Low Power Microwave Signal Detection With a Spin-Torque Nano-Oscillator in the Active Self-Oscillating Regime

A spin-torque nano-oscillator (STNO) driven by a ramped bias current can perform spectrum analysis quickly over a wide frequency bandwidth. The STNO spectrum analyzer operates by injection locking to external microwave signals and produces an output DC voltage $V_{\rm dc}$ that temporally encodes the input spectrum. We found, via numerical analysis with a macrospin approximation, that an STNO is able to scan a $10~\rm GHz$ bandwidth in less than $100~\rm ns$ (scanning rate $R$ exceeds $100~\rm MHz/ns$). In contrast to conventional quadratic microwave detectors, the output voltage of the STNO analyzer is proportional to the amplitude of the input microwave signal $I_{\rm rf}$ with sensitivity $S = dV_{\rm dc}/dI_{\rm rf} \approx 750~\rm mV/mA$. The minimum detectable signal of the analyzer depends on the scanning rate $R$ and, at low $R \approx 1~\rm MHz/ns$, is about $1~\rm pW$.Comment: 5 pages, 5 figure

Noise properties of a resonance-type spin-torque microwave detector

We analyze performance of a resonance-type spin-torque microwave detector (STMD) in the presence of noise and reveal two distinct regimes of STMD operation. In the first (high-frequency) regime the minimum detectable microwave power $P_{\rm min}$ is limited by the low-frequency Johnson-Nyquist noise and the signal-to-noise ratio (SNR) of STMD is proportional to the input microwave power $P_{\rm RF}$. In the second (low-frequency) regime $P_{\rm min}$ is limited by the magnetic noise, and the SNR is proportional to $\sqrt{P_{\rm RF}}$. The developed formalism can be used for the optimization of the practical noise-handling parameters of a STMD.Comment: 3 pages, 2 figure

A Fuzzy Simulation Model for Military Vehicle Mobility Assessment

There has been increasing interest in improving the mobility of ground vehicles. The interest is greater in predicting the mobility for military vehicles. In this paper, authors review various definitions of mobility. Based on this review, a new definition of mobility called fuzzy mobility is given. An algorithm for fuzzy mobility assessment is described with the help of fuzzy rules. The simulation is carried out and its implementation, testing, and validation strategies are discussed

Hierarchical fuzzy deep learning for image classification

Considerable interest has been shown over the last several decades for fuzzy logic and its application. The intelligent and deep learning systems are gaining breakthroughs in all walks of life to solve real-life problems for the future. The conventional fuzzy has the constraint to work with limited rule dimensions, whereas deep neural networks are unable to handle uncertain and imprecise data implicitly in the system. The objective of this paper is to develop a generalized algorithm for intelligent systems that can handle uncertainty and imprecise behavior especially for processing of large image datasets. In this paper, the hierarchical fuzzy approach is suggested, as it is gaining attention to tackle large real-life problems. The strategy used is to partition a large image dataset into small data samples and connect all the fuzzy subsystems in a hierarchical manner. In the literature, as far as authors know, no one has developed a hierarchical fuzzy approach to handle a large image dataset of real images. The algorithm for hierarchical fuzzy logic for a large image data using image thresholding has been discussed. To make the assessment, the real image database has been considered. The image classification has attained the potential applications to defense and security especially for target identification and classification. The accuracy and computational time comparisons of hierarchical fuzzy systems with existing methodologies such as deep neural networks have been discussed

Detection and Elimination of a Potential Fire in Engine and Battery Compartments of Hybrid Electric Vehicles

This paper presents a novel fuzzy deterministic noncontroller type (FDNCT) system and an FDNCT inference algorithm (FIA). The FDNCT uses fuzzy inputs and produces a deterministic non-fuzzy output. The FDNCT is an extension and alternative for the existing fuzzy singleton inference algorithm. The research described in this paper applies FDNCT to build an architecture for an intelligent system to detect and to eliminate potential fires in the engine and battery compartments of a hybrid electric vehicle. The fuzzy inputs consist of sensor data from the engine and battery compartments, namely, temperature, moisture, and voltage and current of the battery. The system synthesizes the data and detects potential fires, takes actions for eliminating the hazard, and notifies the passengers about the potential fire using an audible alarm. This paper also presents the computer simulation results of the comparison between the FIA and singleton inference algorithms for detecting potential fires and determining the actions for eliminating them