Fuzzy C-means Clustering and Pseudo-coloring-based Pest detection of Ripe-Fruit Health Monitoring System using 2-D Aggrotech Images

Fruits are the gift of almighty to nature. Fresh fruit promote good health and having rich source of micronutrients, vitamins and fiber value. But due to its high sugar level on ripping stage different type of pest are attracted by its smell and effects on harvesting. This paper focuses on identification of the pest on ripe fruits using Fuzzy C Means (FCM) clustering for segmentation and simultaneously highlights the segmented insects with Pseudo-coloring using Pseudo-color image processing techniques. IoT integrated Drone based images are inputted as the dataset to perform detection of pest on fruit monitoring system. Before clustering-based segmentation the images undergo preprocessing stage for tone correction and noise removal. Hybrid FCM with Pseudo-color image processing method supersedes many segmentation algorithms by performance

Ionotronic WS2 memtransistors for 6-bit storage and neuromorphic adaptation at high temperature

Abstract Inspired by massive parallelism, an increase in internet-of-things devices, robust computation, and Big-data, the upsurge research in building multi-bit mem-transistors is ever-augmenting with different materials, mechanisms, and state-of-the-art architectures. Herein, we demonstrate monolayer WS2-based functional mem-transistor devices which address nonvolatility and synaptic operations at high temperature. The ionotronic memory devices based on WS2 exhibit reverse hysteresis with memory windows larger than 25 V, and extinction ratio greater than 106. The mem-transistors show stable retention and endurance greater than 100 sweep cycles and 400 pulse cycles in addition to 6-bit (64 distinct nonvolatile storage levels) pulse-programmable memory features ranging over six orders of current magnitudes (10−12–10−6 A). The origin of the multi-bit states is attributed to the carrier dynamics under electrostatic doping fluctuations induced by mobile ions, which is illustrated by employing a fingerprint mechanism including band-bending pictures. The credibility of all the storage states is confirmed by obtaining reliable signal-to-noise ratios. We also demonstrate key neuromorphic behaviors, such as synaptic plasticity, near linear potentiation, and depression, rendering it suitable for successful implementation in high temperature neuromorphic computing. Furthermore, artificial neural network simulations based on the conductance weight update characteristics of the proposed ionotronic mem-transistors are performed to explore the potency for accurate image recognition. Our findings showcase a different class of thermally aided memories based on 2D semiconductors unlocking promising avenues for high temperature memory applications in demanding electronics and forthcoming neuromorphic computing technologies

Thermally Driven Multilevel Non-Volatile Memory with Monolayer MoS2 for Brain-Inspired Artificial Learning

The demands of modern electronic components require advanced computing platforms for efficient information processing to realize in-memory operations with a high density of data storage capabilities toward developing alternatives to von Neumann architectures. Herein, we demonstrate the multifunctionality of monolayer MoS2 memtransistors, which can be used as a high-geared intrinsic transistor at room temperature; however, at a high temperature (>350 K), they exhibit synaptic multilevel memory operations. The temperature-dependent memory mechanism is governed by interfacial physics, which solely depends on the gate field modulated ion dynamics and charge transfer at the MoS2/dielectric interface. We have proposed a non-volatile memory application using a single Field Effect Transistor (FET) device where thermal energy can be ventured to aid the memory functions with multilevel (3-bit) storage capabilities. Furthermore, our devices exhibit linear and symmetry in conductance weight updates when subjected to electrical potentiation and depression. This feature has enabled us to attain a high classification accuracy while training and testing the Modified National Institute of Standards and Technology datasets through artificial neural network simulation. This work paves the way toward reliable data processing and storage using 2D semiconductors with high-packing density arrays for brain-inspired artificial learning

Thermally Driven Multilevel Non-Volatile Memory with Monolayer MoS₂ for Brain-Inspired Artificial Learning

The demands of modern electronic components require advanced computing platforms for efficient information processing to realize in-memory operations with a high density of data storage capabilities toward developing alternatives to von Neumann architectures. Herein, we demonstrate the multifunctionality of monolayer MoS2 memtransistors, which can be used as a high-geared intrinsic transistor at room temperature; however, at a high temperature (>350 K), they exhibit synaptic multilevel memory operations. The temperature-dependent memory mechanism is governed by interfacial physics, which solely depends on the gate field modulated ion dynamics and charge transfer at the MoS2/dielectric interface. We have proposed a non-volatile memory application using a single Field Effect Transistor (FET) device where thermal energy can be ventured to aid the memory functions with multilevel (3-bit) storage capabilities. Furthermore, our devices exhibit linear and symmetry in conductance weight updates when subjected to electrical potentiation and depression. This feature has enabled us to attain a high classification accuracy while training and testing the Modified National Institute of Standards and Technology datasets through artificial neural network simulation. This work paves the way toward reliable data processing and storage using 2D semiconductors with high-packing density arrays for brain-inspired artificial learning