One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher learning capability. The latter forms the basis of the present study in which a new ionic model for reservoir-like networks, consisting of spiking neurons, is introduced. High plasticity of this model makes learning possible with a fewer number of neurons. In order to study the effect of the applied stimulus in an ionic liquid space through time, a diffusion operator is used which somehow compensates for the separation between spatial and temporal coding in spiking neural networks and therefore, makes the mentioned model suitable for spatiotemporal patterns. Inspired by partial structural changes in the human brain over the years, the proposed model evolves during the learning process. The effect of topological evolution on the proposed model's performance for some classification problems is studied in this paper. Several datasets have been used to evaluate the performance of the proposed model compared to the original LSM. Classification results via separation and accuracy values have shown that the proposed ionic liquid outperforms the original LSM.

Sound source localization has always been one of the most challenging subjects in different fields of engineering, one of the most important of which being tracking of flying objects. This paper focuses on sound source localization using fuzzy fusion and a beamforming method. It proposes a new fuzzybased algorithm for localizing a sound source using distributed sensor nodes. Eight low-cost sensor nodes have been constructed in this study each of which consists of a microphone array to capture sound waves. Each node is able to record audio signals synchronously on an SD card to evaluate different algorithms offline. However, the sensor nodes are designed to be able to estimate the location of the sound source in real-time. In the proposed algorithm, every node estimates the direction of the sound source. Moreover, a calibration algorithm is used for extracting the orientation of sensor nodes to calibrate the estimated directions. The calibrated directions are fuzzified and then used for localizing the sound source by fuzzy fusion. An experiment was designed based on localizing a flying quadcopter as a moving sound source to evaluate the performance of the proposed algorithm. The flying trajectory was then estimated and compared with the target trajectory extracted from the GPS module mounted on the quadcopter. Comparing the estimated sound source with the target location, a mean distance error of 6.03 m was achieved in a wide-range outdoor environment with the size of 240 × 160 × 80 m 3. The achieved mean distance error is reasonable regarding the mean precision of the GPS module. The practical results illustrate the effectiveness of the proposed approach in localizing a sound source in a wide-range outdoor environment.

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Distributed wireless sensor networks, which are consisting of several single sensors, are becoming very popular in many important applications. This paper talks about collaborative algorithms which are implemented on WSN in order to find the location of sound source. In this paper, an algorithm is discussed for each node and a collaborative algorithm is proposed for sink node. The algorithms are designed in order to find the location of acoustic source in realtime. For localizing, 4 nodes are used. Each of them finds the direction of acoustic source individually. Then, they transfer their computed angle to sink node in order to localize acoustic source. Thus, 4 direction finder nodes and 1 sink node are designed. Direction finder nodes, which consist of 2 microphones, compute the angle of acoustic source respect to itself using GCC algorithm. Sink node receives the computed angles through wireless communication. Moreover, sink node computes the probability graph of the environment. Then, the maximum point of this graph determines the location of acoustic source. At last, represented nodes are built and they are tested in practical environment. The experimental results demonstrate that the proposed algorithm is accurate and feasible.