Heart is the most vital organ which circulates blood along with nutrients and oxygen throughout the body. There are number of reasons which may affect its normal working. In this paper ten heart diseases, as well as normal, have been classified by extracting features from original ECG (electrocardiogram) signals and sixth level wavelet transformed ECG signals. The results have been compared and improved accuracy has been obtained using wavelet transformed signals.
Magnetic skyrmions are potential candidates for neuromorphic computing due to their inherent topologically stable particle-like behavior, low driving current density, and nanoscale size. Antiferromagnetic skyrmions are favored as they can be driven parallel to in-plane electrical currents as opposed to ferromagnetic skyrmions which exhibit the skyrmion Hall effect and eventually cause their annihilation at the edge of nanotracks. In this paper, an antiferromagnetic skyrmion based artificial neuron device consisting of a magnetic anisotropy barrier on a nanotrack is proposed. It exploits inter-skyrmion repulsion, mimicking the integrate-fire (IF) functionality of a biological neuron. The device threshold represented by the maximum number of skyrmions that can be pinned by the barrier can be tuned based on the particular current density employed on the nanotrack. The corresponding neuron spiking event occurs when a skyrmion overcomes the barrier. By raising the device threshold, lowering the barrier width and height, the operating current density of the device can be decreased to further enhance its energy efficiency. The proposed device paves the way for developing energy-efficient neuromorphic computing in antiferromagnetic spintronics.
In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.
Spintronic devices based on antiferromagnetic skyrmion (AFM) motion on the nanotracks have gained significant interest as a key component of neuromorphic data processing systems. AFM skyrmions are favorable over the ferromagnetic skyrmions as they follow the straight trajectories and prevent its annihilation at the nanotrack edges. In this paper, the AFM skyrmion-based neuron device that exhibits the leaky-integrate-fire (LIF) functionality is proposed for the first time. It exploits the current-driven skyrmion dynamics on the shape-configured nanotracks that are linearly decreasing and exponentially decaying. The device structure creates the regions from lower to higher energy states for the AFM skyrmions during its motion from the wider to narrower region. This causes the repulsion force from the nanotrack edges to act on the AFM skyrmion thereby, drifting it in the backward direction in order to minimize the system energy. This provides the leaking functionality to the neuron device without any external stimuli and additional hardware cost. The average velocities during the integration and leaky processes are in the order of 103 and 102 m/sec, respectively for the linearly and exponentially tapered nanotracks. Moreover, the energy of the skyrmion is in the order 10-20 J. Hence, the suggested device opens up the path for the development of high-speed and energy-efficient devices in antiferromagnetic spintronics for neuromorphic computing.
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