The difference between a sensor and a human nose is that the sensor responds in a highly selective fashion to a specific target analyte or class of target analytes through the use of a "lock-andkey" design, whereas the human nose is designed to discriminate between various kinds of analytes (i.e., odorants). 1 The odorant is first introduced in the nasal cavity and exposed to a small area of about 2.5 cm 2 containing around 50 million primary sensor cells. Each type of cell is replicated. Identical cells are connected to a glomerulus in the olfactory bulb. The information sent to the cortex upon sensing the odorant is not the signal from a specific receptor but rather it is a 2D map of the responses of all glomeruli projected on the surface of the olfactory bulb. The brain then recognizes a distributed pattern formed by an ensemble of signals coming from all the glomeruli and not a single response from a specific cell. 2 To mimic the working principle of a human nose, artificial electro-optical noses 3À9 operate on exposing odorants to an array of sensors and analyze the data using principal component analysis. 10 Artificial electro-optical noses have a broad spectrum of interesting and extraordinary applications, such as diabetes diagnosis 11 and lung cancer identification 12 by the analysis of breath, cigarette brand identification, 13 online monitoring of composting procedures, 14 evaluation of coffee ripening, 15 monitoring of plant health in greenhouse, 16 identification of paper

The sensors of taste and odor play important roles of recognition as well as reception. In our research, the taste and odor sensing capabilities were based on the photoluminescence (PL) responses of luminescent metal-organic frameworks (MOFs). For the sensing of taste, [In(OH)(bdc)]n(bdc = 1,4-benzenedicarboxylate) and [Tb(btc)] (MOF-76, btc = benzene-1,3,5-tricarboxylate), were tested on aqueous solutions of five basic tastants such as sucrose (sweet), caffeine (bitter), citric acid (sour), sodium chloride (salty) and monosodium glutamate (umami). The photoluminescence (PL) responses of polyacrylic acid-chelated [In(OH)bdc]n and lanthanide Tb(btc) were used to demonstrate the applicability of MOF-based biomimetic tongue through: (1) identification of five tastes: sweet, bitter, sour, salty and umami, by 3-D PCA (principle component analysis) to distinguish the corresponding tastants, (2) quantification of the strength of five tastes determined by the relationships between the PL intensity and the τ scale of taste. For the sensing of odor, [In(OH)(bdc)]nand [Zn4O(bdc)3] (MOF-5) were exposed to the odorants such as cumin, cinnamon, vanillin, p-xylene, m-xylene, o-xylene, water, and ethanol. Similarly, the MOF-based biomimetic nose could distinguish the odors of the analytes based on a pattern recognition method (i.e., principal component analysis) constructed by the 2-D map of PL emission responses.

To recover QAM signals at the receiver of blind equalizer, a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.