Detection Of Multiple Signals Research Articles

Electrospinning fibrous membrane matrix ionic capacitive sensor for pressure value and distribution detection

Considering the practical applications of capacitive sensors, electrical sensing fibrous membranes should own high mechanical strength, elasticity, sensitivity, thermal diffusivity and biocompatibility, listing the basic design and preparation demands via electrostatic spinning. Polyvinylidene fluoride (PVDF) fibrous membrane matrix pressure ionic capacitive sensors were prepared via electrostatic spinning. The addition and dissolution of polyvinylpyrrolidone (PVP) particles realized porous structures of PVDF fibrous membrane, providing silver particles (Ag) dependence matrix for conductive Ag-porous PVDF fibrous membrane of electrode layer. The PVDF matrix capacitive sensor was composed of sandwiching dielectric layer of PVDF fibrous membrane with ionic liquid (IL-PVDF) between two electrode layers. The construction of electrical double layers at the interfaces between electrode layer and dielectric layer significantly enhanced capacitance sensing properties. After encapsulating by pure PVDF fibrous membranes, PVDF fibrous membrane matrix ionic capacitive sensor exhibited excellent stability, sensitivity and durability in pressure loading range of 0–8 kPa for 5000 cyclic experiments，which was suitable for real-time detection of multiple physiological bending signals. Based on pressure sensing array construction, the values and distribution of static and dynamic loads were reflected accurately and intuitively, providing feasible methods for practical applications of functional fiber matrix sensors in fields of healthcare and human-machine interaction.

Chaos detection scheme for multiple variable-frequency signals with overlapping frequencies

As one of the most critical technologies in signal detection, weak signal detection has received a great deal of attention in many areas. Traditional methods perform well when the number of signal is single and the form of signal is simple, which are quite different from the situation in some real applications (e.g., the marine communication scenario). In this paper, we consider the problem of high-precision frequency detection of multiple variable-frequency signals with overlapping frequencies (MVFS-O) in the low signal-to-noise ratio scenario, whose key point is how to detect the parameters (frequency range and frequency modulation rate) of each signal. To solve the problem, a novel chaos detection scheme based on Duffing difference oscillator is proposed. In this scheme, the wide frequency detection range of the critical state is used to determine the frequency range of the signals quickly, and the Lyapunov exponent is used to discriminate the output state of the system qualitatively. Furthermore, combining with the time points of the periodic state of the system, we can obtain the FMR of the corresponding signal by analyzing overlapping frequency ratio. Numerical results are provided to verify the practical effectiveness of the proposed scheme.

Single-Line Multi-Channel Flexible Stress Sensor Arrays.

Flexible stress sensor arrays, comprising multiple flexible stress sensor units, enable accurate quantification and analysis of spatial stress distribution. Nevertheless, the current implementation of flexible stress sensor arrays faces the challenge of excessive signal wires, resulting in reduced deformability, stability, reliability, and increased costs. The primary obstacle lies in the electric amplitude modulation nature of the sensor unit's signal (e.g., resistance and capacitance), allowing only one signal per wire. To overcome this challenge, the single-line multi-channel signal (SLMC) measurement has been developed, enabling simultaneous detection of multiple sensor signals through one or two signal wires, which effectively reduces the number of signal wires, thereby enhancing stability, deformability, and reliability. This review offers a general knowledge of SLMC measurement beginning with flexible stress sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on different arraying methods and their corresponding advantages and disadvantages. Finally, this review categorizes existing SLMC measurement methods into RLC series resonant sensing, transmission line sensing, ionic conductor sensing, triboelectric sensing, piezoresistive sensing, and distributed fiber optic sensing based on their mechanisms, describes the mechanisms and characteristics of each method and summarizes the research status of SLMC measurement.

Smart gating membrane based on polyzwitterion-modified SiO2 nanoparticles for water treatment and in-situ multi-modal detection

The smart gating membranes with stimuli-response permeation variations have attracted tremendous interest in the field of wastewater treatment. Specifically, successful integration of accurate porous architecture, precise water permeance regulation, long-term duration and in-situ detection of multiple environmental signals in a visualized way is highly desired yet still exclusive. Herein, a smart gating membrane with a combination of accurate porous adjustment and multi-signals detection is developed by assembling poly[2-(1-(4-vinylbenzyl) pyridin-1-ium-4-yl) ethane-1-sulfonate] (PVPES)-grafted SiO2 nanoparticles on cellulose membrane. The membrane exhibits uniform packing architecture with highly tunable water permeance from 0 to 15,330 L m−2 h−1 bar−1, mediated via polymeric shell thickness control and external salt-stimulation; besides, in-situ detection of heavy metal ions and pH values of the treated solution is simultaneously achieved via the pH-induced discoloration and heavy metal ion-dependent fluorescence of PVPES. What’s more, the membrane shows great mechanical stability against repeated bending, vibration or abrasion, as well as great anti-fouling capability, which together contributing to a long service life. We believe the membrane developed here will definitely promote the practical application of smart gating membranes.

Modulation and demodulation with quadrature square waves as carriers to improve spectrum utilization

Frequency division modulation enables fast and highly accurate detection of multiple signals, but its spectrum utilization is extremely low. Of course, existing methods improve the spectrum utilization by using quadrature signals as carriers, but it requires the carrier generation to be synchronized with the start of the acquisition. This is difficult to achieve when the carrier generation and the signal acquisition are not controlled by the same clock. Therefore, this paper proposes a demodulation method for the case where the carrier signal is a pair of quadrature square wave signals and the start of the carrier is not synchronized with the sampling. The method demodulates two signals modulating a pair of quadrature carriers by calculating the phase of one independent carrier signal. In this paper, three-wavelength multispectral images are acquired, and the demodulation results are compared with those acquired by the time-division method. The results show that there is a high similarity between the two, which proves the effectiveness of the method proposed in this paper, improves spectrum utilization, and is useful for the acquisition of multiplexed signals.

The dual-mode sensing of pressure and temperature based on multilayer structured flexible sensors for intelligent monitoring of human physiological information

Flexible multifunctional sensor with excellent performance via unsophisticated fabrication method is imperatively anticipated for the development of smart human health monitoring systems and wearable electronic devices. Here, we report a multilayer dual-mode flexible sensor (DMFS) by integrating different sensing layers on the two sides of a flexible dielectric substrate for sensing pressure and temperature. The introduction of elastic nanofiber skeleton structure and internal “sea urchin” like conductive materials is the top priority for the DMFS to have excellent sensing performance and durability. As a result, the fabricated DMFS ultimately shows excellent pressure sensitivity of 111.96 kPa−1 within a wide sensing range up to 70 kPa. In the case of temperature stimulus, the DMFS accomplishes a high, linear response to the ambient temperature variation within the range from 20 °C to 50 °C, while maintaining high stability and durability after long-term bending cycles. In addition, DMFS reveals its great potential for non-contact dynamic temperature measurement by being integrated into an intelligent temperature monitoring system. Most importantly, a novel smart human health-monitoring system, which attributes to the coordination and common work of multiple DMFS, has been developed and realizes simultaneous detection of multiple physiological signals of human beings in real-time.

Single-trigger dual-modal nanoplatform for in situ switch on cancer diagnosis imaging

Tumor markers in situ switch on nanoplatform is of great significance for disease early diagnosis and accurate treatment. It has significant advantages to realize simultaneous detection of multiple signals through single signal triggering. However, traditional nanoplatform often needs complex modification and integration of multiple materials. Herein, to achieve “one stone for two birds” and simplify nanoplatform structure, a dual-modal system was developed for glutathione (GSH)-triggered in situ switch on imaging of cancer. The transition metal oxide FeOOH shell was generated around CuInZnS quantum dots (CIZS QDs), and the near-infrared (NIR) fluorescence of QDs could be quenched. After being reduced to Fe2+ by GSH which normally overexpressed in cancer cells, CIZS QDs with fluorescence will generate. Therefore, the GSH-triggered fluorescence recovery could be used for in situ switch on cancer diagnostic imaging of cells and tumor-bearing mice. Furthermore, the reaction product Fe2+ enabled followed chemodynamic therapy (CDT) and magnetic resonance (MR) imaging properties to realize dual-modal diagnosis imaging. Compared with other FeOOH nanostructures, our method was more simple and efficient. Hence, this study provided a new attempt to construct versatile nanoplatform, which integrated cancer diagnosis imaging capability and may be applied in subsequent therapeutics.

Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications.

Synthetic biology aims at engineering new biological systems and functions that can be used to provide new technological solutions to worldwide challenges. Detection and processing of multiple signals are crucial for many synthetic biology applications. A variety of logic circuits operating in living cells have been implemented. One particular class of logic circuits uses site-specific recombinases mediating specific DNA inversion or excision. Recombinase logic offers many interesting features, including single-layer architectures, memory, low metabolic footprint, and portability in many species. Here, we present two automated design strategies for both Boolean and history-dependent recombinase-based logic circuits. One approach is based on the distribution of computation within multicellular consortia, and the other is a single-cell design. Both are complementary and adapted for non-expert users via a web design interface, called CALIN and RECOMBINATOR, for multicellular and single-cell design strategies, respectively. In this book chapter, we are guiding the reader step by step through recombinase logic circuit design, from selecting the design strategy fitting to their final system of interest to obtaining the final design using one of our design web interfaces.

Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy.

Liquid biopsy for determining the presence of cancer and the underlying tissue of origin is crucial to overcome the limitations of existing tissue biopsy and imaging-based techniques by capturing critical information from the dynamic tumor heterogeneity. A newly emerging liquid biopsy with extracellular vesicles (EVs) is gaining momentum, but its clinical relevance is in question due to the biological and technical challenges posed by existing technologies. The biological barriers of existing technologies include the inability to generate fundamental details of molecular structure, chemical composition as well as functional variations in EVs by gathering simultaneous information on multiple intra-EV molecules, unavailability of holistic qualitative analysis, in addition to the inability to identify tissue of origin. Technological barriers include reliance on EV isolation with a few labeled biomarkers, resulting in the inability to generate comprehensive information on the disease. A more favorable approach would be to generate holistic information on the disease without the use of labels. Such a marker-free diagnosis is impossible with the existing liquid biopsy due to the unavailability clinically validated cancer stem cells (CSC)-specific markers and dependence of existing technologies on EV isolation, undermining the clinical relevance of EV-based liquid biopsy. Here, CSC EVs were employed as an independent liquid biopsy modality. We hypothesize that tracking the signals of CSCs in peripheral blood with CSC EVs will provide a reliable solution for accurate cancer diagnosis, as CSC are the originators of tumor contributing to tumor heterogeneity. We report nanoengineered 3D sensors of extremely small nano-scaled probes self-functionalized for SERS, enabling integrative molecular and functional profiling of otherwise undetectable CSC EVs. A substantially enhanced SERS and ultralow limit of detection (10 EVs per 10 μL) were achieved. This was attributed to the efficient probe-EV interaction due to the 3D networks of nanoprobes, ensuring simultaneous detection of multiple EV signals. We experimentally demonstrate the crucial role of CSC EVs in cancer diagnosis. We then completed a pilot validation of this modality for cancer detection as well as for identification of the tissue of origin. An artificial neural network distinguished cancer from noncancer with 100% sensitivity and 100% specificity for three hard to detect cancers (breast, lung, and colorectal cancer). Binary classification to distinguish one tissue of origin against all other achieved 100% accuracy, while simultaneous identification of all three tissues of origin with multiclass classification achieved up to 79% accuracy. This noninvasive tool may complement existing cancer diagnostics, treatment monitoring as well as longitudinal disease monitoring by validation with a large cohort of clinical samples.

Fast FBG Interrogator on Chip Based on Silicon on Insulator Ring Resonator Add/Drop Filters

A fast FBG sensor interrogator on chip based on SOI ring resonator (RR) filters is presented. The measurement of the Bragg wavelength shift is based on the detection of multiple signals at the output ports of the RR filter, providing fast dynamic strain measurement capabilities, fundamentally limited by the photodetector bandwidth and signal processing. The ring resonator resonance wavelength is tuned and fixed by an integrated heater and the ring acts as a passband filter for the reflected FBG spectrum. After a theoretical analysis of the interrogation method, which allows the optimization of the RR characteristics depending on the specific application and requirements, we present experimental results on a single RR based interrogation scheme applying high frequency dynamic strain up to 40 kHz. We also theoretically evaluate the performance of multi-ring resonators. The proposed scheme provides low cost, high scalability to mass-volume market applications as well as an extremely fast interrogation method.

Glioblastoma Associated Natural Killer Cell EVs Generating Tumour-Specific Signatures: Noninvasive GBM Liquid Biopsy with Self-Functionalized Quantum Probes.

Diagnosis of glioblastoma (GBM) poses a recurring struggle due to many factors, including the presence of the blood-brain barrier (BBB) in addition to the significant tumor heterogeneity. Natural killer (NK) cells of the innate immune system are the primary immune surveillance mechanism for GBM and identify GBM tumors without any previous sensitization. The metabolic reprogramming of NK cells during GBM association is expected to be reflected in its extracellular vesicles. Therefore, tracking the activity of NK cell vesicles in circulation (circulating immune vesicles, CIVs) has great potential for accurate GBM diagnosis. However, identification GBM associated CIVs in circulation is immensely challenging as there is no availability of clinically validated GBM-specific circulating biomarkers. Here, we present GBM associated CIV profiling for noninvasive GBM diagnosis. We investigated the feasibility of using the signals derived from GBM associated CIVs as a de novo methodology for GBM diagnosis. An ultrasensitive sensor and a marker-free approach were essential for the detection of rare signals of GBM associated CIVs. For this purpose, we designed GBM ImmunoProfiler platform using scalable ultrafast laser multiphoton ionization mechanism and adopted surface enhanced Raman spectroscopy (SERS) ensuring simultaneous detection of multiple CIV signals to identify GBM. We experimentally demonstrated that GBM associated CIVs carry unique, tumor-specific signals. The features of GBM associated CIVs were explored through machine learning identifying its similarity with GBM patient blood (without cell isolation) using a very small amount of peripheral blood (5 μL) with 96.82% sensitivity and 100% specificity. In addition, we demonstrated that a tumor associated CIV profile can classify between multiple brain cancer types (astrocytoma, oligodendroglioma, and glioblastoma). We also experimentally demonstrated significant variation in the immune checkpoint protein expression (PDL-1 and CTLA-4) between GBM associated CIVs and uninteracted CIVs. Preclinical analysis with serum specimens of GBM patients showed the possibility of using our technology for minimally invasive GBM diagnosis. With clinical validation, our technology has potential to improve GBM diagnostics with a useful, minimally invasive GBM liquid biopsy.

High-Sensitivity Electric Potential Sensors for Non-Contact Monitoring of Physiological Signals

The paper describes highly-sensitive passive electric potential sensors (EPS) for non-contact detection of multiple biophysical signals, including electrocardiogram (ECG), respiration cycle (RC), and electroencephalogram (EEG). The proposed EPS uses an optimized transimpedance amplifier (TIA), a single guarded sensing electrode, and an adaptive cancellation loop (ACL) to maximize sensitivity (DC transimpedance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 150\,\,\text{G}\Omega $ </tex-math></inline-formula> ) in the presence of power line interference (PLI) and motion artifacts. Tests were performed on healthy adult volunteers in noisy and unshielded indoor environments. Useful sensing ranges for ECG, RC, and EEG measurements, as validated against reference contact sensors, were observed to be approximately 50 cm, 100 cm, and 5 cm, respectively. ECG and RC signals were also successfully measured through wooden tables for subjects in sleep-like postures. The EPS were integrated with a wireless microcontroller to realize wireless sensor nodes capable of streaming acquired data to a remote base station in real-time.

Multiple Signal Source Terrain Topography Determination using Labview VI Algorithm

To apply the knowledge in hardware and software integration, as well as utilizing the NI LabVIEW and NI myDAQ signal acquisition attribute. The aim of this research paper is to provide a solution for determining the type of land topograhy through the detection of incoming multiple groundwave signals from different oscillation sources. Aside from source determination and notification, the user will also be able to visualize, analyze, and classify the type of the incoming signal which depends upon its response as it passes through an indefinite terrain. The system will be using Low Frequency (LF) ground-wave oscillations. This paper deals with the concern using the finite-difference time-domain (FDTD) of topographic terrain identification through soil conductivity and creating a virtual instrument (vi) that will execute a determination algorithm. The LabVIEW program that will be implemented is multiplatform, it will be able to support different versions of LabVIEW depending on the program availability and to support real-time targets

Low-Cost and Highly Sensitive Wearable Sensor Based on Napkin for Health Monitoring.

The development of sensors with high sensitivity, good flexibility, low cost, and capability of detecting multiple inputs is of great significance for wearable electronics. Herein, we report a napkin-based wearable capacitive sensor fabricated by a novel, low-cost, and facile strategy. The capacitive sensor is composed of two pieces of electrode plates manufactured by spontaneous assembly of silver nanowires (NWs) on a polydimethylsiloxane (PDMS)-patterned napkin. The sensor possesses high sensitivity (>7.492 kPa−1), low cost, and capability for simultaneous detection of multiple signals. We demonstrate that the capacitive sensor can be applied to identify a variety of human physiological signals, including finger motions, eye blinking, and minute wrist pulse. More interestingly, the capacitive sensor comfortably attached to the temple can simultaneously monitor eye blinking and blood pulse. The demonstrated sensor shows great prospects in the applications of human–machine interface, prosthetics, home-based healthcare, and flexible touch panels.

Classification and unsupervised clustering of LIGO data with Deep Transfer Learning

The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals. The sophisticated design of these detectors mitigates the effect of most types of noise. However, advanced LIGO data streams are contaminated by numerous artifacts known as glitches: non-Gaussian noise transients with complex morphologies. Given their high rate of occurrence, glitches can lead to false coincident detections, obscure and even mimic gravitational wave signals. Therefore, successfully characterizing and removing glitches from advanced LIGO data is of utmost importance. Here, we present the first application of Deep Transfer Learning for glitch classification, showing that knowledge from deep learning algorithms trained for real-world object recognition can be transferred for classifying glitches in time-series based on their spectrogram images. Using the Gravity Spy dataset, containing hand-labeled, multi-duration spectrograms obtained from real LIGO data, we demonstrate that this method enables optimal use of very deep convolutional neural networks for classification given small training datasets, significantly reduces the time for training the networks, and achieves state-of-the-art accuracy above 98.8%, with perfect precision-recall on 8 out of 22 classes. Furthermore, new types of glitches can be classified accurately given few labeled examples with this technique. Once trained via transfer learning, we show that the convolutional neural networks can be truncated and used as excellent feature extractors for unsupervised clustering methods to identify new classes based on their morphology, without any labeled examples. Therefore, this provides a new framework for dynamic glitch classification for gravitational wave detectors, which are expected to encounter new types of noise as they undergo gradual improvements to attain design sensitivity.

Complementary Electromagnetic‐Triboelectric Active Sensor for Detecting Multiple Mechanical Triggering

AbstractWith the fast development of integrated circuit technology and internet of things, sensors with multifunctional characteristics are desperately needed. This work presents an integrated electromagnetic‐triboelectric active sensor (ETAS) for simultaneous detection of multiple mechanical triggering signals. The good combination of a contact‐separation mode triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) realizes the complement of their individual advantages. The theoretical calculation and analysis of EMG and TENG are performed to understand the relationship between their output and the external mechanical signals. The experimental results show that the output voltage of TENG part is suitable to detect the magnitude of the external triggering force with a sensitivity of about 2.01 V N−1. Meanwhile, the output current of EMG part is more appropriate to reflect the triggering velocity and the sensitivity is about 4.3 mA (m s−1)−1. Moreover, both the TENG part and the EMG part exhibit good stabilities after more than 20 000 cycles of force loading and unloading. One ETAS that can record the typing behavior of the finger precisely is demonstrated. In addition, the TENG part can harvest the mechanical energy during typing for possible powering of tiny electronics. This ETAS has promising applications in complex human–machine interface, personal identification, and security system.

Detection and Attribution of Changes in Extreme Temperatures at Regional Scale

This paper improves an extreme-value-theory-based detection and attribution method and then applies it to four types of extreme temperatures, annual minimum daily minimum (TNn) and maximum (TXn) and annual maximum daily minimum (TNx) and maximum (TXx), using the HadEX2 observation and the CMIP5 multimodel simulation datasets of the period 1951–2010 at 17 subcontinent regions. The methodology is an analog of the fingerprinting method adapted to extremes using the generalized extreme value (GEV) distribution. The signals are estimated as the time-dependent location parameters of GEV distributions fitted to extremes simulated by multimodel ensembles under anthropogenic (ANT), natural (NAT), or combined anthropogenic and natural (ALL) external forcings. The observed extremes are modeled by GEV distributions whose location parameters incorporate the signals as covariates. A coordinate descent algorithm improves both computational efficiency and accuracy in comparison to the existing method, facilitating detection of multiple signals simultaneously. An overall goodness-of-fit test was performed at the regional level. The ANT signal was separated from the NAT signal in four to six regions. In these analyses, the waiting times of the 1951–55 20-yr return level in the 2006–10 climate for the temperature of the coldest night and day were found to have increased to over 20 yr; the corresponding waiting times for the warmest night and day were found to have dropped below 20 yr in a majority of the regions.

Internal Asymmetric Tandem Supercapacitor for High Working Voltage along with Superior Rate Performance

An asymmetric tandem supercapacitor (ATSC) is fabricated for the first time by using a unique hybrid nanocomposite for a wider working voltage. The as-fabricated flexible ATSC displays a much higher working voltage of 4.5 V and hence efficient energy density as well as power density, which are 61% and 33% higher than those of the single asymmetric supercapacitor, respectively. The ATSC displays high capacitance stability over large working voltage of 4.5 V with 97% capacitance retention after 5000 charging–discharging cycles. In addition, a stable capacitive response is maintained during the flexible bending, and a constant capacitive response is maintained even at a much higher scan rate of 5000 mV/s. Moreover, the ATSC could be easily coupled with a photodetector to provide stable power during the detection of multiple illumination signals. Therefore, results suggest that ATSCs are a promising energy storage device with efficient power and energy density and a much wider working voltage of 4.5 V.

Improvement in measurements of hydroxyapatite layers by hybrid high dynamic range image processing in white-light interferometry

Hydroxyapatite (HA) is one of the most common natural biomaterials widely used in medical applications. Full-field optical coherence tomography can be used to characterise the internal structures of this material by detection of multiple fringe envelope signals. However, the semi-translucent nature of HA and complex spongy aspect is a challenge. We propose here the “IMPROVE-Protocol”, combining a hybrid high dynamic range technique, image averaging and dark and flat corrections to study HA. The resulting increase in the SNR and improvement in the power of detection of the system allows normally unmeasured features to be observed, down to sub-µm in size.