lördag 30 augusti 2014

Towards a single-chip, implantable RFID system: is a single-cell radio possible?

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896640/

Biomed Microdevices. Aug 2010; 12(4): 589–596.

Published online Jan 24, 2009. doi: 10.1007/s10544-008-9266-4

PMCID: PMC2896640

Towards a single-chip, implantable RFID system: is a single-cell radio possible?

Peter Burke corresponding author

and Christopher Rutherglen

Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA 92697 USA

Peter Burke, Email: ude.icu@ekrubp.

Corresponding author.

Author information ▼ Copyright and License information ►

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Introduction

When James Clerk Maxwell discovered the displacement current, hence completing Maxwell’s equations, in 1865, the stage was set for the wireless transmission of information. It another 75 years for the semiconductor industry to be born and mature into the field it is today. The modicum anytime anywhere access to information is now taken for granted. Today, every student in every science and engineering field is taught the fundamental basis of electromagnetics and wireless propagation of signals and information.

The next 100 years will see a similar revolution in the integration of information technology and biotechnology, also called biomedical engineering. The field of telemedicine (e.g. scanned patient records, email or virtual office visits, etc.) has access to wireless technology and is beginning to deploy and utilize the vast, low cost information processing capacity. In this paper, we wish to address a more general issue, that of interfacing the physical world with the biological world.

For interrogation of biological systems, one is generally interested in a chemical or physical quantity. For a chemical quantity, typical assays determine the presence or concentration of a protein, antibody, or small molecule anylate, the presence or concentration of a particular DNA or RNA, or even more subtle quantities such as the phosphorylation state of an enzyme.

In general, these biomedically relevant physical quantities are sensed and turned into a measurable optical or electronic signal. Because of the broad availability of low cost high performance electronics, the development of electronic technologies will be more of concern to us here. The use of electronic interrogation of biological function can be integrated into a Si CMOS chip at potentially low cost. However, there is an important issue of how to interface the CMOS chip to the outside world, and this is where RFID comes in.

The use of RFID for identification and sensing has been demonstrated in many applications. In general the acronym is used to refer only to identifying the presence of an RFID system. However, in this paper we wish to extend this definition and propose a more general RFID system that can both identify its presence and also interrogate its local chemical environment. Thus, in this work, RFID is used not just to refer to identification, but rather to measure and possibly effect biological function.

Figure Figure11 below poses the central question of this paper: Is a single-cell radio possible? Such a system would be able to interrogate and possibly affect biological function inside a single, living cell. This paper is not to be considered a comprehensive or complete review, but rather a discussion of some fundamental issues in scaling down RFID to the single cell level, and a presentation of future research directions.

Fig. 1

Single cell radio concept

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RFID state of the art

The field of RFID in general is a complex field, with many applications in industry, medicine, and commerce (Finkenzeller 2003; Ahson and Ilyas 2008). Overall size reduction is not the primary goal in most applications; rather the cost is the most important factor. The development of single chip RFID solutions is not then the primary goal of industry to date. However, in this paper we argue that it could lead to lower cost, and especially smaller size, for biomedical implants. In addition, it seems the most appropriate technology vector for implantable biomedical microdevices. We next review current efforts in this direction.

Small chips

Hitachi(Usami et al.2003, 2007) has developed technology for progressively smaller die sizes for RFID tags. The latest size is 50

5 μm, and shown as an example in Fig. 2 (compiled from Ref. (Usami et al.2007)) below. This demonstrates the feasibility of small (microscopic) chips for RFID, however the antenna was external and adds significantly to the system size, discussed next.

Fig. 2

Big antennas

Although the Hitachi work has demonstrated very small die sizes for the memory, the antenna must be external, and is typically cm or so in size. This is generally achieved via an off-chip antenna. However, recent research in on-chip antennas(Bouvier et al.1997; Abrial et al.2001; O et al.2005; Shamin et al.2005,2008; Guo et al.2006) has demonstrated the ability to fabricate smaller RF antennas on the same chip as the signal processing components. Using either GHz near-field antenna or MHz inductively coupled coils, researchers have shown of order 1 mW of available DC power on chip (from the RF field) in an area of order 1 mm2.

In Fig. 3 (compiled from Refs. (Guo et al.2006; Usami et al.2007)), the OCA operates at 2.45 GHz. The on-chip circuitry uses the energy from the incoming RF field to power itself, so that no battery is needed. In this case, 1 mW is available to power the on-chip circuitry. The antenna size is roughly a few mm by a few mm. As Fig. 3 shows, the antenna is still much larger than the active circuitry of the Hitachi microchip, inserted to scale for reference. Thus, while a major advance in integration and size reduction (compared to the cm scale external antennas typically used), there is still vast room for improvement in miniaturization of this RFID device.

Fig. 3

Biomedical/commercial efforts

It is one thing to demonstrate a lab prototype, but another to demonstrate and certify and in-vivo system. Several commercialization efforts towards this end are underway at various stages of maturity already. At least three companies have developed implantable RFID sensors (SMS, BioRasis, and ISSYS). SMS and BioRasis have targeted glucose sensors. ISSYS has targeted MEMs based pressure sensors. While detailed designs are not published, they use a heterogeneous (as opposed to single chip) approaches.

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Towards a nano-radio

Our group recently demonstrated what could be called the world’s smallest radio (Fig. 4 (Rutherglen and Burke )), which consisted of an AM demodulator made of a single carbon nanotube (a molecular scale tube with radius of order 1 nm). However, the external antenna was several cm in length, and the audio amplifier, speaker, and power supply (battery) were off the shelf, so the entire system volume was of order 10−3 m3. A similar elegant nano-mechanical radio (operating in a high-vacuum environment) was also recently demonstrated by UC Berkeley(Jensen et al.).

Fig. 4

Carbon nanotube radio

We next discuss the size of circuitry, antenna, and complete system size. The goal is to understand how the system size can be minimized, and how small it can go.

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Summary of small radios

We have compiled in Table 1 below some representative sizes for the circuit, antenna, and complete radio system, from the literature (Bouvier et al.1997; Abrial et al.2001; Hill 2003; Usami et al.2003; Rutherglen and Burke ; Usami et al.2007). This comparison is meant to give an overview of various technical approaches (and so is not to be considered an “apples to apples” comparison), and to illustrate the state of the art and the relative importance of antenna volume in total system size. From this it is clear that the small circuit size is possible, but having a small antenna size is more challenging.

Table 1

Estimated circuit, antenna, and system size for various radios complied from the literature

We also show in Table 1 our estimate for the size of a possible single-chip radio using “COTS” (commercial off the shelf) technology (discussed below), as well as possible advances using nanotechnology. These estimates show that a single cell radio is not completely out of reach using existing technology. In Fig. 5 below, we show the system size and single cell size of various existing and possible radio systems.

Fig. 5

Sizes of various existing and proposed radios

Nano-antennas

The field of antenna studies which are smaller than an electrical wavelength is termed electrically small antennas. We have proposed, for example, to use novel quantum properties of a single carbon nanotube to make a resonant antenna with size about 100× smaller than a classical dipole antenna for a given frequency. Such a concept is indicated schematically in Fig. 6 below(Burke et al.2006).

Fig. 6

Carbon nanotube antenna concept

While the technology to build such prototype antennas exists(Li et al.2004; Yu et al.2004), the predicted losses due to ohmic currents in the arms of the antenna are severe. Our simple estimate is −90 dB loss from this effect (Burke et al.2006). More rigorous calculations are in progress along these lines. In principle, this loss can be overcome by higher intensity input radiation. However, this could result in significant heating of the antenna itself and possibly the surrounding tissue. This issue of heat, energy, voltage and current is discussed in more depth below.

Nano-antenna vs. classical antenna

As discussed above, nano-antennas have severe energy loss problems due to the ohmic currents in the arms of the antennas. On the other hand, in classical metal antennas these ohmic currents are insignificant and generally avoided by having large enough antennas. The issue of the transition from nano-antenna to classical antenna is not yet worked out in detail, and an important topic for future study. Using existing technology it should be possible to design tiny (micron scale) low-loss metallic antennas that do not absorb much radiation, and hence can be integrated into a small, single cell radio.

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RF nano-heaters

Above, we have argued ohmic losses in small antennas are a problem for signal reception and delivery to circuitry. Another approach to the absorption of RF power is to use it as a local heater, which can be used to effect biochemistry at the nanoscale for nanotechnology investigations and potential therapeutic applications. This is another form of “RF remote control” of biological function, which uses heat rather than circuitry to control chemistry. While there are many possibilities, we summarize two examples from the literature using various forms of RF Nano-heaters: One targeted towards therapeutics, and the other a more basic proof of principle demonstration.

Therapeutic heaters

When heated above a certain temperatures, tissues undergo necrosis; this effect is called thermoablation. The fundamental hypothesis of thermoablative cancer therapy currently under study is that RF absorbing nanoparticles can be selectively localized to cancer tissue (using, e.g., antibodies to target specific tissues), allowing heat to be applied locally with minimal or no damage to surrounding, healthy tissue. Both nanoparticles and nanotube antennas have been proposed for this purpose.

Iron oxide nanoparticles can be used to absorb RF or AC magnetic field energy through eddy currents. Several proof of principles studies have been published(Ito et al., ; DeNardo et al.; Ivkov et al.; Sonvico et al.; Wust et al.; Majewski and Thierry 2007). Carbon nanotubes can also serve as nano-antenna absorbers to heat locally tissue. However, the specific absorption of the material is not well-understood at the current state of the art(Gannon et al.). In all cases, in addition to better understanding of the RF nano-antenna interaction, the most significant issue to be resolved are the pharmico-kinetics and localization of the absorbers with sufficient specificity to allow targeted application of heat for therapeutic effects with little or no damage to surrounding tissue.

RF remote control: turning heat to biochemical activity

DNA hybridization/dehybridization is well know as a temperature sensitive event. In (Hamad-Schifferli et al.), Au nanoparticles (NP) were covalently linked to DNA, and exposed to a 1 GHz RF field. This caused heating of the Au NPs, and reversible dehybridization. This is clear demonstration of RF remote control of molecular scale biochemistry. Further development of such techniques, similar to therapeutic techniques discussed above, will require close attention to local heating at the nanoscale, clearly addressing global RF absorption of the surrounding environment.

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Heater vs. radio: what is information?

For a truly nanoscale radio, the concepts of integrated electronic memory, signal processing, and currents and voltages may need to be revised. The two-way flow of information as depicted schematically in Fig. 1above is a traditional way of thinking about information flow in engineering systems.

Is it possible to have remote control of cell operation using RF in vivo, with different concepts from the current/voltage used in electrical RF engineering? Some ideas deal with heating, but this is just a step. We have only scratched the surface. We have fundamental concepts of heat for nanodetectors, and current/voltage for macro/microcircuits. The Au NP RF absorption (Hamad-Schifferli et al.) is a translation of heat to a biomolecular event. However, signal transduction in cells occurs by completely different “circuit elements”, by phosphorylization and de-phosphorylization of proteins by kinases and concomitant enzyme allosteric conformational activity, and the resultant binding/unbinding of DNA expression inhibitors/promoters, and the subsequent concentration of proteins expressed by various genes. Also, small molecule concentration ratios (e.g. ATP to ADP) provided metabolic information. How to translate between these two information carrying worlds is a question that should be addressed by the academic community, and may hold promise for many diverse biomedical applications yet to be dreamed of.

Fundamental limits

In some sense, the world of electrical engineering is incompatible with the signal processing hardware prevalent in biological systems. So, scaling down the entire “top down” RFID circuit project may be doomed to failure from the get go. In any case, it should be interesting to determine what the limits are on the development of traditional RF engineering and single cell RFID. Ultimately, there is a fundamental relationship between energy, entropy, heat, and information, and we conjecture this will set a fundamental limit on the interaction between electronics and biological systems at the single cell and biochemical level.

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Prospects for the future

Above we have discussed the state of the art RFID technologies as well as some general issues regarding scaling these technologies down to the single cell limit. We next discuss possible new and future research directions towards the goal of integrated nano-radio systems. We propose three broad directions, in increasing levels of ambition, challenge, and significance.

Step 1: a vision for a unified, single-chip universal platform

A common, uniform, and standardized interface between a single-chip radio and the outside world would be very useful for the research community. Our proposed goal is indicated schematically in Fig. 7 below, and would achieve the following milestones:

100×100×5 μm CMOS radio chip
On-chip-antenna
No battery required
Two-way communication enabled
Real-estate for custom sensors (e.g. nanowire sensors, electrochemical sensors, nanotube sensors, etc.)

Fig. 7

Micro-radio concept

The development of such a “platform” would enable the research community to integrate (post fabrication) various sensors onto the chip. As a first application, glucose sensors for diabetes monitoring would be a good target. However, in general there is a myriad of possible biological events to be monitored in vivo, and new sensing technologies are constantly emerging. A single RF platform to interface to these new technologies would be a significantly accelerate the application of new sensing and nanotechnologies in the life sciences and biomedical device field.

Challenges

The first challenge regards the antenna, and the amount of RF power required to couple if the antenna is electrically small. For a tiny radio to work, would RF fields need to be so high as to damage surrounding tissue? Are ultra-high frequency (e.g. 60 GHz) signals needed for small enough antennas, and how is this going to propagate through tissue? The on-chip-antennas developed above are already sub-mm in length, so the prospect of a 0.1

0.1 mm antenna seems feasible short term. The goal of integrating this onto a CMOS chip is also feasible, since it has been demonstrated in other applications as well.

Step 2: development of a single cell radio?

Once step 1 is achieved, the next goal will be to determine the fundamental scaling limit of such a tiny RFID chip technology. Key challenges will be the miniaturization of the antenna, and getting enough power coupled through smaller and smaller antennas. Miniaturization of the circuitry itself may also become an issue as the circuitry will need to fit into an area comparable to one square micron. This would enable roughly 1000 components in the 30 nm node, so resource starved circuitry and computing would need to be seriously addressed.

These research challenges are difficult engineering issues, but all of them seem fundamentally solvable. In particular, new fabrication technologies are not needed or assumed in the development of a single cell size radio. However, to get a radio that will fit into a cell easily, nanotechnology will be needed, as is discussed next.

Step 3: integrated nanosystems

The long term goal of a sub-micron sized nano-robot with computing and communications ability is the realm of nanotechnology, and currently does not exist. This goal can be best stated as a vision, because the particular roadmap to get there (and even the best route) cannot be clearly laid out given our current state of knowledge of how RF fields interact with nanosystems. We have proposed one possible implementation of such a vision, using nanotube antennas and frequency domain multiplexing for high-bandwidth communication with integrated nanosystems (Fig. 8). One of the main challenges (in addition to the RF interface) is the development of integrated nano-circuits, which also is predicated on economical, precision nano-fabrication. The latter is a grand challenge beyond the scope of this paper, but being addressed by the research community at large via self-assembly, DNA controlled assembly, etc. The vision of a nano-radio is thus to be considered one (important) application of a yet to be developed nanotechnology.

Fig. 8

Possible architecture for integrated nanosystems

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Conclusions

The key components to reducing radio size are the antenna and the battery. Using the RF field to generate on-chip power completely obviates the need for the battery. Using an on-chip antenna allows for smaller system sizes. Even with an on-chip antenna, the system sizes demonstrated to date and immediately feasible are dominated by the antenna size, not the circuitry. In order to address this issue, we have proposed nano-antennas. More research is needed to address the trade-offs between efficiency, required external power, antenna size, and heating. A single chip (including antenna) radio system (with room for on-board sensors) of size 100

100 μm by 1 μm seems feasible with current technology. It remains an open question whether such an approach can be taken to develop a single-cell radio system. A true nano-radio will require further developments in nano-fabrication technology, an issue which is currently being addressed at a world-wide level.

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Acknowledgments

Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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References

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fredag 29 augusti 2014

Middle ear microphone aims to improve cochlear implants

http://www.gizmag.com/middle-ear-microphone/22343/

torsdag 28 augusti 2014

Mark of the Beast : Obama introduces the RFID BRAIN Initiative to read your thoughts (Apr 3, 2013)

https://www.youtube.com/watch?v=tW5l_P641j0

onsdag 27 augusti 2014

1000000W (1 million watt) from satelite to get a activeRFID to function, and this work together

https://www.youtube.com/watch?v=FwspDjkDEL4

https://www.youtube.com/watch?v=FwspDjkDEL4

fredag 22 augusti 2014

Photo RAMBO Microchip

lördag 22 augusti 2014

söndag 10 augusti 2014

favorit i reprice från 20 mars 2014

140810 Rambochip

favorite in reprice från 20 mars 2014

Richard Jan Azim Svanberg

Delas offentligt - 20 mar 2014

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RAMBO Microchip

TelepathHero

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data compression

http://en.wikipedia.org/wiki/Data_compression#cite_note-30

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In computer science and information theory, data compression, source coding,^[1] or bit-rate reductioninvolves encoding information using fewer bits than the original representation

Lossy

In lossy audio compression, methods of psychoacoustics are used to remove non-audible (or less audible) components of the audio signal. Compression of human speech is often performed with even more specialized techniques; speech coding, or voice coding, is sometimes distinguished as a separate discipline from audio compression. Different audio and speech compression standards are listed underaudio codecs. Voice compression is used in Internet telephony, for example audio compression is used for CD ripping and is decoded by audio players.^[10]

Audio

Lossy audio compression algorithms provide higher compression at the cost of fidelity and are used in numerous audio applications. These algorithms almost all rely on psychoacoustics to eliminate less audible or meaningful sounds, thereby reducing the space required to store or transmit them.^[16]

Lossy audio compression

The innovation of lossy audio compression was to use psychoacoustics to recognize that not all data in an audio stream can be perceived by the human auditory system. Most lossy compression reduces perceptual redundancy by first identifying perceptually irrelevant sounds, that is, sounds that are very hard to hear. Typical examples include high frequencies or sounds that occur at the same time as louder sounds. Those sounds are coded with decreased accuracy or not at all.

Coding methods

To determine what information in an audio signal is perceptually irrelevant, most lossy compression algorithms use transforms such as the modified discrete cosine transform (MDCT) to convert time domainsampled waveforms into a transform domain. Once transformed, typically into the frequency domain, component frequencies can be allocated bits according to how audible they are. Audibility of spectral components calculated using the absolute threshold of hearing and the principles of simultaneous masking—the phenomenon wherein a signal is masked by another signal separated by frequency—and, in some cases, temporal masking—where a signal is masked by another signal separated by time.Equal-loudness contours may also be used to weight the perceptual importance of components. Models of the human ear-brain combination incorporating such effects are often called psychoacoustic models.^[20]

Other types of lossy compressors, such as the linear predictive coding (LPC) used with speech, are source-based coders. These coders use a model of the sound's generator (such as the human vocal tract with LPC) to whiten the audio signal (i.e., flatten its spectrum) before quantization. LPC may be thought of as a basic perceptual coding technique: reconstruction of an audio signal using a linear predictor shapes the coder's quantization noise into the spectrum of the target signal, partially masking it.^[21]

Lossy formats are often used for the distribution of streaming audio or interactive applications (such as the coding of speech for digital transmission in cell phone networks). In such applications, the data must be decompressed as the data flows, rather than after the entire data stream has been transmitted. Not all audio codecs can be used for streaming applications, and for such applications a codec designed to stream data effectively will usually be chosen.^[22]

Speech encoding[edit]

Speech encoding is an important category of audio data compression. The perceptual models used to estimate what a human ear can hear are generally somewhat different from those used for music. The range of frequencies needed to convey the sounds of a human voice are normally far narrower than that needed for music, and the sound is normally less complex. As a result, speech can be encoded at high quality using a relatively low bit rate.

If the data to be compressed is analog (such as a voltage that varies with time), quantization is employed to digitize it into numbers (normally integers). This is referred to as analog-to-digital (A/D) conversion. If the integers generated by quantization are 8 bits each, then the entire range of the analog signal is divided into 256 intervals and all the signal values within an interval are quantized to the same number. If 16-bit integers are generated, then the range of the analog signal is divided into 65,536 intervals.

This relation illustrates the compromise between high resolution (a large number of analog intervals) and high compression (small integers generated). This application of quantization is used by several speech compression methods. This is accomplished, in general, by some combination of two approaches:

Only encoding sounds that could be made by a single human voice.
Throwing away more of the data in the signal—keeping just enough to reconstruct an "intelligible" voice rather than the full frequency range of human hearing.

Perhaps the earliest algorithms used in speech encoding (and audio data compression in general) were the A-law algorithm and the µ-law algorithm.

History

The world's first commercial broadcast automation audio compression system was developed by Oscar Bonello, an Engineering professor at the University of Buenos Aires.^[24] In 1983, using the psychoacoustic principle of the masking of critical bands first published in 1967,^[25] he started developing a practical application based on the recently developed IBM PC computer, and the broadcast automation system was launched in 1987 under the name Audicom. Twenty years later, almost all the radio stations in the world were using similar technology manufactured by a number of companies.

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Data compression

From Wikipedia, the free encyclopedia

"Source coding" redirects here. For the term in computer programming, see Source code.

In computer science and information theory, data compression, source coding,^[1] or bit-rate reductioninvolves encoding information using fewer bits than the original representation.^[2] Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by identifying unnecessary information and removing it.^[3] The process of reducing the size of a data file is popularly referred to as data compression, although its formal name is source coding (coding done at the source of the data before it is stored or transmitted).^[4]

Compression is useful because it helps reduce resource usage, such as data storage space or transmission capacity. Because compressed data must be decompressed to use, this extra processing imposes computational or other costs through decompression; this situation is far from being a free lunch. Data compression is subject to a space–time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (e.g., when using lossy data compression), and the computational resources required to compress and uncompress the data.^[5]

Lossless[edit]

Lossless data compression algorithms usually exploit statistical redundancy to represent data more concisely without losing information, so that the process is reversible. Lossless compression is possible because most real-world data has statistical redundancy. For example, an image may have areas of colour that do not change over several pixels; instead of coding "red pixel, red pixel, ..." the data may be encoded as "279 red pixels". This is a basic example of run-length encoding; there are many schemes to reduce file size by eliminating redundancy.

The Lempel–Ziv (LZ) compression methods are among the most popular algorithms for lossless storage.^[6] DEFLATE is a variation on LZ optimized for decompression speed and compression ratio, but compression can be slow. DEFLATE is used in PKZIP, Gzip and PNG. LZW (Lempel–Ziv–Welch) is used in GIF images. Also noteworthy is the LZR (Lempel-Ziv–Renau) algorithm, which serves as the basis for the Zip method. LZ methods use a table-based compression model where table entries are substituted for repeated strings of data. For most LZ methods, this table is generated dynamically from earlier data in the input. The table itself is often Huffman encoded (e.g. SHRI, LZX). A current LZ-based coding scheme that performs well is LZX, used in Microsoft's CAB format.

The best modern lossless compressors use probabilistic models, such as prediction by partial matching. The Burrows–Wheeler transform can also be viewed as an indirect form of statistical modelling.^[7]

The class of grammar-based codes are gaining popularity because they can compress highly repetitive text, extremely effectively, for instance, biological data collection of same or related species, huge versioned document collection, internet archives, etc. The basic task of grammar-based codes is constructing a context-free grammar deriving a single string. Sequitur and Re-Pair are practical grammar compression algorithms for which public codes are available.

In a further refinement of these techniques, statistical predictions can be coupled to an algorithm calledarithmetic coding. Arithmetic coding, invented by Jorma Rissanen, and turned into a practical method by Witten, Neal, and Cleary, achieves superior compression to the better-known Huffman algorithm and lends itself especially well to adaptive data compression tasks where the predictions are strongly context-dependent. Arithmetic coding is used in the bi-level image compression standard JBIG, and the document compression standard DjVu. The text entry system Dasher is an inverse arithmetic coder.^[8]

Lossy[edit]

Lossy data compression is the converse of lossless data compression. In these schemes, some loss of information is acceptable. Dropping nonessential detail from the data source can save storage space. Lossy data compression schemes are informed by research on how people perceive the data in question. For example, the human eye is more sensitive to subtle variations in luminance than it is to variations in color. JPEG image compression works in part by rounding off nonessential bits of information.^[9] There is a corresponding trade-off between preserving information and reducing size. A number of popular compression formats exploit these perceptual differences, including those used in music files, images, and video.

Lossy image compression can be used in digital cameras, to increase storage capacities with minimal degradation of picture quality. Similarly, DVDs use the lossy MPEG-2 Video codec for video compression.

Theory[edit]

The theoretical background of compression is provided by information theory (which is closely related toalgorithmic information theory) for lossless compression and rate–distortion theory for lossy compression. These areas of study were essentially forged by Claude Shannon, who published fundamental papers on the topic in the late 1940s and early 1950s. Coding theory is also related. The idea of data compression is deeply connected with statistical inference.^[11]

Machine learning[edit]

Data differencing[edit]

Main article: Data differencing

Data compression can be viewed as a special case of data differencing:^[13]^[14] Data differencing consists of producing a difference given a source and a target, with patching producing a target given a source and a difference, while data compression consists of producing a compressed file given a target, and decompression consists of producing a target given only a compressed file. Thus, one can consider data compression as data differencing with empty source data, the compressed file corresponding to a "difference from nothing." This is the same as considering absolute entropy (corresponding to data compression) as a special case of relative entropy (corresponding to data differencing) with no initial data.

When one wishes to emphasize the connection, one may use the term differential compression to refer to data differencing.

Outlook and currently unused potential[edit]

It is estimated that the total amount of the data that are stored on the world's storage devices could be further compressed with existing compression algorithms by a remaining average factor of 4.5:1. It is estimated that the combined technological capacity of the world to store information provides 1,300exabytes of hardware digits in 2007, but when the corresponding content is optimally compressed, this only represents 295 exabytes of Shannon information.^[15]

Uses[edit]

Audio[edit]

Lossy audio compression[edit]

Comparison of acoustic spectrograms of a song in an uncompressed format and lossy formats. That the lossy spectrograms are different from the uncompressed one indicates that they are, in fact, lossy, but nothing can be assumed about the effect of the changes on perceived quality.

Lossy audio compression is used in a wide range of applications. In addition to the direct applications (mp3 players or computers), digitally compressed audio streams are used in most video DVDs, digital television, streaming media on the internet, satellite and cable radio, and increasingly in terrestrial radio broadcasts. Lossy compression typically achieves far greater compression than lossless compression (data of 5 percent to 20 percent of the original stream, rather than 50 percent to 60 percent), by discarding less-critical data.^[19]

Due to the nature of lossy algorithms, audio quality suffers when a file is decompressed and recompressed (digital generation loss). This makes lossy compression unsuitable for storing the intermediate results in professional audio engineering applications, such as sound editing and multitrack recording. However, they are very popular with end users (particularly MP3) as a megabyte can store about a minute's worth of music at adequate quality.

Coding methods[edit]

Latency results from the methods used to encode and decode the data. Some codecs will analyze a longer segment of the data to optimize efficiency, and then code it in a manner that requires a larger segment of data at one time to decode. (Often codecs create segments called a "frame" to create discrete data segments for encoding and decoding.) The inherent latency of the coding algorithm can be critical; for example, when there is a two-way transmission of data, such as with a telephone conversation, significant delays may seriously degrade the perceived quality.

In contrast to the speed of compression, which is proportional to the number of operations required by the algorithm, here latency refers to the number of samples that must be analysed before a block of audio is processed. In the minimum case, latency is zero samples (e.g., if the coder/decoder simply reduces the number of bits used to quantize the signal). Time domain algorithms such as LPC also often have low latencies, hence their popularity in speech coding for telephony. In algorithms such as MP3, however, a large number of samples have to be analyzed to implement a psychoacoustic model in the frequency domain, and latency is on the order of 23 ms (46 ms for two-way communication)).

Speech encoding[edit]

Only encoding sounds that could be made by a single human voice.
Throwing away more of the data in the signal—keeping just enough to reconstruct an "intelligible" voice rather than the full frequency range of human hearing.

Perhaps the earliest algorithms used in speech encoding (and audio data compression in general) were the A-law algorithm and the µ-law algorithm.

History[edit]

Solidyne 922: The world's first commercial audio bit compression card for PC, 1990

A literature compendium for a large variety of audio coding systems was published in the IEEE Journal on Selected Areas in Communications (JSAC), February 1988. While there were some papers from before that time, this collection documented an entire variety of finished, working audio coders, nearly all of them using perceptual (i.e. masking) techniques and some kind of frequency analysis and back-end noiseless coding.^[23] Several of these papers remarked on the difficulty of obtaining good, clean digital audio for research purposes. Most, if not all, of the authors in the JSAC edition were also active in the MPEG-1 Audio committee.

Video[edit]

Encoding theory[edit]

Video data may be represented as a series of still image frames. The sequence of frames contains spatial and temporal redundancy that video compression algorithms attempt to eliminate or code in a smaller size. Similarities can be encoded by only storing differences between frames, or by using perceptual features of human vision. For example, small differences in color are more difficult to perceive than are changes in brightness. Compression algorithms can average a color across these similar areas to reduce space, in a manner similar to those used in JPEG image compression.^[28] Some of these methods are inherently lossy while others may preserve all relevant information from the original,uncompressed video.

One of the most powerful techniques for compressing video is interframe compression. Interframe compression uses one or more earlier or later frames in a sequence to compress the current frame, while intraframe compression uses only the current frame, effectively being image compression.^[29]

The most powerful used method works by comparing each frame in the video with the previous one. If the frame contains areas where nothing has moved, the system simply issues a short command that copies that part of the previous frame, bit-for-bit, into the next one. If sections of the frame move in a simple manner, the compressor emits a (slightly longer) command that tells the decompressor to shift, rotate, lighten, or darken the copy. This longer command still remains much shorter than intraframe compression. Interframe compression works well for programs that will simply be played back by the viewer, but can cause problems if the video sequence needs to be edited.^[30]

Because interframe compression copies data from one frame to another, if the original frame is simply cut out (or lost in transmission), the following frames cannot be reconstructed properly. Some video formats, such as DV, compress each frame independently using intraframe compression. Making 'cuts' in intraframe-compressed video is almost as easy as editing uncompressed video: one finds the beginning and ending of each frame, and simply copies bit-for-bit each frame that one wants to keep, and discards the frames one doesn't want. Another difference between intraframe and interframe compression is that, with intraframe systems, each frame uses a similar amount of data. In most interframe systems, certain frames (such as "I frames" in MPEG-2) aren't allowed to copy data from other frames, so they require much more data than other frames nearby.^[31]

It is possible to build a computer-based video editor that spots problems caused when I frames are edited out while other frames need them. This has allowed newer formats like HDV to be used for editing. However, this process demands a lot more computing power than editing intraframe compressed video with the same picture quality.

Today, nearly all commonly used video compression methods (e.g., those in standards approved by theITU-T or ISO) apply a discrete cosine transform (DCT) for spatial redundancy reduction. The DCT that is widely used in this regard was introduced by N. Ahmed, T. Natarajan and K. R. Rao in 1974.^[32] Other methods, such as fractal compression, matching pursuit and the use of a discrete wavelet transform(DWT) have been the subject of some research, but are typically not used in practical products (except for the use of wavelet coding as still-image coders without motion compensation). Interest in fractal compression seems to be waning, due to recent theoretical analysis showing a comparative lack of effectiveness of such methods.^[33]

Timeline[edit]

The following table is a partial history of international video compression standards.

History of Video Compression Standards
Year	Standard	Publisher	Popular Implementations
1984	H.120	ITU-T
1988	H.261	ITU-T	Videoconferencing, Videotelephony
1993	MPEG-1 Part 2	ISO, IEC	Video-CD
1995	H.262/MPEG-2 Part 2	ISO, IEC, ITU-T	DVD Video, Blu-ray, Digital Video Broadcasting,SVCD
1996	H.263	ITU-T	Videoconferencing, Videotelephony, Video on Mobile Phones (3GP)
1999	MPEG-4 Part 2	ISO, IEC	Video on Internet (DivX, Xvid ...)
2003	H.264/MPEG-4 AVC	Sony, Panasonic, Samsung,ISO, IEC, ITU-T	Blu-ray, HD DVD Digital Video Broadcasting,iPod Video, Apple TV,
2009	VC-2 (Dirac)	SMPTE	Video on Internet, HDTV broadcast, UHDTV
2013	H.265	ISO, IEC, ITU-T

Genetics[edit]

Genetics compression algorithms are the latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and genetic algorithms adapted to the specific datatype. In 2012, a team of scientists from Johns Hopkins University published a genetic compression algorithm that does not use a reference genome for compression. HAPZIPPER was tailored for HapMap data and achieves over 20-fold compression (95% reduction in file size), providing 2- to 4-fold better compression and in much faster time than the leading general-purpose compression utilities. For this, Chanda, Elhaik, and Bader introduced MAF based encoding (MAFE), which reduces the heterogeneity of the dataset by sorting SNPs by their minor allele frequency, thus homogenizing the dataset.^[34] Other algorithms in 2009 and 2013 (DNAZip and GenomeZip) have compression ratios of up to 1200-fold—allowing 6 billion basepair diploid human genomes to be stored in 2.5 megabytes (relative to a reference genome or averaged over many genomes).^[35]^[36]

lördag 30 augusti 2014

Towards a single-chip, implantable RFID system: is a single-cell radio possible?

Abstract

Introduction

RFID state of the art

Small chips

Big antennas

Biomedical/commercial efforts

Towards a nano-radio

Summary of small radios

Nano-antennas

Nano-antenna vs. classical antenna

RF nano-heaters

Therapeutic heaters

RF remote control: turning heat to biochemical activity

Heater vs. radio: what is information?

Fundamental limits

Prospects for the future

Step 1: a vision for a unified, single-chip universal platform

Challenges

Step 2: development of a single cell radio?

Step 3: integrated nanosystems

Conclusions

Acknowledgments

References

fredag 29 augusti 2014

http://www.gizmag.com/middle-ear-microphone/22343/

torsdag 28 augusti 2014

Mark of the Beast : Obama introduces the RFID BRAIN Initiative to read your thoughts (Apr 3, 2013)

onsdag 27 augusti 2014

fredag 22 augusti 2014

lördag 22 augusti 2014

söndag 10 augusti 2014

favorit i reprice från 20 mars 2014

http://en.wikipedia.org/wiki/Data_compression#cite_note-30

Audio

Lossy audio compression

Coding methods

Speech encoding[edit]

History

Data compression

Contents

Lossless[edit]

Lossy[edit]

Theory[edit]

Machine learning[edit]

Data differencing[edit]

Outlook and currently unused potential[edit]

Uses[edit]

Audio[edit]

Lossy audio compression[edit]

Coding methods[edit]

Speech encoding[edit]

History[edit]

Video[edit]

Encoding theory[edit]

Timeline[edit]

Genetics[edit]

See also[edit]