Non-ideal Environments Research Articles

Portable, open-source solutions for estimating wrist position during reaching in people with stroke

Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method’s ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods’ estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.

Wireless power transfer in attenuating media

Dissipative media (underground/underwater, biological materials and tissues, etc.) pose a challenge to inductive wireless power transfer systems as they generally attenuate the near fields that enable mutual coupling. Apart from this, the impact of the environment on electromagnetic fields can also be seen in the self-impedance of coils, resulting in significant eddy current losses and detuning effects. In this article, we study, theoretically, the mechanism of wireless power transfer via a pair of magnetic resonators inside an infinite homogeneous medium with a comprehensive circuit model that takes into account all the electromagnetic effects of the background medium. This analytical approach can offer deep insights into the design and operation of wireless charging systems in non-ideal environments.

Electrochemical Sensors to Detect Bacterial Foodborne Pathogens.

Bacterial foodborne pathogens cause millions of illnesses each year and disproportionately impact those in developing countries. To combat these diseases and their spread, effective monitoring of foodborne pathogens is needed. Technologies to detect these microbes must be deployable at the point-of-contamination, often in nonideal environments. Electrochemical sensors are uniquely suited for field-deployable monitoring, as they are quantitative, rapid, and do not require expensive instrumentation. When combined with the inherent recognition capabilities of biomolecules, electrochemistry is unmatched for quantitative biological measurements with minimal equipment requirements. This Review is centered on recent advances in electrochemical sensors for the detection of bacterial foodborne pathogens with a specific emphasis on field-deployable platforms, as this is a key requirement of any technology that could effectively halt the spread of foodborne diseases. Innovative electrochemical sensing strategies are highlighted that demonstrate the ability of these technologies to achieve high sensitivity and large detection ranges with rapid readout. Sensing strategies are categorized on the basis of whether they incorporate biological pretreatments or biorecognition elements, and their key advantages and disadvantages are summarized. As this class of sensors continues to mature, methods to incorporate device specificity and to detect targets from complex solutions will enable the translation of these platforms from laboratory prototypes to real-world implementation.

Error analysis method of intelligent electricity meter based on improved BP neural network

Many smart electricity meters work in different nonideal environments. The complexity of the working environment often leads to the difference between the measurement error of electricity meters and the ideal conditions. In this paper, BP neural network is improved to make it suitable for error analysis of intelligent electricity meter. By identifying, processing, analyzing and integrating a large number of experimental data, the error model of smart electricity meter is established. Then the internal relationship between various stress factors and the error of electricity meter is revealed from the data level. This method can accurately predict the error of electricity meters under certain conditions and lay a foundation for error correction of smart electricity meters in the future. In this paper, the realization process of the method is briefly described through simulation experiments, and the feasibility, rationality and reliability of the method are verified.

Abstract P202: Estimation of Arm Kinematics in Stroke Survivors With Wearable Sensors

Introduction: Stroke is the leading cause of long-term disability in the US, often resulting in upper extremity (UE) motor impairment. Numerous clinical metrics have been developed and used to evaluate UE motor function to assess rehabilitation outcomes, but most of these are innately subjective and/or have relatively low sensitivity to change due to the use of ordinal scales. As a result, there is a growing interest in the use of kinematics to evaluate UE motor function, as they provide completely objective, high resolution quantitative measurements. However, obtaining kinematic measurements in stroke survivors can be a challenging task, as non-ideal environments (e.g., hospital rooms), expense, and a limited ability to perform calibration poses can make traditional optical tracking systems impractical. To overcome these challenges, we developed and compared two methods using different wearable sensors to estimate elbow angle and wrist position during reaching movements in people with stroke. Methods: We developed frameworks specific for two different types of sensors, inertial measurement units (IMU) and virtual reality (Vive) trackers, to estimate elbow angle and wrist position. We assessed each sensor’s estimation accuracy during pure flexion-extension motion, performed by a manual goniometer, and pure pronation-supination motion, performed by a healthy participant. We also compared each sensor’s ability to longitudinally track a stroke survivor’s UE function by using the wrist position estimates to compute sweep areas during a sweeping task. Results: For the IMUs, our results demonstrated accuracy to within 4.8° and 1.2 cm for elbow angle and wrist position, respectively. For the Vive, our results demonstrated accuracy to within 2.2° and 0.9 cm. The change in sweep area estimated by each method agreed with each other, as the difference between estimates was approximately 4% of the change in sweep area. Conclusion: These methods can be used to develop and accurately assess kinematic metrics for use in evaluating stroke rehabilitation outcomes.

Minimizing transient microenvironment-associated variability for analysis of environmental anthropogenic contaminants via ambient ionization

The rapid and quantitative analysis of anthropogenic contaminants in environmental matrices is crucial for regulatory testing and to elucidate the environmental fate of these pollutants. Direct ambient mass spectrometry (AMS) methodologies greatly increase sample throughput, can be adapted for onsite analysis and are often regarded as semi-quantitative by most developed protocols. One of the limitations of AMS, especially for on site analysis applications, is the irreproducibility of the measurements related to the occurrence of transient microenvironments (TME) and variable background interferences. In this work we report an effective strategy to minimize these effects by hyphenating, for the first time, solid phase microextraction (SPME) arrow to mass spectrometry via a thermal desorption unit (TDU) and direct analysis in real time (DART) source. The developed method was optimized for the extraction and analysis of pesticides and pharmaceuticals from surface water. It was demonstrated that the hyphenation of the SPME and TDU-DART resulted in reduced background contamination, indicating the suitability of the method for onsite analysis even in variable and non-ideal environments. Model analytes were quantitated in the low μg/L range with a total analysis time of less than 5 min, linear dynamic ranges (LDR) and interday reproducibility for most compounds being 2.5–500 μg/L and lower than 10%, respectively. The developed approach provides an excellent analytical tool that can be applied for the onsite high-throughput analysis of water samples as well as air and aereosols. Considering the tunability of our extraction process, time-resolved environmental monitoring can be achieved onsite within minutes.

Natural Scene Nutrition Information Acquisition and Analysis Based on Deep Learning

Background: In the field of personalized health, it is often difficult for individuals to obtain professional knowledge to solve their practical problems timely and accurately. While there are some applications that can get targeted information, they often fail to function properly in nonideal environments, and they cannot achieve precise answers to individual users. Therefore, how to establish an information capture model based on big data and combine it with intelligent search is an important issue in the field of personalized health. Objective: This paper starts with the information acquisition and intelligent recommendation in the field of personalized health, and proposes a natural scene information acquisition and analysis model based on deep learning, focusing on improving the recognition rate of text in natural scenes and achieving targeted smart search to allow users to get more accurate personalized health advice. Methods: In this model, natural scene information is processed from four aspects: targeted big data collection and search, connected text proposal network text detection algorithm and projectionbased text segmentation, capsule network text recognition and result analysis. The model reduces recognition bias due to problems such as special filming conditions and photographic techniques by using deep learning algorithms. At the same time, the data mining has also improved the pertinence of the results analysis. Conclusion: This model combines deep learning and data mining methods to obtain intelligent solutions at a professional level by uploading target information images in non-ideal environments, and is suitable for accurate analysis of problems in personalized health area. Results: The proposed model is applied to analyze the user's nutrient intake requirements. The results show that the method achieves 83% prediction accuracy on the nutrient composition table dataset, and its performance is better than current convolutional neural network applications. And the model can get accurate personalized data to provide users with dietary advice.

RSS and Phase Kalman Filter Fusion for Improved Velocity Estimation in the Presence of Real-World Factors

Motion estimation of RFID tags has been shown to be possible using phase information, while position estimation is possible using received signal strength (RSS). This article combines phase and RSS information using a Kalman filter as sensor fusion technique. It compares the combined RSS and phase fusion Kalman filter technique for motion estimation with the phase only Kalman filter, exponential smoothing filter and raw phase motion estimation excluding a Kalman filter. The influence of frequency hopping broadcasting regulations on the performance of these techniques is investigated, an aspect that has not been properly addressed before. The primary factors found in real-world scenarios that have an effect on motion estimation techniques are identified as multipath reflections and the presence of multiple tags. The influence of such non-ideal environments are characterized and specific vulnerabilities of these techniques are investigated. Finally, the shortcoming of these techniques as well as advantages and possible environments where this technique can be successfully used are discussed.

Iris Segmentation Using Feature Channel Optimization for Noisy Environments

In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.

Experimental and Numerical Study of Supersonic Non-ideal Flows for Organic Rankine Cycle Applications

Abstract The organic Rankine cycle (ORC) is low-grade heat recovery technology, for sources as diverse as geothermal, industrial, and vehicle waste heat. The working fluids used within these systems often display significant real-gas effects, especially in proximity of the thermodynamic critical point. Three-dimensional (3D) computational fluid dynamics (CFD) is commonly used for performance prediction and flow field analysis within expanders, but experimental validation with real gases is scarce within the literature. This paper therefore presents a dense-gas blowdown facility constructed at Imperial College London, for experimentally validating numerical simulations of these fluids. The system-level design process for the blowdown rig is described, including the sizing and specification of major components. Tests with refrigerant R1233zd(E) are run for multiple inlet pressures, against a nitrogen baseline case. CFD simulations are performed, with the refrigerant modeled by ideal gas, Peng–Robinson, and Helmholtz energy equations of state. It is shown that increases in fluid model fidelity lead to reduced deviation between simulation and experiment. Maximum and mean discrepancies of 9.59% and 8.12% in nozzle pressure ratio with the Helmholtz energy EoS are reported. This work demonstrates an over-prediction of pressure ratio and power output within commercial CFD packages, for turbomachines operating in non-ideal fluid environments. This suggests a need for further development and experimental validation of CFD simulations for highly non-ideal flows. The data contained within this paper are therefore of vital importance for the future validation and development of CFD methods for dense-gas turbomachinery.

Resilient Load Frequency Control of Cyber-Physical Power Systems Under QoS-Dependent Event-Triggered Communication

This article investigates resilient event-triggered load frequency control (LFC) of multiarea power systems under nonideal network environments. Under the sample-data framework, a novel QoS-dependent event-triggered communication (QEC) scheme is presented to deal with nonideal network environments while preserving the desired control performance and improving the communication efficiency. In comparison with some traditional state-dependent event-triggered communication schemes, since the proposed QEC depends not only on the state of controlled plant but also on the QoS of communication network, higher communication efficiency can be achieved. Then, a resilient LFC is well developed based on the proposed QEC, where “resilient” implies that: 1) for a normal QoS case, less packets are transmitted to save the communication resources and 2) for an abnormal QoS case, more packets are transmitted to mitigate the influence of nonideal QoS. Moreover, the proposed method provides a better way to balance the control performance and communication resource by reasonably choosing the parameters of QEC. Finally, the simulation results show the effectiveness of the proposed method.

Optimal control of Raman pulse sequences for atom interferometry

We present the theoretical design and experimental implementation of mirror and beamsplitter pulses that improve the fidelity of atom interferometry and increase its tolerance of systematic inhomogeneities. These pulses are designed using the GRAPE optimal control algorithm and demonstrated experimentally with a cold thermal sample of 85Rb atoms. We first show a stimulated Raman inversion pulse design that achieves a ground hyperfine state transfer efficiency of 99.8(3)%, compared with a conventional π pulse efficiency of 75(3)%. This inversion pulse is robust to variations in laser intensity and detuning, maintaining a transfer efficiency of 90% at detunings for which the π pulse fidelity is below 20%, and is thus suitable for large momentum transfer interferometers using thermal atoms or operating in non-ideal environments. We then extend our optimization to all components of a Mach–Zehnder atom interferometer sequence and show that with a highly inhomogeneous atomic sample the fringe visibility is increased threefold over that using conventional π and π/2 pulses.

Lensless fiber-deployed low-coherence interferometer for in-situ measurements in nonideal environments

Low-Coherence Interferometry (LCI) is a well-established optical method used for obtaining geometric measurements, suited to operating in non-ideal environments as shown through its use in biomedical science where it is referred to as Optical Coherence Tomography (OCT). However, work on characterising this technologies ability to work in-situ within the area of manufacturing is yet to be demonstrated. This research is motivated by the need to develop robust sensors capable of operating in the harsh environment of manufacturing processes in near real-time, providing on demand process control for the next generation of precise, and highly adaptive schemes of production. The evaluation of a common-path, lensless, spectral-domain, LCI based sensor for measurements of step-heights in air and in the non-ideal operating environment of water is demonstrated. Calibration experiments have explored linearity of measurements over a 1 mm investigated axial range with deviations of the order of 50 nm in air and 100 nm in water. Step heights of 8 micrometres, 7 micrometres, 6 micrometres and 5 micrometres were measured in air and also with the sample and sensing probe submerged in water. Step heights in both mediums closely align with calibrated specifications given by the manufacturer demonstrating sub-micrometre accuracy and a precision of 56 nm in air and 76 nm in water.

Effect of bacterial encapsulation in concrete: A review on applications and effects

Concrete being durable, strong and most widely used construction material, however, it is quite vulnerable to cracking due to its inherent heterogeneity and non-ideal service environments. These cracks show improvement with time if they are not healed. Self-healing has been one of the most promising way to repair the cracks at the early stage of formation and thus reduce the repair cost. Self-healing methods like the addition of bacteria, geo-polymers etc. are being adopted. However, the efficient way to heal the inside as well as surface cracks is the incorporation of bacteria in the concrete. The healing efficiency of cracks has increased to a large extent when bacteria are encapsulated inside a carrier, as the carrier protects the bacteria from harsh environment of concrete. In this review, the use of various materials for bacterial encapsulation is analyzed. The encapsulation materials are hydrophilic in nature and have high water retention capacity, thus provides favorable environment for bacterial activity after crack formation. The bacteria (Genus Bacillus) are incorporated in the concrete at the time of mixing. The bacteria show ureolytic activity and carbonatogenesis and fill the crack in efficient way. This paper reveals the impact of encapsulation material on the healing properties of concrete as the bacterial encapsulation is eco-friendly and requires less human intervention for crack healing compared to the conventional concrete. The properties like permeability, compressive strength, healing ratio were analyzed by different techniques like X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and Ultra-sonic Pulse Velocity Test (UPVT) and it was observed that crack healing in concrete using bio-based mechanisms is more efficient, eco-friendly and economical due to higher deposition of binders (calcium carbonate) inside the cracks.

Analysis and Application of Modified ZNN Design With Robustness Against Harmonic Noise

The Zhang neural network (ZNN) has recently realized remarkable success in solving time-varying problems. Harmonic noise widely exists in industrial applications and can severely affect the solution computed by ZNN models. This article attempts to solve the aforementioned limitations by providing the first ZNN design with an inherent capability to prohibit harmonic noise. Moreover, it opens new opportunities to shift the research on ZNNs in ideal situations to that with theoretical consideration on nonideal working environments. We establish a modified ZNN design formula in a noisy environment by incorporating the dynamics of harmonic signals. Theoretical analysis shows the convergence of the proposed ZNN design. An application case study for the new ZNN model verifies its effectiveness for time-varying matrix inversion in the presence of harmonic noise. The simulation further substantiates the effectiveness, superiority, and application prospects of the proposed ZNN design.

Laboratory and in situ sound absorption measurement under a synthetized diffuse acoustic field

This article reports numerical and experimental results concerning the estimation of the diffuse field sound absorption coefficient of several different materials under a synthetized diffuse acoustic field excitation in laboratory and in situ conditions. The proposed measurement method is based on a sound field reproduction approach and a synthetic array of acoustic monopoles facing the material to be tested. Numerical simulations are first conducted to optimize the geometrical parameters of the method and to compute theoretical sound absorption coefficients of the considered materials. Measurements on a set of six typical acoustic materials are then conducted following the standardized reverberant room method as well as the proposed approach in a hemi-anechoic room and in two realistic rooms. Albeit showing limitations in the low-frequency domain, the proposed method enables a significant reduction of the tested specimen dimensions compared with the reverberant room method and allows performing tests in non-ideal acoustic environments.

WONDER SUSTAINED: A REPLY TO CRITICS

AbstractA set of science‐inspired cosmic narratives referred to as the Epic of Evolution and the Universe Story or, collectively, the new cosmology, proposes to bring humans closer to nature by placing us into the broader narrative of the cosmos. This article responds to commentary and critique on my book Consecrating Science: Wonder, Knowledge, and the Natural World, which critically examines these science‐based cosmic narratives and their particular and problematic modes and objects of wonder. Themes include the relationship of wonder to science and ethical engagement; the question of whether wonder, grounded in everyday sensory experience, can scale up to the level of global environmental problems; the relevance of wonder to nonideal environments and negative affects like fear or grief; and the importance of humanistic and religious studies scholarship for critiquing grand narratives of science, among other themes. I also respond to claims that my book misdiagnoses and distorts the work of the new cosmology and its claims to wonder.

On our approaches toward expanding application areas utilizing GNSS high precision positioning

GNSS high precision positioning has high potential for being applied to wide range of industrial areas. In this paper introduced our recent approaches toward expanding application areas of GNSS high precision positioning. We have launched cost-effective correction data delivery services for GNSS carrier-phase positioning over mobile communication networks. We also study the feasibility of cloud-based GNSS positioning architecture which is expected to improve the positioning accuracy in non-ideal GNSS signal reception environments including urban-canyon environments.

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

Temperature-Programmed Desorption of Pyridine on Zeolites in the Presence of Liquid Solvents

Selecting the proper solvent is a major challenge involved in biomass conversion processes as there is little predictive understanding of how solvent choice will affect reaction rates. To date, most attempts to understand solvent behavior are on a case by case basis, through catalytic activity testing and screening multiple solvent choices. There is a need for characterization techniques aimed toward fundamental understanding of solvent behavior in a simple and systematic fashion. In this work, the effect of solvent on the Bronsted acid site–pyridine interaction was isolated to improve understanding of a solvent’s ability to stabilize charge in nonideal environments like zeolite micropores. Pyridine temperature-programmed desorption (TPD) was applied to zeolites ZSM-5, Beta, and Y in back-pressurized, flowing solvent. The desorption temperatures of pyridine from Bronsted acid sites in vacuum were compared in liquid water, acetonitrile, and n-heptane. For H/ZSM-5 (Si/Al = 12), the pyridine desorption tempe...