Real-time digital signal processor implementation of self-calibrating pulse-shape discriminator for high purity germanium

Pulse-shape analysis of the ionization signals from germanium gamma-ray spectrometers is a method for obtaining information that can characterize an event beyond just the total energy deposited in the crystal. However, as typically employed, this method is data-intensive requiring the digitization, transfer, and recording of electronic signals from the spectrometer. A hardware realization of a real-time digital signal processor for implementing a parametric pulse shape is presented. Specifically, a previously developed method for distinguishing between single-site and multi-site gamma-ray interactions is demonstrated in an on-line digital signal processor, compared with the original off-line pulse-shape analysis routine, and shown to have no significant difference. Reduction of the amount of the recorded information per event is shown to translate into higher duty-cycle data acquisition rates while retaining the benefits of additional event characterization from pulse-shape analysis.Comment: Accepted by NIM

SIM-DSP: A DSP-Enhanced CAD Platform for Signal Integrity Macromodeling and Simulation

Macromodeling-Simulation process for signal integrity verifications has become necessary for the high speed circuit system design. This paper aims to introduce a “VLSI Signal Integrity Macromodeling and Simulation via Digital Signal Processing Techniques” framework (known as SIM-DSP framework), which applies digital signal processing techniques to facilitate the SI verification process in the pre-layout design phase. Core identification modules and peripheral (pre-/post-)processing modules have been developed and assembled to form a verification flow. In particular, a single-step discrete cosine transform truncation (DCTT) module has been developed for modeling-simulation process. In DCTT, the response modeling problem is classified as a signal compression problem, wherein the system response can be represented by a truncated set of non-pole based DCT bases, and error can be analyzed through Parseval’s theorem. Practical examples are given to show the applicability of our proposed framework

A software definable MIMO testbed: architecture and functionality

Following the intensive theoretical studies of recently emerged MIMO technology, a variety of performance measures become important to investigate the challenges and trade-offs at various levels throughout MIMO system design process. This paper presents a review of the MIMO testbed recently set up at King’s College London. The architecture that distinguishes the testbed as a flexible and reconfigurable system is first preseneted. This includes both the hardware and software aspects, and is followed by a discussion of implementation methods and evaluation of system research capabilities

Analysis and application of digital spectral warping in analog and mixed-signal testing

Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation

Real-time adaptive filtering of dental drill noise using a digital signal processor

The application of noise reduction methods requires the integration of acoustics engineering and digital signal processing, which is well served by a mechatronic approach as described in this paper. The Normalised Least Mean Square (NLMS) algorithm is implemented on the Texas Instruments TMS320C6713 DSK Digital Signal Processor (DSP) as an adaptive digital filter for dental drill noise. Blocks within the Matlab/Simulink Signal Processing Blockset and the Embedded Target for TI C6000 DSP family are used. A working model of the algorithm is then transferred to the Code Composer Studio (CCS), where the desired code can be linked and transferred to the target DSP. The experimental rig comprises a noise reference microphone, a microphone for the desired signal, the DSK and loudspeakers. Different load situations of the dental drill are considered as the noise characteristics change when the drill load changes. The result is that annoying drill noise peaks, which occur in a frequency range from 1.5 kHz to 10 kHz, are filtered out adaptively by the DSP. Additionally a schematic design for its implementation in a dentist’s surgery will also be presented

Overview of Parallel Platforms for Common High Performance Computing

The paper deals with various parallel platforms used for high performance computing in the signal processing domain. More precisely, the methods exploiting the multicores central processing units such as message passing interface and OpenMP are taken into account. The properties of the programming methods are experimentally proved in the application of a fast Fourier transform and a discrete cosine transform and they are compared with the possibilities of MATLAB's built-in functions and Texas Instruments digital signal processors with very long instruction word architectures. New FFT and DCT implementations were proposed and tested. The implementation phase was compared with CPU based computing methods and with possibilities of the Texas Instruments digital signal processing library on C6747 floating-point DSPs. The optimal combination of computing methods in the signal processing domain and new, fast routines' implementation is proposed as well