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IEEE Transactions on Biomedical... Jan 2023This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs...
This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs out-of-band noise suppression via a linear filter, and in-band noise suppression via a nonlinear filter. The nonlinear filter also enhances the QRS-waves by facilitating stochastic resonance. The processor identifies the QRS-waves on noise-suppressed and enhanced recordings using a constant threshold detector. For energy-efficiency and compactness, the processor exploits current-mode analog signal processing techniques, which significantly reduces the design complexity when implementing the second-order dynamics of the nonlinear filter. The processor is designed and implemented in TSMC 65 nm CMOS technology. In terms of detection performance, the processor achieves an average F1 = 99.88% over the MIT-BIH Arrhythmia database and outperforms all previous ultra-low power ECG processors. The processor is the first that is validated against noisy ECG recordings of MIT-BIH NST and TELE databases, where it achieves better detection performances than most digital algorithms run on digital platforms. The design has a footprint of 0.08 mm and dissipates 2.2 nW when supplied by a single 1V supply, making it the first ultra-low power and real-time processor that facilitates stochastic resonance.
PubMed: 37018643
DOI: 10.1109/TBCAS.2023.3235786 -
Journal of Dairy Science May 2018Over the last 25 years, whole-plant corn silage has become an important and popular feedstuff for dairy production. Copious research has been dedicated to the... (Review)
Review
Over the last 25 years, whole-plant corn silage has become an important and popular feedstuff for dairy production. Copious research has been dedicated to the development and evaluation of alternatives to enhance the nutritive value of whole-plant corn silage. These efforts have been aimed at manipulating the physical and chemical characteristics of whole-plant corn silage in an effort to maximize dairy profitability. Results from this review indicate that optimization of harvest maturity, kernel processing, theoretical length of cut, and cutting height improve or maintain the nutritive value and milk production of lactating dairy cows. Technological advancements have been developed and made available to dairy producers and corn growers desiring to enhance fiber and starch digestibility of whole-plant corn silage. Future research should be directed toward further assessment of new processors available in the market and the development of assessment methods for optimization of crop processor settings, harvest efficiency, and nutritional modeling.
Topics: Animal Feed; Animals; Cattle; Digestion; Food Handling; Nutritive Value; Silage; Zea mays
PubMed: 29685271
DOI: 10.3168/jds.2017-13728 -
Acta Otorrinolaringologica Espanola May 2021Osseointegrated auditory devices are hearing gadgets that use the bone conduction of sound to produce hearing improvement. The mechanisms and factors that contribute to...
BACKGROUND AND OBJECTIVE
Osseointegrated auditory devices are hearing gadgets that use the bone conduction of sound to produce hearing improvement. The mechanisms and factors that contribute to this sound transmission have been widely studied, however, there are other aspects that remain unknown, for instance, the influence of the processor power output. The aim of this study was to know if there is any relationship between the power output created by the devices and the hearing improvement that they achieve.
MATERIALS AND METHODS
Forty-four patients were implanted with a percutaneous Baha® 5 model. Hearing thresholds in pure tone audiometry, free-field audiometry, and speech recognition (in quiet and in noise) were measured pre and postoperatively in each patient .The direct bone conduction thresholds and the power output values from the processors were also obtained.
RESULTS
The pure tone average threshold in free field was 39.29dB (SD 9.15), so that the mean gain was 29.18dB (SD 10.13) with the device. This involved an air-bone gap closure in 63.64% of patients. The pure tone average threshold in direct bone conduction was 27.6dB (SD 10.91), which was 8.4dB better than the pure tone average threshold via bone conduction. The mean gain in speech recognition was 39.15% (SD 23.98) at 40dB and 36.66% (SD 26.76) at 60dB. The mean gain in the signal-to-noise ratio was -5.9dB (SD 4.32). On the other hand, the mean power output values were 27.95dB μN (SD 6.51) in G40 and 26.22dB μN (SD 6.49) in G60. When analysing the relationship between bone conduction thresholds and G40 and G60 values, a correlation from the frequency of 1,000Hz was observed. However, no statistically significant association between power output, functional gain or speech recognition gain was found.
CONCLUSIONS
The osseointegrated auditory devices generate hearing improvement in tonal thresholds and speech recognition, even in noise. Most patients closed the air-bone gap with the device. There is a direct relationship between the bone conduction threshold and the power output values from the processor, but only in mid and high frequencies. However, the relationship between power output and gain in speech recognition is weaker. Further investigation of contributing factors is necessary.
PubMed: 34082922
DOI: 10.1016/j.otorri.2021.01.004 -
IEEE Transactions on Neural Networks... Nov 2023Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground...
Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called "meta-learner." Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H2), lithium hydride (LiH), and helium hydride cation (HeH+). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.
PubMed: 35226607
DOI: 10.1109/TNNLS.2022.3151127 -
Micromachines Jul 2022Along with deep scaling transistors and complex electronics information exchange networks, very-large-scale-integrated (VLSI) circuits require high performance and... (Review)
Review
Along with deep scaling transistors and complex electronics information exchange networks, very-large-scale-integrated (VLSI) circuits require high performance and ultra-low power consumption. In order to meet the demand of data-abundant workloads and their energy efficiency, improving only the transistor performance would not be sufficient. Super high-speed microprocessors are useless if the capacity of the data lines is not increased accordingly. Meanwhile, traditional on-chip copper interconnects reach their physical limitation of resistivity and reliability and may no longer be able to keep pace with a processor's data throughput. As one of the potential alternatives, carbon nanotubes (CNTs) have attracted important attention to become the future emerging on-chip interconnects with possible explorations of new development directions. In this paper, we focus on the electrical, thermal, and process compatibility issues of current on-chip interconnects. We review the advantages, recent developments, and dilemmas of CNT-based interconnects from the perspective of different interconnect lengths and through-silicon-via (TSV) applications.
PubMed: 35888965
DOI: 10.3390/mi13071148 -
IScience Jan 2022Ultra-high chip power densities that are expected to surpass 1-2kW/cm in future high-performance systems cannot be easily handled by conventional cooling methods....
Ultra-high chip power densities that are expected to surpass 1-2kW/cm in future high-performance systems cannot be easily handled by conventional cooling methods. Various emerging cooling methods, such as liquid cooling via microchannels, thermoelectric coolers (TECs), two-phase vapor chambers, and hybrid cooling options have been designed to efficiently remove heat from high-performance processors. However, selecting the optimal cooling solution for a given chip and determining the optimal cooling parameters for that solution to achieve high efficiency are open problems. These problems are, in fact, computationally expensive because of the massive space of possible solutions. To address this design challenge, this article introduces a deep learning-based cooling design optimization flow that rapidly and accurately converges to the optimal cooling solution as well as the optimal cooling parameters for a given chip floorplan and its power profile.
PubMed: 35005532
DOI: 10.1016/j.isci.2021.103582 -
Journal of Neural Engineering May 2023A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different...
A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and different objectives. Traditional approaches, such as those currently used for deep brain stimulation, have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation.To achieve goal-directed closed-loop neurostimulation, we propose the use of brain co-processors, devices which exploit artificial intelligence to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. Here we investigate a specific type of co-processor called a 'neural co-processor' which uses artificial neural networks and deep learning to learn optimal closed-loop stimulation policies. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for futuretests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity in preparation for futuretests.Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function in the range 75%-90%.Our results provide the first proof-of-concept demonstration, using computer simulations, of a neural co-processor for adaptive activity-dependent closed-loop neurostimulation for optimizing a rehabilitation goal after injury. While a significant gap remains between simulations andapplications, our results provide insights on how such co-processors may eventually be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.
Topics: Humans; Artificial Intelligence; Algorithms; Brain; Neural Networks, Computer; Deep Brain Stimulation
PubMed: 37019099
DOI: 10.1088/1741-2552/accaa9 -
International Journal of Audiology May 2020The satisfaction experienced with using an audio processor is very important to hearing implant system users. Currently there are no measures that can be used to assess...
The satisfaction experienced with using an audio processor is very important to hearing implant system users. Currently there are no measures that can be used to assess user satisfaction with an audio processor. This study aims to develop and validate a specific and standardised questionnaire that focuses on user satisfaction with their audio processor. A preliminary version of the questionnaire was initially developed by experts in the field. Following validation of these results, the final version of the Audio Processor Satisfaction Questionnaire (APSQ) was developed consisting of 15 items. Item analyses and questionnaire validation measurements were assessed. Sixty-nine subjects were recruited and asked to complete the APSQ twice within 2-4 weeks.: Subjects reported a high user satisfaction with the questionnaire and with their audio processor. The questionnaire had good reliability and results for test-retest reliability were high and significant across all items and across subscale analyses. Item analyses and reliability analyses show that the questionnaire is a valid and reliable tool to assess user satisfaction across different audio processors and hearing implant systems. The APSQ is a quick and easy tool to measure user satisfaction with their audio processor.
Topics: Adolescent; Adult; Aged; Aged, 80 and over; Correction of Hearing Impairment; Female; Hearing Aids; Hearing Loss; Hearing Tests; Humans; Male; Middle Aged; Patient Satisfaction; Principal Component Analysis; Reproducibility of Results; Speech Perception; Surveys and Questionnaires; Young Adult
PubMed: 31944127
DOI: 10.1080/14992027.2019.1697830 -
ACS Nano Dec 2021A key goal of bottom-up synthetic biology is to construct cell- and tissue-like structures. Underpinning cellular life is the ability to process several external...
A key goal of bottom-up synthetic biology is to construct cell- and tissue-like structures. Underpinning cellular life is the ability to process several external chemical signals, often in parallel. Until now, cell- and tissue-like structures have been constructed with no more than one signaling pathway. Many pathways rely on signal transport across membranes using protein nanopores. However, such systems currently suffer from the slow transport of molecules. We have optimized the application of these nanopores to permit fast molecular transport, which has allowed us to construct a processor for parallel chemical signals from the bottom up in a modular fashion. The processor comprises three aqueous droplet compartments connected by lipid bilayers and operates in an aqueous environment. It can receive two chemical signals from the external environment, process them orthogonally, and then produce a distinct output for each signal. It is suitable for both sensing and enzymatic processing of environmental signals, with fluorescence and molecular outputs. In the future, such processors could serve as smart drug delivery vehicles or as modules within synthetic tissues to control their behavior in response to external chemical signals.
Topics: Lipid Bilayers; Lipid Droplets; Nanopores; Proteins; Water
PubMed: 34788543
DOI: 10.1021/acsnano.1c08217 -
Nature May 2021The most promising quantum algorithms require quantum processors that host millions of quantum bits when targeting practical applications. A key challenge towards...
The most promising quantum algorithms require quantum processors that host millions of quantum bits when targeting practical applications. A key challenge towards large-scale quantum computation is the interconnect complexity. In current solid-state qubit implementations, an important interconnect bottleneck appears between the quantum chip in a dilution refrigerator and the room-temperature electronics. Advanced lithography supports the fabrication of both control electronics and qubits in silicon using technology compatible with complementary metal oxide semiconductors (CMOS). When the electronics are designed to operate at cryogenic temperatures, they can ultimately be integrated with the qubits on the same die or package, overcoming the 'wiring bottleneck'. Here we report a cryogenic CMOS control chip operating at 3 kelvin, which outputs tailored microwave bursts to drive silicon quantum bits cooled to 20 millikelvin. We first benchmark the control chip and find an electrical performance consistent with qubit operations of 99.99 per cent fidelity, assuming ideal qubits. Next, we use it to coherently control actual qubits encoded in the spin of single electrons confined in silicon quantum dots and find that the cryogenic control chip achieves the same fidelity as commercial instruments at room temperature. Furthermore, we demonstrate the capabilities of the control chip by programming a number of benchmarking protocols, as well as the Deutsch-Josza algorithm, on a two-qubit quantum processor. These results open up the way towards a fully integrated, scalable silicon-based quantum computer.
PubMed: 33981049
DOI: 10.1038/s41586-021-03469-4