The Novel LMS Algorithm without Frequency/Phase Offset Feedback in Real Time Coherent Optic Receiver

AbstractThe aim of this study was to examine delay discounting in girls and boys with ADHD-Combined type (ADHD-C) relative to typically developing (TD) children on two tasks that differ in the extent to which the rewards and delays were experienced by participants. Children ages 8–12 years with ADHD-C (n=65; 19 girls) and TD controls (n=55; 15 girls) completed two delay discounting tasks involving a series of choices between smaller, immediate and larger, delayed rewards. The classic delay discounting task involved choices about money at delays of 1–90 days and only some of the outcomes were actually experienced by the participants. The novel real-time discounting task involved choices about an immediately consumable reward (playing a preferred game) at delays of 25–100 s, all of which were actually experienced by participants. Participants also provided subjective ratings of how much they liked playing the game and waiting to play. Girls with ADHD-C displayed greater delay discounting compared to boys with ADHD-C and TD girls and boys on the real-time discounting task. Diagnostic group differences were not evident on the classic discounting task. In addition, children with ADHD-C reported wanting to play the game more and liking waiting to play the game less than TD children. This novel demonstration of greater delay discounting among girls with ADHD-C on a discounting task in which the rewards are immediately consumable and the delays are experienced in real-time informs our understanding of sex differences and motivational processes in children with ADHD. (JINS, 2016, 22, 12–23)

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Bacteria Detection and Differentiation Using Impedance Flow Cytometry

Sensors ◽

10.3390/s18103496 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3496 ◽

Cited By ~ 21

Author(s):

Casper Clausen ◽

Maria Dimaki ◽

Christian Bertelsen ◽

Gustav Skands ◽

Romen Rodriguez-Trujillo ◽

...

Keyword(s):

Drinking Water ◽

Real Time ◽

Electrical Response ◽

Solid Particles ◽

Electrical Impedance Spectroscopy ◽

Contaminated Water ◽

E Coli ◽

Two Samples ◽

Microbiological Control ◽

Frequency Phase

Monitoring of bacteria concentrations is of great importance in drinking water management. Continuous real-time monitoring enables better microbiological control of the water and helps prevent contaminated water from reaching the households. We have developed a microfluidic sensor with the potential to accurately assess bacteria levels in drinking water in real-time. Multi frequency electrical impedance spectroscopy is used to monitor a liquid sample, while it is continuously passed through the sensor. We investigate three aspects of this sensor: First we show that the sensor is able to differentiate Escherichia coli (Gram-negative) bacteria from solid particles (polystyrene beads) based on an electrical response in the high frequency phase and individually enumerate the two samples. Next, we demonstrate the sensor’s ability to measure the bacteria concentration by comparing the results to those obtained by the traditional CFU counting method. Last, we show the sensor’s potential to distinguish between different bacteria types by detecting different signatures for S. aureus and E. coli mixed in the same sample. Our investigations show that the sensor has the potential to be extremely effective at detecting sudden bacterial contaminations found in drinking water, and eventually also identify them.

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The Design of a Wavelet-Based Neural Network Adaptive Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2791 ◽

2013 ◽

Vol 860-863 ◽

pp. 2791-2795

Author(s):

Qian Xiao ◽

Yu Shan Jiang ◽

Ru Zheng Cui

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Real Time ◽

Adaptive Filter ◽

Adaptive Algorithm ◽

Distributed Storage ◽

Hopfield Neural Network ◽

Lms Algorithm ◽

The Neural Network ◽

Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

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Quantitative real-time RT-PCR data analysis: current concepts and the novel “gene expression’s C T difference” formula

Journal of Molecular Medicine ◽

10.1007/s00109-006-0097-6 ◽

2006 ◽

Vol 84 (11) ◽

pp. 901-910 ◽

Cited By ~ 510

Author(s):

Jan H. Schefe ◽

Kerstin E. Lehmann ◽

Ivo R. Buschmann ◽

Thomas Unger ◽

Heiko Funke-Kaiser

Keyword(s):

Data Analysis ◽

Real Time ◽

The Novel ◽

Rt Pcr ◽

Novel Gene ◽

Difference Formula

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ARTIST - a reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

Research Ideas and Outcomes ◽

10.3897/rio.5.e36464 ◽

2019 ◽

Vol 5 ◽

Cited By ~ 2

Author(s):

Konstantinos Kotis

Keyword(s):

Real Time ◽

Development Time ◽

Mixed Reality ◽

Research Approach ◽

The Novel ◽

High Quality ◽

Related Data ◽

Novel Methods ◽

Novel Concept ◽

Heterogeneous Resources

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.

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A reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

Research Ideas and Outcomes ◽

10.3897/rio.5.e36128 ◽

2019 ◽

Vol 5 ◽

Author(s):

Konstantinos Kotis

Keyword(s):

Real Time ◽

Development Time ◽

Mixed Reality ◽

Research Approach ◽

The Novel ◽

High Quality ◽

Related Data ◽

Novel Methods ◽

Novel Concept ◽

Heterogeneous Resources

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.

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Disposable silicon-based all-in-one micro-qPCR for rapid on-site detection of pathogens

Nature Communications ◽

10.1038/s41467-020-19911-6 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Estefania Nunez-Bajo ◽

Alexander Silva Pinto Collins ◽

Michael Kasimatis ◽

Yasin Cotur ◽

Tarek Asfour ◽

...

Keyword(s):

Real Time ◽

Limit Of Detection ◽

Low Cost ◽

High Specificity ◽

Rapid Screening ◽

Quantitative Detection ◽

The Novel ◽

Novel Coronavirus ◽

Silicon Based ◽

Specific Sequences

AbstractRapid screening and low-cost diagnosis play a crucial role in choosing the correct course of intervention when dealing with highly infectious pathogens. This is especially important if the disease-causing agent has no effective treatment, such as the novel coronavirus SARS-CoV-2, and shows no or similar symptoms to other common infections. Here, we report a disposable silicon-based integrated Point-of-Need transducer (TriSilix) for real-time quantitative detection of pathogen-specific sequences of nucleic acids. TriSilix can be produced at wafer-scale in a standard laboratory (37 chips of 10 × 10 × 0.65 mm in size can be produced in 7 h, costing ~0.35 USD per device). We are able to quantitatively detect a 563 bp fragment of genomic DNA of Mycobacterium avium subspecies paratuberculosis through real-time PCR with a limit-of-detection of 20 fg, equivalent to a single bacterium, at the 35th cycle. Using TriSilix, we also detect the cDNA from SARS-CoV-2 (1 pg) with high specificity against SARS-CoV (2003).

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Development of Novel Real-Time Radiation Systems Using 4-Channel Sensors

Sensors ◽

10.3390/s20092741 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2741

Author(s):

Yohei Inaba ◽

Masaaki Nakamura ◽

Masayuki Zuguchi ◽

Koichi Chida

Keyword(s):

Radiation Exposure ◽

Dose Rate ◽

Real Time ◽

Angular Dependence ◽

The Novel ◽

Medical Radiation ◽

Dose Linearity ◽

Tissue Injuries ◽

Low Sensitivity ◽

Shape And Size

Radiation-related tissue injuries after medical radiation procedures, such as fluoroscopically guided intervention (FGI), have been reported in patients. Real-time monitoring of medical radiation exposure administered to patients during FGI is important to avoid such tissue injuries. In our previous study, we reported a novel (prototype) real-time radiation system for FGI. However, the prototype sensor indicated low sensitivity to radiation exposure from the side and back, although it had high-quality fundamental characteristics. Therefore, we developed a novel 4-channel sensor with modified shape and size than the previous sensor, and evaluated the basic performance (i.e., measured the energy, dose linearity, dose rate, and angular dependence) of the novel and previous sensors. Both sensors of our real-time dosimeter system demonstrated the low energy dependence, excellent dose linearity (R2 = 1.0000), and good dose rate dependence (i.e., within 5% statistical difference). Besides, the sensitivity of 0° ± 180° in the horizontal and vertical directions was almost 100% sensitivity for the new sensor, which significantly improved the angular dependence. Moreover, the novel dosimeter exerted less influence on X-ray images (fluoroscopy) than other sensors because of modifying a small shape and size. Therefore, the developed dosimeter system is expected to be useful for measuring the exposure of patients to radiation doses during FGI procedures.

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