Compression of Medical Sensor Data [Exploratory DSP

Wegener, Al

doi:10.1109/msp.2010.936781

Cited by 14 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the tree configuration in Fig. 4, the frames from tree nodes [5,6] and [5,7] are replaced by two frames from tree node [4,3]. The frames from tree nodes [5,16], [5,17], [5,18], and [5,19] have been replaced by four frames from tree node [3,4].…”

Section: A Signal X(n) Is Filtered By Two Filters G(z) and H(z)mentioning

confidence: 99%

“…8, the G(z) and H(z) filter blocks have been replaced by bypass blocks below the selected tree nodes. For example, the filter outputs at tree nodes [5,6] and [5,7] are ignored and are replaced by the filter output from tree node [4,3]. The even output samples of Y [4,3] show up at tree node [5,6], and the odd samples show up at tree node [5,7].…”

Section: Partial Tree Configurationmentioning

confidence: 99%

“…Since the initial target application is the sensor data compression of ultrasound imaging, the focus of our work is on 1-D signals. The proposed H/W architecture is a crucial part in the implementation of an all-digital receiver for a wide class of medical imaging systems [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Flexible Hardware Architecture for Wavelet Packet Transform With Arbitrary Tree Structure

Garcia

Mansour

Ali

2013

IEEE Trans. Circuits Syst. II

View full text Add to dashboard Cite

We present a new hardware architecture for implementing the orthogonal wavelet packet transform with an arbitrary wavelet tree. The architecture is flexible enough to accommodate an arbitrary wavelet filter and an arbitrary tree up to a certain depth. The tree structure is specified through an efficient register parameterization architecture. We provide a detailed description of the different modules and complexity estimates for the application-specified integrated circuit implementation of the proposed architecture.Index Terms-Application-specified integrated circuit (ASIC), sensor compression, wavelet packets, wavelet tree.

show abstract

Section: A Signal X(n) Is Filtered By Two Filters G(z) and H(z)mentioning

confidence: 99%

Section: Partial Tree Configurationmentioning

confidence: 99%

See 1 more Smart Citation

A Flexible Hardware Architecture for Wavelet Packet Transform With Arbitrary Tree Structure

Garcia

Mansour

Ali

2013

IEEE Trans. Circuits Syst. II

View full text Add to dashboard Cite

show abstract

“…In many cases the features themselves are extracted from multiple signal data such as vehicle telemetry [16], medical sensors [14], weather forecasting [10], and financial time-series analysis [13]. When features are extracted from signal data, it is possible for feature redundancy to be either between features extracted from the same signal (intra-signal), or between features extracted from different signals (inter-signal).…”

Section: Introductionmentioning

confidence: 99%

Redundant Feature Selection for Telemetry Data

Taylor

Griffths

Bhalerao

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Feature sets in many domains often contain many irrelevant and redundant features, both of which have a negative effect on the performance and complexity of agents that use the data [8]. Supervised feature selection aims to overcome this problem by selecting features that are highly related to the class labels, yet unrelated to each other. One proposed technique to select good features with few inter-dependencies is minimal Redundancy Maximal Relevance (mRMR) [11], but this can be impractical with large feature sets. In many situations, features are extracted from signal data such as vehicle telemetry, medical sensors, or financial time-series, and it is possible for feature redundancies to exist both between features extracted from the same signal (intra-signal), and between features extracted from different signals (inter-signal). We propose a two stage selection process to take advantage of these different types of redundancy, considering intra-signal and inter-signal redundancies separately. We illustrate the process on vehicle telemetry signal data collected in a driver distraction monitoring project. We evaluate it using several machine learning algorithms: Random Forest; Naïve Bayes; and C4.5 Decision Tree. Our results show that this two stage process significantly reduces the computation required because of inter-dependency calculations, while having minimal detrimental effect on the performance of the feature sets produced.

show abstract

“…The need for smart components has become evident and intelligent integrated circuits (I 2 C) based upon horizontal integration of radically differing technologies have been clearly identified as the driving force for future generation of products. As an example, a nanophotonic clinic based on system on chip (SoC) and system on system (SoS) technologies can be created using multifunctional nanoparticles in which therapeutic or monitoring agents are encapsulated and activated or monitored with either electromagnetic or light waves [1][2][3]. Embedded nano-based biophotonic sensors, for example that could be powered and monitored remotely to observe a wide range of physiological conditions such as status of internal tissues, blood sugar and oxygen level, blood pressure etc., would provide important enabling capabilities for robotic microsurgery.…”

mentioning

confidence: 99%

Evolution of Nonvolatile Resistive Switching Memory Technologies: The Related Influence on Hetrogeneous Nanoarchitectures

Eshraghian¹

2010

Transactions on Electrical and Electronic Materials

View full text Add to dashboard Cite

To gain an economic advantage in the last decade many organizations have invested billions of dollars in research and development programs that span over a varied range of activities from smart pills for image sensing and monitoring, smart drug delivery for targeted therapy, through to more futuristic products based on memristive systems and metamaterials. The need for smart components has become evident and intelligent integrated circuits (I 2 C) based upon horizontal integration of radically differing technologies have been clearly identified as the driving force for future generation of products. As an example, a nanophotonic clinic based on system on chip (SoC) and system on system (SoS) technologies can be created using multifunctional nanoparticles in which therapeutic or monitoring agents are encapsulated and activated or monitored with either electromagnetic or light waves [1][2][3]. Embedded nano-based biophotonic sensors, for example that could be powered and monitored remotely to observe a wide range of physiological conditions such as status of internal tissues, blood sugar and oxygen level, blood pressure etc., would provide important enabling capabilities for robotic microsurgery. The integration of bio-devices with non-biological material provides higher performance in applications such as biomolecular motors for targeted drag delivery and bio-micro-electromechanical systems as analyte detector system with recognition capability of antigen-antibody. The ability to transmit and receive data efficiently will be a major challenge-as the consequence the 'first centimeter' communication (body sensors and clothing) as part of the over-all communications strategy brings about new opportunities in health care and aged care environments. Such progress supported by bio-based technologies provide the foundation for a future in which information sensing, embedded processing, imaging and multi-level communication become pervasive [4,5].Light-wave technology (photonics) synonymous with high speed networks is also beginning to show its footprint in areas that not so long ago appeared to be far removed from the conventional information communication technology arena. Photonics has always been viewed as an alternate to electronic systems to transmit, distribute, and process high volumes of digital The emergence of different and disparate materials together with the convergence of both the 'old' and 'emerging' technologies is paving the way for integration of heterogeneous technologies that are likely to extend the limitations of silicon technology beyond the roadmap envisaged for complementary metal-oxide semiconductor. Formulation of new information processing concepts based on novel aspects of nano-scale based materials is the catalyst for new nanoarchitectures driven by a different perspective in realization of novel logic devices. The memory technology has been the pace setter for silicon scaling and thus far has pave the way for new architectures. This paper provides an overview of the inevitability of hetero...

show abstract

Compression of Medical Sensor Data [Exploratory DSP

Cited by 14 publications

References 4 publications

A Flexible Hardware Architecture for Wavelet Packet Transform With Arbitrary Tree Structure

A Flexible Hardware Architecture for Wavelet Packet Transform With Arbitrary Tree Structure

Redundant Feature Selection for Telemetry Data

Evolution of Nonvolatile Resistive Switching Memory Technologies: The Related Influence on Hetrogeneous Nanoarchitectures

Contact Info

Product

Resources

About