be mounted within outer cases of any larger size and can\r\nbe precisely moulded to match the shapes of natural sediment. Total grain mass can be\r\ncontrolled by packing the outer case with dense material. Case design uses Solid-WorksTM\r\nsoftware, and shape-matching involved 3D laser scanning of natural pebbles. The cases\r\nwere printed using a HP DesignjetTM 3D printer that generates high precision parts that lock\r\nrigidly in place. The casings are watertight and robust. Laboratory testing produces accurate\r\nresults over a wider range of accelerations than previously reported....
Step counting-based dead-reckoning has been widely accepted as a cheap and\r\neffective solution for indoor pedestrian tracking using a hand-held device equipped with\r\nmotion sensors. To compensate for the accumulating error in a dead-reckoning tracking\r\nsystem, extra techniques are always fused together to form a hybrid system. In this paper,\r\nwe first propose a map matching (MM) enhanced particle filter (PF) as a robust\r\nlocalization solution, in which MM utilizes the corridor information to calibrate the step\r\ndirection estimation and PF is applied to filter out impossible locations. To overcome the\r\ndependency on manually input corridor information in the MM algorithm, as well as the\r\ncomputational complexity in combining two such algorithms, an improved PF is proposed.\r\nBy better modelling of the location error, the improved PF calibrates the location\r\nestimation, as well as step direction estimation when the map information is available,\r\nwhile keeping the computational complexity the same as the original PF. Experimental\r\nresults show that in a quite dense map constraint environment with corridors, the proposed\r\nmethods have similar accuracy, but outperform the original PF in terms of accuracy. When\r\nonly partial map constraints are applied to simulate a new testbed, the improved PF obtains\r\nthe most robust and accurate results. Therefore, the improved PF is the recommended DR\r\nsolution, which is adaptive to various indoor environments....
The paper presents a novel methodology for the control management of a swarm\r\nof autonomous vehicles. The vehicles, or agents, may have different skills, and be\r\nemployed for different missions. The methodology is based on the definition of descriptor\r\nfunctions that model the capabilities of the single agent and each task or mission. The\r\nswarm motion is controlled by minimizing a suitable norm of the error between agents�\r\ndescriptor functions and other descriptor functions which models the entire mission. The\r\nvalidity of the proposed technique is tested via numerical simulation, using different task\r\nassignment scenarios....
Hermes is a Single-Input Single-Output (SISO) underwater acoustic modem that\r\nachieves very high-bit rate digital communications in ports and shallow waters. Here, the\r\nauthors study the capability of Hermes to support Multiple-Input-Multiple-Output (MIMO)\r\ntechnology. A least-square channel estimation algorithm is used to evaluate multiple\r\nMIMO channel impulse responses at the receiver end. A deconvolution routine is used to\r\nseparate the messages coming from different sources. This paper covers the performance of\r\nboth the channel estimation and the MIMO deconvolution processes using either simulated\r\ndata or field data. The MIMO equalization performance is measured by comparing three\r\nrelative root mean-squared errors (RMSE), obtained by calculations between the source\r\nsignal (a pseudo-noise sequence) and the corresponding received MIMO signal at various\r\nstages of the deconvolution process; prior to any interference removal, at the output of the\r\nLinear Equalization (LE) process and at the output of an interference cancellation process\r\nwith complete a priori knowledge of the transmitted signal. Using the simulated data, the\r\nRMSE using LE is -20.5 dB (where 0 dB corresponds to 100% of relative error) while the lower bound value is -33.4 dB. Using experimental data, the LE performance is -3.3 dB\r\nand the lower bound RMSE value is -27 dB....
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