Recently, the realization of artificial sensory systems mimicking the biological perception has been intensively pursued for the next generation neuromorphic electronics and humanoid robots. Particularly, an artificial somatosensory system which can emulate the functions of the biological skin and body sensation is considered to have a great potential in achieving highly integrated and neuromorphic sensory network. The biological somatosensory system is a complex sensory network, which is composed of sensory neurons (receptors), neural pathways, and a part of the brain for the perception process. By the sensory receptors such as mechanoreceptors, thermoreceptors, and nociceptors, [1][2][3][4][5][6][7][8][9][10] which are located on or beneath the skin, various environmental stimuli are detected and transmitted to the brain through the neural pathways. This enables the specific sensations such as strain, pressure, temperature, and distortion (flexion/ bending) of the body. In realizing an artificial somatosensory system, however, the integration of a large amount of sensory networks for the individual sensation still remains as a significant challenge, especially in the case of largearea electronic skin (e-skin) devices. For example, it is reported that to realize an e-skin for robotics and prosthetic limbs, an estimated 45 000 mechanoreceptors are needed in about 1.5 m 2 -area devices. [11] Additionally, the number of sensors could increase even further, considering the e-skins to have equivalent numbers of thermoreceptors and nociceptors in the system. Therefore, to fully mimic the biological skin perception over a large-area, a large number of sensory systems with complicated multi-layer architectures would be required as well as a large amount of data associated with their perception processing.In recent research, a new strategy to achieve artificially intelligent perception has been introduced in chemical and gas detection systems by analyzing the different responses recognized from many cross interferences. [12][13][14][15][16][17][18] These cross-reactive sensory systems, inspired by mammalian olfactory and gustatory systems, can simultaneously detect and identify specific responses from a variety of non-specific vapor, liquid elements, and their combinations by analyzing the difference in sensing responses with pattern recognition and machine learning algorithms. [19][20][21][22][23][24][25][26][27] Although these previous advances are noteworthy, Mimicking human skin sensation such as spontaneous multimodal perception and identification/discrimination of intermixed stimuli is severely hindered by the difficulty of efficient integration of complex cutaneous receptor-emulating circuitry and the lack of an appropriate protocol to discern the intermixed signals. Here, a highly stretchable cross-reactive sensor matrix is demonstrated, which can detect, classify, and discriminate various intermixed tactile and thermal stimuli using a machine-learning approach. Particularly, the multimodal perception ability is ...
Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.
ministic parameters such as sensitivity, durability, sensing range, and response time were significantly improved by tremendous efforts of researchers. Notably, many attempts have been carried out to realize highly strain-sensitive composites consisting of metallic nanowires, carbon nanotubes, carbon/metal nanoparticles, and elastomers. [6,7] Along with such material consolidating strategy, a variety of mechanistic device structures (resistive, capacitive, piezoresistive, triboelectric, and piezoelectric types) has been demonstrated to achieve high-performance strain sensors. [8][9][10][11][12] However, most of the conventional strain sensors are only capable of detecting uniaxial strain and lack the ability to determine directional variable motions, limiting their applications in realistic surface stimuli environments such as dynamic human skin motion sensing and human-machine interfaces.To address this issue, geometrically engineered [13][14][15] sensor devices composed of multidimensionally stacked channel or cross-shaped detecting electrodes have been introduced. [16,17] These strain sensors are capable of detecting the difference of gauge factor corresponding to different loading directions, successfully exhibiting direction-dependent sensing characteristics. In fact, these newly engineered architectures have been developed to avoid the difficulty to achieve multidirectional strain sensing by employing microscopically isotropic conducting pathways of sensing channel layers, which typically experience identical deformation upon any stimulating direction. Although Mechanically stretchable strain sensors gain tremendous attention for bioinspired skin sensation systems and artificially intelligent tactile sensors. However, high-accuracy detection of both strain intensity and direction with simple device/array structures is still insufficient. To overcome this limitation, an omnidirectional strain perception platform utilizing a stretchable strain sensor array with triangular-sensor-assembly (three sensors tilted by 45°) coupled with machine learning (ML) -based neural network classification algorithm, is proposed. The strain sensor, which is constructed with strain-insensitive electrode regions and strain-sensitive channel region, can minimize the undesirable electrical intrusion from the electrodes by strain, leading to a heterogeneous surface structure for more reliable strain sensing characteristics. The strain sensor exhibits decent sensitivity with gauge factor (GF) of ≈8, a moderate sensing range (≈0-35%), and relatively good reliability (3000 stretching cycles). More importantly, by employing a multiclassmultioutput behavior-learned cognition algorithm, the stretchable sensor array with triangular-sensor-assembly exhibits highly accurate recognition of both direction and intensity of an arbitrary strain by interpretating the correlated signals from the three-unit sensors. The omnidirectional strain perception platform with its neural network algorithm exhibits overall strain intensity and direction accura...
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