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Quality control and counterfeit product detection have become exceedingly important due to the vertical market of beers in the global economy. China is the largest producer of beer globally and has a massive problem with counterfeit alcoholic beverages. In this research, a modular electronic nose system with 4 MOS gas sensors was designed for colle...
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... the NN model reported only 79.6% classification accuracy with 4,000 hidden neurons when the NN model was trained using features extracted from the rising curve. Accuracy comparison of rising and whole curves with 1,000 and 4,000 hidden neurons is shown in Figure 4. Average accuracies for individual beers were also reported to be decreased. ...
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The basic components of modern electronics are primarily fabricated by using semiconductor material in which the movement of charges can be electrically modulated. For metallic material, however, such modulation is almost impossible because of its field‐shielding characteristics, leading to insensitive response toward applied potentials. In this pe...
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... Memristive devices show promise in achieving efficient and scalable computing systems that emulate neural network functionalities [66]. Their ability to facilitate adaptive synaptic weight adjustments makes them valuable for applications in neuromorphic computing and various domains [84][85][86]. ...
A mathematical model of a spiking neuron network accompanied by astrocytes is con- sidered. The network is composed of excitatory and inhibitory neurons with synaptic connections supplied by a memristor-based model of plasticity. Another mechanism for changing the synaptic connections involves astrocytic regulations using the concept of tripartite synapses. In the absence of memristor-based plasticity, the connections between these neurons drive the network dynamics into a burst mode, as observed in many experimental neurobiological studies when investigating living networks in neuronal cultures. The memristive plasticity implementing synaptic plasticity in inhibitory synapses results in a shift in network dynamics towards an asynchronous mode. Next,it is found that accounting for astrocytic regulation in glutamatergic excitatory synapses enable the restoration of ’normal’ burst dynamics. The conditions and parameters of such astrocytic regulation’s impact on burst dynamics established.
... The active development of odor analysis systems and memristive devices, as well as their practical significance, have made their combination relevant. For example, in the work [48] to detect counterfeit beer in China, researchers designed an electronic nose system with 4 MOS gas sensors, a sample-delivery subsystem, and a software-based data acquisition subsystem. They used a memristor back-propagation neural network to classify the quality of the beer. ...
We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of “decoder” neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the “decoder” layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of “decoder” neurons does not exhibit specificity in recognizing odorants. The recognition of the ’odorant’ occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications.
The electronic nose (e-nose) is a highly advanced detection technique with numerous practical uses in the areas of food, health, environment, and safety. Especially in certain hazardous situations and repetitive mechanical use, utilizing portable and compact e-nose detection equipment can significantly minimize harm to individuals and economic costs. This requires the combination of advanced artificial intelligence algorithms and low-power hardware to design some low-power, miniaturized, and high-speed response e-nose devices. This paper proposes a compact and low-power computing scheme with a memristor-based in-memory computation accelerator for the multi-gas sensor processing system. We first created a platform for acquiring gas data and gas concentration from a multi-gas sensing array and processed it in two dimensions to make it suitable for a convolutional neural network. A new 16-level non-volatile ReRAM memory in-store computation scheme is used to achieve parallel multiplication and addition computations. A hybrid quantization accuracy-aware algorithm was also designed to improve the recognition accuracy of the neural network with low-bit quantization weights. When using 4-bit weight quantification, the identification accuracy of gas and gas combination concentration reached 95.2% and 94.67%, respectively. Finally, we deploy the trained network weights of each layer into the in-memory computation accelerator. Based on the on-chip experimental results, our proposed in-memory computation acceleration scheme achieves impressive recognition accuracies of 94.69% and 94.21% for classifying 10 different gases and identifying their concentrations, even when utilizing 4-bit quantized weights. Moreover, it takes less than 0.4 ms to perform a single inference with 20.2 mW power consumption. These results demonstrate that our work has great potential for applications in low-power, low-latency, and compact electronic noses.
We consider an unstructured neuron network model composed of excitatory and inhibitory neurons. The synaptic connections are supplied with spike timing-dependent plasticity (STDP). We take the STDP model implemented using a memristor. In normal conditions, the network forms so-called bursting discharges typical of unstructured living networks in dissociated neuronal cultures. Incorporating a biologically inspired model, we demonstrate how memristive plasticity emulates spike timing-dependent plasticity, which is crucial for regulating synchronous brain activity. We have found that, when the memristor-based STDP for inhibitory connections is activated, the bursting dynamics are suppressed and the network turns to a random spiking mode. The dependence of bursting properties on the degree of the memristor-based STDP plasticity is analyzed. These findings hold implications for advancing invasive neurointerfaces and for the identification and management of epileptiform activity.
