Digital Signal Processing

1908 Submissions

[4] viXra:1908.0632 [pdf] submitted on 2019-08-31 12:31:10

Microprocessor of Carbon Nanotube

Authors: George Rajna
Comments: 68 Pages.

After years of tackling numerous design and manufacturing challenges, MIT researchers have built a modern microprocessor from carbon nanotube transistors, which are widely seen as a faster, greener alternative to their traditional silicon counterparts. [40] Now Shulaker and his team in Department of Electrical Engineering and Computer Science, alongside researchers at Analog Devices, Inc.(ADI) also in Massachusetts USA, have taken on a series of challenges that have hampered carbon nanotube (CNT) computers since the first carbon nanotube transistors were reported in the late 1990s. [39]
Category: Digital Signal Processing

[3] viXra:1908.0486 [pdf] replaced on 2019-11-02 12:26:57

Minimizing Acquisition Maximizing Inference a Demonstration on Print Error Detection

Authors: Suyash Shandilya
Comments: 11 Pages.

Is it possible to detect a feature in an image without ever being able to look at it? Images are known to be very redundant in spatial domain. When transformed to bases like Discrete Cosine Transform (DCT) or wavelets, they acquire a sparser (more effective) representation. Compressed Sensing is a technique which proposes simultaneous acquisition and compression of any signal by taking very few random linear measurements (M) instead of uniform samples at more than twice the bandwidth frequency (Shannon-Nyquist theorem). The quality of reconstruction directly relates with M, which should be above a certain threshold (determined by the level of sparsity, k) for a reliable recovery. Since these measurements can non-adaptively reconstruct the signal to a faithful extent using purely analyticalmethodslikeBasisPursuit,MatchingPursuit,Iterativethresholding,etc.,wecanbeassured that these compressed samples contain enough information about any relevant macro-level feature contained in the (image) signal. Thus if we choose to deliberately acquire an even lower number of measurements-inordertothwartthepossibilityofacomprehensiblereconstruction,buthighenough to infer whether a relevant feature exists in an image - we can achieve accurate image classification while preserving its privacy. Through the print error detection problem, it is demonstrated that such a novel system can be implemented in practise.
Category: Digital Signal Processing

[2] viXra:1908.0275 [pdf] submitted on 2019-08-14 02:34:20

Diffractive Neural Network

Authors: George Rajna
Comments: 49 Pages.

A new paper in Advanced Photonics demonstrates distinct improvements to the inference and generalization performance of diffractive optical neural networks. [29] A team of researchers from the University of Münster, the University of Oxford and the University of Exeter has built an all-optical neural network on a single chip. [28] Physicists from Petrozavodsk State University have proposed a new method for oscillatory neural network to recognize simple images. Such networks with an adjustable synchronous state of individual neurons have, presumably, dynamics similar to neurons in the living brain. [27] Navid Borhani, a research-team member, says this machine learning approach is much simpler than other methods to reconstruct images passed through optical fibers, which require making a holographic measurement of the output. [26]
Category: Digital Signal Processing

[1] viXra:1908.0259 [pdf] submitted on 2019-08-12 10:58:34

Improve Optical Data Transmission

Authors: George Rajna
Comments: 80 Pages.

Engineers at the University of Illinois have found a way to redirect misfit light waves to reduce energy loss during optical data transmission. [43] Engineers at the University of California San Diego have developed the thinnest optical device in the world—a waveguide that is three layers of atoms thin. [42] A group of researchers led by Professor Myakzyum Salakhov has been working on the problem of optical states in plasmonic-photonic crystals (PPCs). [41] Such plasmonic resonances have significant roles in biosensing with ability to improve the resolution and sensitivity required to detect particles at the scale of the single molecule. [40]
Category: Digital Signal Processing