Implementation of stealth features on advanced airborne platforms (Aircrafts, Unmanned Air Vehicles, Missiles, etc.) has become a compulsion for each country, for denial/delay detection of these objects from enemy Radars, during tactical missions. Apart from the shaping of airframe, implementation of Microwave Absorbing Materials (MAMs) on identified locations of airborne vehicles is the only viable solution to reduce their Radar Cross Section (RCS) and eventually attain stealth capabilities. Numerous dielectric and magnetic class materials have been developed over the last few decades to fulfil the requirement for RCS reduction against various Radars operating in different frequency ranges. In this review, a detailed representation of almost entire range of materials used as MAMs has been provided along with their possible Microwave (MW) loss mechanism to fill the gap that existed for a systematic insight on MAMs till now. The current limitations, and future aspects are also discussed for the development of future stealth materials.

Speech is the fundamental way of communicating with one another. It simply refers to transmission of messages. In case of speech production the information is transmitted in the form of analog waveform that can be transmitted, recorded or decoded. A number of algorithms for speech recognition have been proposed. In this paper, we have suggested an innovative approach of speech recognition. We have initially stored some voice in the database where the same speaker has told different words. Then we have inputted a sample voice of the same person through the microphone where he is speaking a specific word which is already stored in the database. We have performed the task of similar word recognition by finding the gray scale image and histogram plot of inputted word and finally we have used the correlation coefficient for making comparison between two words.

No abstract

The diverse use of electronic gazettes, equipped with the internet of things (IoT) and artificial intelligence, demand extensive data storage, and simultaneous processing. It poses serious challenges for the current computing devices based on von Neumann architecture, where processing and storage units are separate from each other and thus, need large power consumption. [1][2][3][4] Neuromorphic computing is considered an alternative to the von Neumann architecture as it has the potential for mimicking the human brain. Thus, resistive random-access memory (RRAM) can behave like an artificial neural network due to its nonvolatile nature, extensive data storage capability, ease of fabrication, and ability to act like synapses. RRAM is a two-terminal device with a metalinsulator-metal configuration. Further, it is divided into two sub-categories: 1) digital switching (DS)-based RRAM (i.e., abrupt change in resistance during high resistance to low resistance state transition) and 2) analog switching (AS)-based RRAM (i.e., gradual change in resistance while changing state from low to high or high to low resistance states). [5][6][7][8] DS-RRAM devices are explored more as compared to AS-RRAMs in general. Recently, AS-RRAM devices are also getting attention due to the ease of their implantation in the artificial neural network. The migration of oxygen ions is introduced or promoted to control the abrupt change in resistance using active bilayer material together with compliance current. The minimum and maximum resistances in analog RRAM devices are referred to as low and high resistance states. In contrast, intermediate resistance is referred to as the medium resistance state. Further, resistive switching-based devices are of two categories: 1) the first one is a write once read many (WORM), and 2) the second one is a rewritable one. [9] In WORM-based devices, once data is stored, it cannot be erased, i.e., these can be used for storing permanent data. [10] However, the stability of WORM devices is similar to that of rewritable devices, as both exhibit considerable Joule heating effect. [11] WORM is commonly observed on polymer, metal oxide, and organic semiconductors. The coexistence of resistive switching and negative differential resistance (NDR) in the oxide-based device has been demonstrated in NbO 2 , ZnO, TiO x , WO x , BaTiO 3, BiFeO 3 , etc.,. [12][13][14][15] However, oxide/ sulfide, [16] heterostructures-based devices are also explored for resistive switching and neuromorphic computing. For example, Shen [17] et al. showed TiN x O 2Àx /MoS 2 as an excellent resistive switching material together with its synaptic behavior in presence of light. Similarly, Qi Liu [18] et al., observed excellent resistive behavior with a large LRS/HRS ratio, high durability, and stability in oxide-based HfO 2 /WO 3 heterostructure and proposed that ion transport in the device is due to the Schottky barrier and conductive filament formation. In general, the current increases with increasing applied voltage, which sometimes exhibi...

Resistive Random‐Access Memory Devices Neuromorphic devices are essential for beyond von‐Neumann computing and Resistive Random Access Memories (RRAMs) are the backbone for it. In article number http://doi.wiley.com/10.1002/pssa.202200744, Chandra Prakash and colleagues show RRAM characteristics in a Bi12FeO20 system for the first time, together with negative differential resistance (NDR) and write once and read many (WORM) like characteristics simultaneously. It is hoped this could lead to the potential of neuromorphic computing and low‐power memory devices.