Chip

About: Chip is a research topic. Over the lifetime, 44555 publications have been published within this topic receiving 352381 citations.

Coherent Spread Spectrum Systems

Abstract: Synchronous Pseudonoise Coded Spread Spectrum Systems. PCM Code Formatting and Spectra. Modulation of Multi-channel Coherent Systems. Phase-Locked Loops. Tracking With Suppressed Carrier Loops. Coherent Detection of Coherent Modulated Signals. An Introduction to Linear Pseudonoise Sequences. PN Code Spread Spectrum Systems. PN Code Acquisition and Lock Detection Performance. PN Code Tracking. Gold Codes. Symbol Synchronizatin for PSK. Appendixes. Index.

Compact spectrometer based on a disordered photonic chip

Abstract: Light scattering in disordered media has been studied extensively due to its prevalence in natural and artificial systems. In photonics most of the research has focused on understanding and mitigating the effects of scattering, which are often detrimental. For certain applications, however, intentionally introducing disorder can actually improve device performance, as in photovoltaics. Here, we demonstrate a spectrometer based on multiple light scattering in a silicon-on-insulator chip featuring a random structure. The probe signal diffuses through the chip generating wavelength-dependent speckle patterns, which are detected and used to recover the input spectrum after calibration. A spectral resolution of 0.75 nm at a wavelength of 1,500 nm in a 25-μm-radius structure is achieved. Such a compact, high-resolution spectrometer is well suited for lab-on-a-chip spectroscopy applications. A miniature spectrometer has been developed that employs light scattering in a photonic chip with a random structure. It generates wavelength-dependent speckle patterns, which are detected and analysed to recover the spectrum of the input signal. It has a resolution of 0.75 nm in the 1,500 nm wavelength region.

Methods for making a device for concurrently processing multiple biological chip assays

Abstract: Methods for concurrently processing multiple biological chip assays by providing a biological chip plate comprising a plurality of test wells, each test well having a biological chip having a molecular probe array; introducing samples into the test wells; subjecting the biological chip plate to manipulation by a fluid handling device that automatically performs steps to carry out reactions between target molecules in the samples and probes; and subjecting the biological chip plate to a biological chip plate reader that interrogates the probe arrays to detect any reactions between target molecules and probes.

A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations

Abstract: Memristors and memristor crossbar arrays have been widely studied for neuromorphic and other in-memory computing applications. To achieve optimal system performance, however, it is essential to integrate memristor crossbars with peripheral and control circuitry. Here, we report a fully functional, hybrid memristor chip in which a passive crossbar array is directly integrated with custom-designed circuits, including a full set of mixed-signal interface blocks and a digital processor for reprogrammable computing. The memristor crossbar array enables online learning and forward and backward vector-matrix operations, while the integrated interface and control circuitry allow mapping of different algorithms on chip. The system supports charge-domain operation to overcome the nonlinear I–V characteristics of memristor devices through pulse width modulation and custom analogue-to-digital converters. The integrated chip offers all the functions required for operational neuromorphic computing hardware. Accordingly, we demonstrate a perceptron network, sparse coding algorithm and principal component analysis with an integrated classification layer using the system. A programmable neuromorphic computing chip based on passive memristor crossbar arrays integrated with analogue and digital components and an on-chip processor enables the implementation of neuromorphic and machine learning algorithms.

Model-based analysis of tiling-arrays for ChIP-chip

Abstract: We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/∼wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms.