Sampling Rate

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This paper describes with the help of MathCad, the main considerations, according to datasheet, in order to sample analog waveforms in continuous way, including basic signal conditioning, sample and hold, required A/D resolution and admitted error, etc.

This paper presents an algorithm for Electrocardiogram (ECG) analysis to detect and classify ECG waveform anomalies and abnormalities. This is achieved by extracting various features and durations of the ECG waveform such as RR interval, QRS complex, P wave and PR durations. These durations are then compared with normal values to determine the degree and types of abnormalities. Most of the data used for this study were extracted from the MIT-BIH arrhythmia database while some data was extracted from ECG recordings acquired specifically for the purposes of this study. The paper is concluded with detailed results obtained from testing the algorithm using the ECG data.

Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling strategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its band limit in hertz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling rate is exponentially lower than the Nyquist rate of W hertz. In contrast to Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.

In this paper, a general purpose wireless Brain- Machine -Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz . Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage...

This paper presents an algorithm for Electrocardiogram (ECG) analysis to detect and classify ECG waveform anomalies and abnormalities. This is achieved by extracting various features and durations of the ECG waveform such as RR interval, QRS complex, P wave and PR durations. These durations are then compared with normal values to determine the degree and types of abnormalities. Most of the data used for this study were extracted from the MIT-BIH arrhythmia database while some data was extracted from ECG recordings acquired specifically for the purposes of this study. The paper is concluded with detailed results obtained from testing the algorithm using the ECG data.

The design of switched-current decimators for wide bandwidth video filtering applications is presented. Applying topologies with only one input commutator to switched-currents allows the design of high speed polyphase input branches with reduced distortion. These concepts were utilized in the implementation of a linear phase 19 tap FIR filter chip with an amplitude response tailored to video applications. It is expected that the prototype filter implemented in a 0.5 μm CMOS process will operate at an input sampling rate of 135 MHz and with a decimating factor of 5

This paper deals with reconstruction filters and appropriate prefiltering techniques in the volume rendering. Sampling, resampling and reconstruction of continuous functions are used in many different fields. To satisfy conditions of Shannon's theorem, prefiltering of images or data volumes is usually required before sampling. The usual approach is to use a Gaussian prefilter, where the cutoff frequency is determined by the sampling rate, and one of the different reconstruction schemes is then applied. The problem is to determine a prefilter so that the reconstruction will reproduce the original object as accurately as possible. If the combination of prefiltering and reconstruction in the volumetric space is not adequate, aliasing artifacts will appear. Aliasing artifacts project from the three-dimensional space onto the projection plane and superpose to the artifacts caused by sampling and reconstruction in two dimensions. We present a methodology for designing filters based on optimization of combined operation of prefiltering and reconstruction postfiltering. This methodology offers a framework for comparing different reconstruction schemes in equal circumstances. The correct choice of reconstruction will also influence the derivative reconstruction which is used for shading. We demonstrate our results on examples in two- and three-dimensional space.

The ambiguity function, which is used to determine the target resolution and parameter estimation capabilities of a radar, of discrete-time signals is discussed. Both continuous-time, continuous-frequency and discrete-time, discrete-frequency ambiguity functions are presented. To avoid aliasing, it is shown that the sampling rate must be twice the Nyquist rate associated with the sampled signal

This paper introduces the concept of direct digital down-conversion via subsampling and digital quadrature tuning. In theory, a bandpass filtered intermediate frequency (IF) signal can be sampled by an analog-to-digital converter (ADC) with a sampling rate consistent with the pass band width and center frequency of the signal of interest. During the sampling process, the bandpass signal is constructively aliased to baseband. The practical limits of such an implementation are that the bandwidth and timing accuracy of the ADC's track and hold circuitry must be consistent with the IF location of the signal of interest. The subsampling ADC can be immediately followed by a quadrature digital tuner to provide fine tuning and produce a baseband quadrature signal ready for baseband processing. The combination of the subsampling ADC and digital quadrature tuner forms a complete digital IF processing chain.