A level-crossing sampling scheme for A/D conversion

Sayiner, N.; Sorensen, H.V.; Viswanathan, T.R.

doi:10.1109/82.488288

Cited by 215 publications

(104 citation statements)

References 5 publications

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“…The constants and in (6) , then is an orthonormal basis. The frame bounds can be determined as the highest and lowest eigenvalues of the frame correlation matrix , with the th element…”

Section: B Frames and Basesmentioning

confidence: 99%

“…The problem of signal reconstruction from its nonuniform frequency-domain samples arises in computerized tomography (CT) and magnetic resonance imaging (MRI) [5]. There are also applications where we can benefit from introducing irregular sampling (e.g., quantization methods [6]). In all of these examples, standard reconstruction techniques developed mainly for uniformly spaced samples [2] cannot be applied.…”

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confidence: 99%

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Nonuniform Sampling of Periodic Bandlimited Signals

Margolis

Eldar

2008

IEEE Trans. Signal Process.

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Abstract-Digital processing techniques are based on representing a continuous-time signal by a discrete set of samples. This paper treats the problem of reconstructing a periodic bandlimited signal from a finite number of its nonuniform samples. In practical applications, only a finite number of values are given. Extending the samples periodically across the boundaries, and assuming that the underlying continuous time signal is bandlimited, provides a simple way to deal with reconstruction from finitely many samples. Two algorithms for reconstructing a periodic bandlimited signal from an even and an odd number of nonuniform samples are developed. In the first, the reconstruction functions constitute a basis while in the second, they form a frame so that there are more samples than needed for perfect reconstruction. The advantages and disadvantages of each method are analyzed. Specifically, it is shown that the first algorithm provides consistent reconstruction of the signal while the second is shown to be more stable in noisy environments. Next, we use the theory of finite dimensional frames to characterize the stability of our algorithms. We then consider two special distributions of sampling points: uniform and recurrent nonuniform, and show that for these cases, the reconstruction formulas as well as the stability analysis are simplified significantly. The advantage of our methods over conventional approaches is demonstrated by numerical experiments.

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“…The constants and in (6) , then is an orthonormal basis. The frame bounds can be determined as the highest and lowest eigenvalues of the frame correlation matrix , with the th element…”

Section: B Frames and Basesmentioning

confidence: 99%

mentioning

confidence: 99%

Nonuniform Sampling of Periodic Bandlimited Signals

Margolis

Eldar

2008

IEEE Trans. Signal Process.

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“…If the input signal crosses any level, a new sample of the input signal will be taken. Many other studies about adaptive data processing were based on the Level-Crossing approach [5,6,7]. Another method called variable sampling interval X chart (VSI X Chart) [8] was introduced by Reynolds et al In this method a target value and a signal region are defined to control a certain process.…”

Section: Related Workmentioning

confidence: 99%

Adaptive data sensing rate in ad-hoc sensor networks for autonomous transport application

Wang

Jabbari

Jedermann

et al. 2010

2010 13th International Conference on Information Fusion

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-A novel algorithm for adaptive control of the data sensing rate in wireless ad-hoc sensor networks (WSN) is presented. The WSN is applied to monitor the environmental parameters e.g. temperature and humidity, inside containers for the food transportation. Using the proposed algorithm, each sensor node can control its sensing intervals according to the surrounding environmental conditions. The data sink receives only the required information from data sources, so that the process of data fusion could be optimized by minimizing the overall data transmissions in WSN. In order to approximate the development trend of the environment, a neural network algorithm is used due to its learning capability and flexibility. The algorithm of adaptive sensing rate is tested in a simulation environment and implemented in a refrigerator truck for a food transport purpose. Both the simulation and implementation results show that the algorithm can autonomously concentrate on the problematic situation and efficiently reduce the redundant data measurements.

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“…However, another design is to replace the conventional A/D converter in the above structures with an Asynchronous, or Level-Crossing, ADC. As introduced in [19], [20], the level crossing ADC samples the signal when it passes a predetermined threshold level, instead of at discrete time intervals as performed in traditional ADCs. Thus, the main output of the level-crossing ADC is the time elapsed as the signal has moved between two pre-determined levels, Figure 5.…”

Section: B Low Power Analog Front End Architecturementioning

confidence: 99%

Concepts and architecture for a thumb-sized smart IoT ultrasound measurement system

Delsing

Deventer

Eliasson

et al. 2016

2016 IEEE International Ultrasonics Symposium (IUS)

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Abstract-This paper presents the technology concepts for a "thumb"-sized self-contained ultrasonic IoT measurement system. An overall architecture is proposed, and key elements are discussed with solutions using existing technology, thus arguing that realization is possible with the current technology.Such an ultrasonic IoT measurement system is constrained by its size and available energy, although it requires at least decent computational and communication resources. Because streaming data from such a device is not advisable from an energy viewpoint, there is a need for resource efficient (energy, memory and computational power) data analysis.An architecture with the following parts as well as some implementation details and performance data are proposed here:• Energy supply, battery and super capacitor • Transducer excitation achieving almost zero electrical losses • Event detection sensor interface • Data aggregation using sparse approximation and learned feature dictionaries, adapted to resource constrained embedded systems • IoT communication protocols and implementations enabling event -based communication and System of Systems integration capabilities The optimization of system level performance requires each subsystem to be optimized for the specific measurement situation taking into account the subsystem interdependencies. This can be performed using a combined electrical and acoustical model of the system. Here, the model allowing electronic and acoustic co-simulation using SPICE is an important tool bridging the electronic and acoustic domains.

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A level-crossing sampling scheme for A/D conversion

Cited by 215 publications

References 5 publications

Nonuniform Sampling of Periodic Bandlimited Signals

Nonuniform Sampling of Periodic Bandlimited Signals

Adaptive data sensing rate in ad-hoc sensor networks for autonomous transport application

Concepts and architecture for a thumb-sized smart IoT ultrasound measurement system

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