Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement
Nature Communications, 2022 Wave-based analog signal processing has been challenging for complex nonlinear operations such as data forecasting or classification. The authors propose here an analog neuromorphic platform for optical wave-based machine learning characterized by energy
Ali Momeni, Romain Fleury
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EEG Based Brain Computer Interfacing for Hand Assistant System Using Wavelet Transform [PDF]
ITM Web of Conferences, 2020 Robots have been of great use to mankind for several years. In situation where human body fails to operate as per the need robot’s functions in those situations quite efficiently.
Dhongade Dayanand, Patil Mukesh
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From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals [PDF]
, 2009 Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support ...
Eldar, Yonina C., Mishali, Moshe
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Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification
Sensors, 2013 A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouri´er Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of
Sina Balkir +3 more
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Hybrid integrators with predictive overload estimation for analog computers and continuous-time ΔΣ modulators [PDF]
Advances in Radio Science, 2023 Continuous-time integrators are a central component in ΔΣ modulators, in analog computers, and general analog signal processing. If several integrators are interconnected, scaling plays an important role: In analog computers, scaling is performed with ...
D. Killat, B. Ulmann, S. Köppel
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CMOS realization of a 2-layer CNN universal machine chip [PDF]
, 2002 Some of the features of the biological retina can be modelled by a cellular neural network (CNN) composed of two dynamically coupled layers of locally connected elementary nonlinear processors.
Carmona Galán, Ricardo +4 more
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Integrate-and-fire circuit for converting analog signals to spikes using phase encoding
Neuromorphic Computing and Engineering, 2023 Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end ...
Javier Lopez-Randulfe +2 more
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Xampling: Signal Acquisition and Processing in Union of Subspaces
, 2011 We introduce Xampling, a unified framework for signal acquisition and processing of signals in a union of subspaces. The main functions of this framework are two.
Eldar, Yonina C. +2 more
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Special Considerations in Estate Planning for Same-Sex and Unmarried Couples [PDF]
, 2013 Sub-Nyquist sampling makes use of sparsities in analog signals to sample them at a rate lower than the Nyquist rate. The reduction in sampling rate, however, comes at the cost of additional digital signal processing (DSP) which is required to reconstruct
Johansson, Håkan +1 more
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Hybrid-cascade Coupled-Line Phasers for High-resolution Radio-Analog Signal Processing [PDF]
, 2014 A hybrid-cascade (HC) coupled-line phaser configuration is presented to synthesize enhanced group delay responses for high-resolution Radio-Analog Signal Processing (R-ASP).
Caloz +6 more
core +3 more sources