Digital Frequency Domain Multiplexer for mm-Wavelength Telescopes

An FPGA based digital signal processing (DSP) system for biasing and reading out multiplexed bolometric detectors for mm-wavelength telescopes is presented. This readout system is being deployed for balloon-borne and ground based cosmology experiments with the primary goal of measuring the signature of inflation with the Cosmic Microwave Background Radiation. The system consists of analog superconducting electronics running at 250mK and 4K, coupled to digital room temperature backend electronics described here. The digital electronics perform the real time functionality with DSP algorithms implemented in firmware. A soft embedded processor provides all of the slow housekeeping control and communications. Each board in the system synthesizes multi-frequency combs of 8 to 32 carriers in the MHz band to bias the detectors. After the carriers have been modulated with the sky-signal by the detectors, the same boards digitize the comb directly. The carriers are mixed down to base-band and low pass filtered. The signal bandwidth of 0.050 Hz - 100 Hz places extreme requirements on stability and requires powerful filtering techniques to recover the sky-signal from the MHz carriers.Comment: 6 pages, 6 figures, Submitted May 2007 to IEEE Transactions on Nuclear Science (TNS

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Sub-Nyquist Sampling: Bridging Theory and Practice

Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern applications, an increasingly number of functions is being pushed forward to sophisticated software algorithms, leaving only those delicate finely-tuned tasks for the circuit level. In this paper, we review sampling strategies which target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 50's of the previous century through recent publications from the past several years. The prime focus is bridging theory and practice, that is to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the hardware. In that spirit, we integrate contemporary theoretical viewpoints, which study signal modeling in a union of subspaces, together with a taste of practical aspects, namely how the avant-garde modalities boil down to concrete signal processing systems. Our hope is that this presentation style will attract the interest of both researchers and engineers in the hope of promoting the sub-Nyquist premise into practical applications, and encouraging further research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin

Basics of RF electronics

RF electronics deals with the generation, acquisition and manipulation of high-frequency signals. In particle accelerators signals of this kind are abundant, especially in the RF and beam diagnostics systems. In modern machines the complexity of the electronics assemblies dedicated to RF manipulation, beam diagnostics, and feedbacks is continuously increasing, following the demands for improvement of accelerator performance. However, these systems, and in particular their front-ends and back-ends, still rely on well-established basic hardware components and techniques, while down-converted and acquired signals are digitally processed exploiting the rapidly growing computational capability offered by the available technology. This lecture reviews the operational principles of the basic building blocks used for the treatment of high-frequency signals. Devices such as mixers, phase and amplitude detectors, modulators, filters, switches, directional couplers, oscillators, amplifiers, attenuators, and others are described in terms of equivalent circuits, scattering matrices, transfer functions; typical performance of commercially available models is presented. Owing to the breadth of the subject, this review is necessarily synthetic and non-exhaustive. Readers interested in the architecture of complete systems making use of the described components and devoted to generation and manipulation of the signals driving RF power plants and cavities may refer to the CAS lectures on Low-Level RF.Comment: 36 pages, contribution to the CAS - CERN Accelerator School: Specialised Course on RF for Accelerators; 8 - 17 Jun 2010, Ebeltoft, Denmar