Inaccurate Signal Research Articles

Empowering aged persons care services: Leveraging data-driven BiLSTM-GRU integration with wearable devices

Wearable medical devices (WMDs) have emerged as a promising medical technology to enhance patient care from a variety of medical angles. However, due to inaccurate signal measurement in sensors, the conventional model-driven framework has problems with its limited reconstruction ratio and compression ratio. A growing need for effective and individualized aged care services is created by the world's population's increasing aging. To increase the system quality and overall performance, the collected data is preprocessed to normalized data by using Z score Normalization. This study proposes a novel approach that integrates wearable device data with Elderly people's health and wellbeing may be predicted and monitored using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) models. This data-driven strategy enables caregivers to deliver tailored care plans, timely treatments and early identification of potential health problems, which improve the aged population's overall quality of life. The BiLSTM-GRU evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (96%), Precision (93%), F1-score (95%), Sensitivity (94%), Specificity (90%) and MSE. By advancing data-driven techniques in aged care services, this research intends to pave the way for a more effective and long-lasting healthcare system that is designed to meet the needs of the senior population.

High Purity Solvents for qNMR Measurement

Quantitative NMR (qNMR) has been adopted by documentary standards, including the Japanese Pharmacopoeia (JP), United States Pharmacopoeia (USP), and International Organization for Standardization (ISO), owing to its reliability and efficiency. Note that qNMR can be used for quantifying target components using the signal integration ratio of an analyte to a reference. In qNMR, a modern NMR instrument with high resolution and sensitivity is used to record reliable spectra. This instrument can detect small signals from impurities in a solvent, which may result in inaccurate signal integration in the spectrum. In this study, we investigated the influence of solvent quality on qNMR accuracy focusing on organic impurities, water content, and deuteration ratio. If signals from organic impurities and signals from the analyte overlap, the duplication of signal integration will directly affect the qNMR analytical result. To examine overlapping, we performed blank solvent tests. Additionally, a high water content and low deuteration ratio affect the detection sensitivity, thus reducing the signal-to-noise (S/N) ratio of the target. Thus, these factors must be considered to obtain accurate qNMR results.

Research on WSN reliable ranging and positioning algorithm for forest environment

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

Multistatic radar-based imaging in layered and dispersive media for biomedical applications

This paper presents a novel algorithm for near-field radar imaging in layered and dispersive media with application to biomedical imaging. The proposed algorithm compensates for variations in the dielectric properties and integrates each component’s frequency response to form an image. The algorithm uses both the amplitude and phase of the complex electromagnetic field. The proposed algorithm resolves dispersion issues using frequency-dependent velocity. The algorithm uses phase shift instead of time delay to overcome the disadvantage of time-shift radar-based methods, such as inaccurate signal transformation and wave flight time. Therefore, the proposed algorithm allows detecting small changes in dispersive media such as the human brain. The algorithm is validated using experimental data for different volumes of brain tissue affected by the accumulation of plaques and tangles due to Alzheimer’s disease. The accuracy and robustness of the proposed algorithm are evaluated using estimation factor and localisation error.A maximum estimation factor of 1.1 cm and localisation error of 5.3 mm in the actual and detected volume ensure that the proposed algorithm detects small changes in the brain more accurately. The paper also discusses comparisons with radar-based algorithms, demonstrating that the proposed algorithm is less compute-intensive and yields more accurate results.

In Vivo Chronic Brain Cortex Signal Recording Based on a Soft Conductive Hydrogel Biointerface

In neuroscience, the acquisition of neural signals from the brain cortex is crucial to analyze brain processes, detect neurological disorders, and offer therapeutic brain-computer interfaces. The design of neural interfaces conformable to the brain tissue is one of today's major challenges since the insufficient biocompatibility of those systems provokes a fibrotic encapsulation response, leading to an inaccurate signal recording and tissue damage precluding long-term/permanent implants. The design and production of a novel soft neural biointerface made of polyacrylamide hydrogels loaded with plasmonic silver nanocubes are reported herein. Hydrogels are surrounded by a silicon-based template as a supporting element for guaranteeing an intimate neural-hydrogel contact while making possible stable recordings from specific sites in the brain cortex. The nanostructured hydrogels show superior electroconductivity while mimicking the mechanical characteristics of the brain tissue. Furthermore, in vitro biological tests performed by culturing neural progenitor cells demonstrate the biocompatibility of hydrogels along with neuronal differentiation. In vivo chronic neuroinflammation tests on a mouse model show no adverse immune response toward the nanostructured hydrogel-based neural interface. Additionally, electrocorticography acquisitions indicate that the proposed platform permits long-term efficient recordings of neural signals, revealing the suitability of the system as a chronic neural biointerface.

Observer-Based Adaptive Fuzzy Event-Triggered Control for Mechatronic Systems With Inaccurate Signal Transmission and Motion Constraints

This article presents an adaptive output feedback controller for a class of Euler–Lagrange (EL) mechatronic systems subject to time-delay, input hysteresis, and motion constraints; simultaneously, the event-triggered mechanism is introduced to decrease communication costs. During controller design, the proposed method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not only</i> relaxes additional requirements on the model structures by other methods (e.g., parameters linearly appearing in dynamics or strict triangular/cascade normal forms), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">but also</i> estimates uncertain dynamics and approximation errors online, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instead of</i> utilizing “large-gain” or discontinuous terms to suppress their impacts. Regarding the inaccurate signal transmission of EL systems, this article presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">modified</i> fuzzy observer with an elaborately designed auxiliary term, to recover unmeasurable variables and simultaneously deal with state time-delay. Meanwhile, the Prandtl–Ishlinskii model is employed to imitate input hysteresis, whose unknown parameters are also estimated online to improve tracking accuracy. By Lyapunov-based stability analysis, it is proven that the error signals are always limited within preset constraints and asymptotically converge to zero. As far as we know, for EL systems, this article proposes the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> controller to address such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">comprehensive</i> effects of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable</i> state feedback, limited workspace, and input/output nonlinearities in practice, and also eliminate tracking errors with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">theoretical guarantee</i> . More importantly, the utilized event-triggered mechanism further improves the practicability of this article. Finally, based on a pneumatic artificial muscle (PAM)-actuated robot manipulator, the performance of the proposed controller is validated via hardware experiments.

Water Desalination Plant Fault Detection using Artificial Neural Network

&lt;p&gt;Water famine is very cruel. The desalination plant was brought in to alleviate the water shortage. A desalination plant has been set up in many places around the world. In the view of expanding worldwide need of fresh water, the usage of desalination plant becomes more common. There are more than twenty-five thousand desalination plants around the world. Many industries require treated water for production, water treatment, and other functions. Water quality is occasionally insufficient or does not satisfy the quality criteria for manufacturing causes. So, the enterprises utilize desalination systems to purify water for their own usage. It makes the water safe to drink as well as suitable for a variety of industrial applications. The fault occurring in the desalination plant, it slows down the processing the speed and reduce the output rate. In this study, focuses on the faults like not under system control, electrical fault, pump fault, control valve fault, inaccurate signal, old data fault, derived fault and transmitter fault. The proposed artificial neural network with the single and double component fault (ANN S-DCF) is introduced to detect the faults occurring in the desalination plant. The faults are splitted into two catagories and characteristics of each fault are trained in the artificial neural network. The result of this work achieves the best accuracy comparing to the existing techniques of SVR (support vector regression), PCA (principal component analysis) and DPLS (dynamic partial least square) method. This study achieves the accuracy rate of 96%, precision rate of 93% and sensitivity rate of 95% respectively with low complexity and high operational speed.&lt;/p&gt;&#x0D;

Humans trade off whole-body energy cost to avoid overburdening muscles while walking.

Metabolic cost minimization is thought to underscore the neural control of locomotion. Yet, avoiding high muscle activation, a cause of fatigue, often outperforms energy minimization in computational predictions of human gait. Discerning the relative importance of these criteria in human walking has proved elusive, in part, because they have not been empirically decoupled. Here, we explicitly decouple whole-body metabolic cost and 'fatigue-like' muscle activation costs (estimated from electromyography) by pitting them against one another using two distinct gait tasks. When experiencing these competing costs, participants (n = 10) chose the task that avoided overburdening muscles (fatigue avoidance) at the expense of higher metabolic power (p < 0.05). Muscle volume-normalized activation more closely models energy use and was also minimized by the participants' decision (p < 0.05), demonstrating that muscle activation was, at best, an inaccurate signal for metabolic energy. Energy minimization was only observed when there was no adverse effect on muscle activation costs. By decoupling whole-body metabolic and muscle activation costs, we provide among the first empirical evidence of humans embracing non-energetic optimality in favour of a clearly defined neuromuscular objective. This finding indicates that local muscle fatigue and effort may well be key factors dictating human walking behaviour and its evolution.

Cryopolymerized Polyampholyte Gel with Antidehydration, Self-Healing, and Shape-Memory Properties for Sustainable and Tunable Sensing Electronics.

Hydrogel-based wearable flexible electronics are attracting tremendous interest for use in human healthcare. However, many of the existing hydrogel electronics are often susceptible to dehydration, leading to weakened stretchability and inaccurate signal extraction. Besides, hydrogels are desired to be much smarter for self-repairing physical damage and enabling performance manipulation. Herein, we develop a kind of cryopolymerized polyampholyte gels with the multifunctionality of antidehydration, self-healing, and shape-memory for wearable sensing electronics. The antidehydration property is enabled by the incorporation of glycerol, endowing the sensing electronics with excellent stretchability and strain-sensing performance in long-term monitoring. The ionic bonds in the polyampholyte gel possess a dynamic feature regulated by alternant NaCl(aq) and H2O treatments, laying the foundation for self-healing and shape-memory. As a result, the sensing electronics can automatically repair physical damages without any sacrifice in sensing performance, after healing both conductivity and strain-sensing performance could return to the initial levels. The shape-memory function enables the temporal adjustment of the initial state of the sensing electronics; both the conductivity and sensing performance, for instance, signal intensity, can be manually manipulated. In all, the cryopolymerized polyampholyte gels with antidehydration, self-healing, and shape-memory properties can be an inspiration to develop sustainable and tunable gel-based electronics for human motion monitoring.

Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal.

Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal.

Two-Dimensional calcium silicate nanosheets for trapping atmospheric water molecules in humidity-immune gas sensors

In humid conditions, water vapor can easily neutralize the surface active sites of metal oxide sensors, leading to a lowering in the sensitivity of the gas sensor and a resultant inaccurate signal in practical applications. Herein, we present a new hybrid sensor by introducing a two-dimensional calcium silicate (CS) nanosheet as a water-trapping layer in SnO2 nanowires. Unlike the heavily wrinkled and aggregated morphology of conventional CS nanosheets, our nanosheet in the hybrid material is ultrathin and flat. Moreover, it was grown in the empty spaces between the spider-web-like networks of SnO2 nanowires without covering the nanowire surface. These two morphological features improve moisture trapping with minimal reduction in the active sensing area. Consequently, stable and sensitive gas detection under humid conditions was achieved in this hybrid sensor. The superior humidity-independent sensing is ascribed to the preferential adsorption of water molecules on hydroscopic CS nanosheets through the hydrogen bond. Based on density functional theory calculations, we determined that the improved gas response is driven by the additional formation of oxygen vacancy in SnO2 due to the diffusion of aliovalent Ca ions from the CS nanosheet.

Self-Immolative Dye-Doped Polymeric Probe for Precisely Imaging Hydroxyl Radicals by Avoiding Leakage.

For sensing low abundance of biomarkers, utilizing nanocarriers to load dyes is an efficient method to amplify the detected signal. However, the non-specific leak of the internal dyes in this approach is accompanied by false positive signals, resulting in inaccurate signal acquirement. To address this issue, in this work, we reported a novel signal amplification strategy with dye as a scaffold to construct a self-immolative dye-doped polymeric probe (SDPP). In our proposed approach, the dyes were covalently integrated into the main chain of a polymer, which can avoid the non-specific leak of the dye when used in a rigorous biological environment, thus evading the false positive signal. As a prototype of this concept, a SDPP, which responds to hydroxyl radicals (•OH), was rationally fabricated. Upon being activated by •OH, SDPP will liberate the dye through a self-immolative reaction to bind with protein for amplifying the fluorescence signal. Compared with a dye-loaded nanoprobe, SDPP can precisely track intracellular basal •OH levels and visualize the •OH associated with myocarditis in vivo. More importantly, the attempt in this work not only provides an effective molecular tool to investigate the role of •OH in cardiopathy, but also puts forward a new direction to current signal-amplifying strategies for precisely and reliably acquiring the intracellular molecular information.

Compressive sensing for perceptually correct reconstruction of music and speech signals

Compressive sensing (CS) is a technique that can achieve exact signal reconstruction by using fewer samples than those in the Nyquist theorem. In this study, listening tests were conducted to investigate the minimum number of samples needed for perceptually correct reconstruction by means of CS. The dictionary used as a sparsity domain for the signal reconstruction was the discrete cosine transform, and the reconstruction approach was the one provided by the L1-Magic. Three music signals and four speech signals were used as source signals. These signals were reconstructed by CS using different percentages of Nyquist samples. The results of the listening tests showed that, when 50% of the samples were used for the CS reconstruction, half of the test listeners judged the original and reconstructed signals to be perceptually the same. Listeners with musical training showed better sensitivity in distinguishing the original signals from the reconstructed signals than listeners without musical training. The log spectral distance between the original and reconstructed signals was a better objective index than the root mean square error and signal-to-noise ratio to evaluate the performance of the CS because inaccurate signal reconstruction mainly appeared at high frequencies.

Measurement and analysis of the scattering properties of cement surfaces of urban environment in the millimeter waveband

AbstractThe use of the low‐frequency scattering coefficient leads to inaccurate receiving‐power predictions in the millimeter‐wave band. The inaccurate signal will hinder optimization of the base station location. In the millimeter waveband, a building surface becomes rougher in an urban environment. The scattering becomes increasingly significant and a non‐negligible factor. In this article, the scattering characteristics of rough cement wall surfaces are studied to improve the scattering coefficients for millimeter waveband communications. The ray tracing method is adopted, and the lambertian and directive models are incorporated into this method, respectively. Moreover, the signal distributions of rough cement wall surfaces are acquired through an outdoor experiment at different incidence angles. The results reveal that the scatterings that are embedded into ray tracing are better than those which were not considered in the ray tracing, compared with the experiment. The directive model agrees more with the measurements than the lambertian model. The scattering coefficient S and exponent αR of the directive model are 0.19 and 1 in the millimeter waveband, respectively, which disagree with the results at low frequencies. The scattering properties can be applied to simulate signal propagation in urban environments to improve the reliability of mobile telecommunication systems.

A Cooperative Two-Step Vertical Handoff Scheme with Mobility Prediction

As mobile communication evolves into 3G beyond, the interworking of multiple heterogeneous networks serves as the major effort for taking the best advantage of different technologies available in supporting various emerging services, such as VoIP, Video on Demand (VoD), and IP Television (IPTV), etc. Vertical handoff is one of the key mechanisms in achieving Always Best Connected (ABC) for the mobile users by leveraging the benefits of deploying different types of networks for provisioning seamless handoff/roaming services in presence of user mobility. This paper aims to introduce a novel cooperative two-step vertical handoff scheme for the integration of 3G Wireless Wide-Area Networks (WWAN) and the IEEE 802.11 Wireless Local-Area Networks (WLANs). The proposed scheme is based on the cooperation based access point (AP) and mobile station (MS), where the AP manipulates the sensed signal strength to determine whether a pre-handoff action should be initiated. To improve the accuracy of user mobility prediction, a Markov model that incorporates with a novel parameter training process is developed at the AP for acquiring the hotspot geographic arrangement, such as the location of aisles, walls, and entrances/exits, etc., which is considered as the major factor of determining the user mobility patterns in an indoor hotspot. We will justify feasibility and discuss the operation complexity of the proposed cooperative vertical handoff. Moreover, error propagation due to inaccurate signal strength measurement is studied through Maximum Likelihood estimation. Finally, we will clearly demonstrate the merits gained by using the proposed two-step vertical handoff mechanism through extensive simulation, where the derived analytical models are verified.

Removal algorithm of horizontal noise for echoes in mine geological radar

In view of the problems of inaccurate echo signal caused by noise interference in mine geological radar echo detection, a method for eliminating horizontal noise of radar echo signal based on wavelet threshold is proposed. In this method, binomial function is selected as the Morlet wavelet basis function. According to the wavelet threshold algorithm, the optimal decomposition level is adaptively selected and the wavelet threshold is determined. The sqtwolog criterion is selected to remove the horizontal noise in the mine geological radar echo. The hard threshold method is used to detect the echo signal. Finally, the simulation experiment is carried out. The results show that the method of determining the optimal decomposition level based on wavelet decorrelation whitening test is feasible and effective. The optimal decomposition level of mine geological radar echo signal is between 4 and 5, and the effect of denoising is the best.

The Bed Nucleus of the Stria Terminalis, Homeostatic Satiety, and Compulsions: What Can We Learn From Polydipsia?

A compulsive phenotype characterizes several neuropsychiatric illnesses – including but not limited to – schizophrenia and obsessive compulsive disorder. Because of its perceived etiological heterogeneity, it is challenging to disentangle the specific neurophysiology that precipitates compulsive behaving. Using polydipsia (or non-regulatory water drinking), we describe candidate neural substrates of compulsivity. We further postulate that aberrant neuroplasticity within cortically projecting structures [i.e., the bed nucleus of the stria terminalis (BNST)] and circuits that encode homeostatic emotions (thirst, hunger, satiety, etc.) underlie compulsive drinking. By transducing an inaccurate signal that fails to represent true homeostatic state, cortical structures cannot select appropriate and adaptive actions. Additionally, augmented dopamine (DA) reactivity in striatal projections to and from the frontal cortex contribute to aberrant homeostatic signal propagation that ultimately biases cortex-dependent behavioral selection. Responding becomes rigid and corresponds with both erroneous, inflexible encoding in both bottom-up structures and in top-down pathways. How aberrant neuroplasticity in circuits that encode homeostatic emotion result in the genesis and maintenance of compulsive behaviors needs further investigation.

Comparative study of using different ionosphere models in Thailand for single-frequency GNSS users

An ionosphere layer makes inaccurate global navigation satellite systems (GNSS) signal. The ionosphere layer contains free electrons delaying the speed of GNSS signals. A suitable selection of ionosphere model is very useful to eliminate this inaccurate GNSS signal. This article presents a comparative study by applying various ionosphere models in Thailand including: Klobuchar model, Global Ionosphere Maps, Ionosphere Model from Quasi-Zenith Satellite System and local ionosphere model. The local ionosphere model is generated from GNSS observations at with the use of locally available GNSS tracking stations by the Bernese GPS software 5.0. Coordinates derived from the above-mentioned models were compared and statistical tests in two observed scenarios; namely, single-frequency data and ionosphere-free linear combination from dual-frequency data. These experiment results proved that applying the local ionosphere model to the estimation produces the most accurate positioning results. They are similar to the results obtained from an ionosphere-free linear combination, but with significant differences.

Effects of Porous Structure for Gas Sensor on Sensitivity Using Tin Oxide with Low Density

Recently, gas sensor has been studied because of increasing accidents caused by harmful and explosive gas, which is hard to detect olfactory system. Tin oxide is one of the most promising candidates for gas sensing materials owing to its many advantages, such as various gas detecting capability, good stability and high sensitivity. In gas sensor, the kinds of sensing materials as well as detecting layer structures are important. The sensing materials with high surface areas show high sensing performance, so nanowire structure with mechanical contact between nanowires has been widely used. However, electric signal of nanowire along mechanical contacts is not stable and the external vibration and fast gas flow generates the inaccurate signal. In this research, the low density tin oxide film with stable interconnection between nanoparticles was prepared using the physical vapor deposition by controlling the processing pressure, temperature and power. Macrostructure and interconnected structure between tin oxide nanoparticles were observed using scanning electron microscope (SEM) and transmission electron microscope (TEM) and crystal structure was analyzed by X-ray diffraction (XRD). Platinum and titanium electrodes were fabricated by lift-off process on the silicon substrate with the silicon nitride film of 300 nm, and then tin oxide with low density was deposited on it using shadow mask. The characteristics of the gas sensor were examined using carbon mono-oxide (CO) and ethanol (C2H5OH) changing concentration of gas, operating temperature and post heat-treatment temperature. In conclusion, porous nanostructure of tin oxide was composed of a lot of interconnected nanoparticles of about 10 nm and showed stable resistance in dry air. And post heat-treatment enhanced the stability in spite of inreasing the particle size. It could be suggested that the oxide films with lower density showed the superior sensitivity and the shorter response time than the oxide films with higher density. Figure 1

Triad resonance between a surface-gravity wave and two high frequency hydro-acoustic waves

A new class of resonant triad in the family of gravity–acoustic waves has been found. I show that a hydro-acoustic wave interacting with a surface-gravity wave may generate a second hydro-acoustic wave. Interestingly, the two acoustic waves propagate in the same direction with similar wavelengths, that are almost double of that of the gravity wave. The evolution of the wave triad amplitudes is periodic and it is derived analytically, in terms of Jacobian elliptic functions and elliptic integrals. The physical importance of this type of triad interactions is the modulation of pertinent hydro-acoustic signals, leading to inaccurate signal perceptions.