Bandwidth (signal processing)

About: Bandwidth (signal processing) is a research topic. Over the lifetime, 48550 publications have been published within this topic receiving 600741 citations. The topic is also known as: Bandwidth (signal processing) & bandwidth.

A Mixed-Signal Multichip Neural Recording Interface With Bandwidth Reduction

Abstract: We present a multichip structure assembled with a medical-grade stainless-steel microelectrode array intended for neural recordings from multiple channels. The design features a mixed-signal integrated circuit (IC) that handles conditioning, digitization, and time-division multiplexing of neural signals, and a digital IC that provides control, bandwidth reduction, and data communications for telemetry toward a remote host. Bandwidth reduction is achieved through action potential detection and complete capture of waveforms by means of onchip data buffering. The adopted architecture uses high parallelism and low-power building blocks for safety and long-term implantability. Both ICs are fabricated in a CMOS 0.18-mum process and are subsequently mounted on the base of the microelectrode array. The chips are stacked according to a vertical integration approach for better compactness. The presented device integrates 16 channels, and is scalable to hundreds of recording channels. Its performance was validated on a testbench with synthetic neural signals. The proposed interface presents a power consumption of 138 muW per channel, a size of 2.30 mm2, and achieves a bandwidth reduction factor of up to 48 with typical recordings.

Coupling-Matrix Design of Dual and Triple Passband Filters

Abstract: The concept of the conventional coupling matrix is extended to include designs of dual- and triple-band filters. The multiband response is created by either placing transmission zeros within the bandwidth of a wideband filter or using higher order resonances. Realizable topologies both in planar and waveguide technologies can be imposed and associated coupling coefficients enforced during optimization. The design process is verified by measurements and comparison with results of commercially available field solvers

Time-Delay Estimation From Low-Rate Samples: A Union of Subspaces Approach

Abstract: Time-delay estimation arises in many applications in which a multipath medium has to be identified from pulses transmitted through the channel. Previous methods for time delay recovery either operate on the analog received signal, or require sampling at the Nyquist rate of the transmitted pulse. In this paper, we develop a unified approach to time delay estimation from low-rate samples. This problem can be formulated in the broader context of sampling over an infinite union of subspaces. Although sampling over unions of subspaces has been receiving growing interest, previous results either focus on unions of finite-dimensional subspaces, or finite unions. The framework we develop here leads to perfect recovery of the multipath delays from samples of the channel output at the lowest possible rate, even in the presence of overlapping transmitted pulses, and allows for a variety of different sampling methods. The sampling rate depends only on the number of multipath components and the transmission rate, but not on the bandwidth of the probing signal. This result can be viewed as a sampling theorem over an infinite union of infinite dimensional subspaces. By properly manipulating the low-rate samples, we show that the time delays can be recovered using the well-known ESPRIT algorithm. Combining results from sampling theory with those obtained in the context of direction of arrival estimation, we develop sufficient conditions on the transmitted pulse and the sampling functions in order to ensure perfect recovery of the channel parameters at the minimal possible rate.

Learning in the Frequency Domain

Abstract: Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed without accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well-known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.60% and 0.63% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1.42%. In addition, we observe a 0.8% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset.

A Blocker Filtering Technique for SAW-Less Wireless Receivers

Abstract: An active filtering technique to remove the out-of-band blockers in wireless receivers is presented. The circuit employs a feed-forward filtering path to produce an arbitrarily narrow frequency response in the low-noise amplifier (LNA), eliminating the need for an external surface acoustic wave (SAW) filter at the receiver front-end. The required notch filtering in the feed-forward path is realized through a receiver translational loop, driven by the same local oscillator (LO) signals used in the main receiver. For the proof of concept, a prototype amplifier in 65 nm standard CMOS, intended for Global System for Mobile Communication (GSM) applications, is implemented. When the filtering is enabled, the amplifier 3-dB bandwidth reduces from 220 MHz to about 4.5 MHz, and a stop-band rejection of over 21 dB is achieved.