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A novel approach to monitor the degree of bending via flexible photonics devices, using ratiometric power variation in a few-mode optical waveguide, is proposed. To demonstrate its feasibility, a sensor exploiting a Bragg grating, approximately aligned to the neutral axis, is designed, fabricated and characterized. The reduced thickness of the proposed planar photonic sensor is uniquely limited by optical confinement requirements, enabling for an ultra-thin and highly flexible planar device, based on all-glass platform. Finite Element Method, Beam Propagation Method and Coupled Mode Theory are employed in the design to model the electromagnetic and mechanical phenomena occurring during the three-point bending test. The experiment demonstrates that the planar device withstands tight curvature without mechanical failure. The device shows that by increasing the bending the reflected power from the fundamental mode decreases and the reflected power from the higher order mode increases. The measured ratiometric power sensitivity versus displacement is $K_{P_{R}}=-0.78 \mathrm {dB/mm}$ with negligible variation over a 40°C thermal range. Therefore, exploiting both the Bragg wavelength shift and the mode optical power change, the proposed sensor can be employed for multiparameter sensing purposes, e.g. simultaneous temperature and curvature monitoring.

Parkinson’s disease is a degenerative neurological disorder that impairs motor functions and is accompanied by a wide range of non-motor symptoms, such as sleep problems. Parkinsonism is assessed during clinical evaluations and via self-administered diaries and, based on these, the required medication therapies are provided to lessen symptoms. Tri-axial accelerometers and gyroscopes have the potential utility to objectively assess the patient’s condition and aid clinicians in their decision-making. People with Parkinson’s often have significant abnormalities in blood pressure due to comorbid age-related cardiovascular disease and orthostatic hypotension, which result in blurred vision, dizziness, and falls. Frequent blood pressure monitoring may aid in the evaluation of such events and differentiate Parkinson’s disease symptoms from those originated by hypotension. In the present paper, a novel technology for the remote monitoring of Parkinsonian symptoms is presented: the WESAA system. It consists of two devices, worn on the wrist and ankle; its main function is to record accelerations and angular velocities from these body parts, together with photoplethysmograph and electrocardiogram data. This information can be elaborated offline to measure common Parkinson’s disease motor symptoms (e.g., tremor, bradykinesia, and dyskinesia), as well as gait speed, sleep-wake cycles, and cuff-less blood pressure measurements. The overall system requirements, market overview, industrial design and ergonomics, system development, user experience, early results of the gathered inertial raw data, and validation of the photoplethysmograph and electrocardiogram signal waveforms are all thoroughly discussed. The developed technology satisfies all system requirements, and the sensors adopted provided outcomes comparable with gold standard techniques.

Chirp spread spectrum (CSS) signal is the heart of long-range (LoRa) modulation, also known as CSS modulation, and is used in long-range wide area network (LoRaWAN) in Internet of things (IoT) scenarios. CSS modulation has drawn much attention from the research and industry communities in recent years. However, to the best of our knowledge, a comprehensive tutorial, investigating the mathematical foundations of CSS modulation in the LoRaWAN application, is missing in the literature. Therefore, in the first part of this paper, we provide a thorough analysis and tutorial of CSS modulation as the physical (PHY) layer of LoRaWAN, discussing various aspects such as signal generation, detection, error performance, and spectral characteristics. Moreover, a summary of key recent advances in the context of CSS modulation modifications and applications in IoT networks is presented in the second part of this paper under four main categories, i.e., transceiver configuration and design, data rate improvement, interference modeling, and synchronization algorithms. Finally, future research directions are also provided to discuss the potential issues and solutions for improving the performance of CSS modulation in LoRaWAN.

This paper presents a novel design method for a dual-band power amplifier (PA) with widely-spaced frequency bands, suitable for concurrent sub-6 GHz and millimeter-wave 5G band operation. The approach de-couples the design of the high- and low-frequency impedance matching networks, so that the high-frequency matching network can be fully designed before the low-frequency path is added. The circuit architecture includes a built-in bias structure for the high-frequency band so that a separate broadband or multiband bias network is avoided. This design process is first described theoretically, and a detailed design example of a C/Ka concurrent dual-band MMIC using quarter-wavelength lines implemented in a 150 nm GaN process is described. The MMIC is measured in CW with 32.5 dBm and 33% peak power and PAE in C band and 31 dBm and 24% peak power and PAE in Ka-band. Measurements with 100-MHz LTE-like modulated signals are also shown, including both individual and concurrent operation of the two bands.

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here.

To remain competitive in a highly dynamic environment, manufacturing companies have to quickly react to disturbances or changing customer requirements. To enable manufacturing systems to cover these dynamics, the concept of reconfigurable manufacturing systems was introduced. From a technical point of view, this concept has been exploited for the past 20 years, revealing several different design solutions. However, industrial application is still an exception. Our analysis led to the assumption that this is due to a lack of operator support for reconfiguration management. In addition, mostly individual aspects of reconfiguration are considered instead of exploiting the entire reconfiguration space at the system and machine level. Therefore, in this paper, we present a digital twin framework for reconfiguration management considering reconfiguration as a holistic problem. We evaluate the framework by conducting a case study and challenging it by evaluating the completeness based on a systematic literature review, and analyze if it follows good practice based on 32 requirements for digital twin frameworks.

This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [${\rm {DA}}_{\rm {v1}}$] and without [${\rm {DA}}_{\rm {v2}}$] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and ${\rm {DA}}_{\rm {v2}}$. Comparison of SWE estimates from Sentinel-1 ${\rm {DA}}_{\rm {v1}}$ against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to in situ measurements. River discharge using Sentinel-1 ${\rm {DA}}_{\rm {v1}}$ improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.

This paper presents a design methodology for a load modulated balanced amplifier (LMBA) focusing on AMPM distortion mitigation. By introducing second harmonic control as a degree of design freedom, a complex load trajectory can be selected to compensate for AMPM nonlinearities in the device without substantially affecting efficiency. A mathematical derivation is accompanied by a design procedure based on closed-form equations to fabricate an LMBA based solely on load-pull data. The theory is validated through the measured comparison of three different designs operating at 2.4 GHz in a pseudo-RF-input Doherty-like LMBA configuration, with class-J, -B, and -J* main PAs. The class-J prototype outperforms the other designs with 54% and 49% drain efficiency at peak output power and 6-dB back-off, respectively, and only 4 degrees of AM-PM across this power range. When driven with a 10-MHz, 8.6-dB PAPR LTE signal, 40.5% average efficiency is achieved with better than $-40.5$ dBc ACLR without digital predistortion.

Manufacturers are constantly looking to enhance the performance of their manufacturing systems by improving their ability to address disruptions and disturbances, while reducing cost and maximizing quantity and quality. Even though innovative mechanisms for adaptability and flexibility continuously contribute to the smart manufacturing evolutionary process, they generally stop short of providing a capability for coordinated on-line learning. This is especially true when that learning requires exploration outside of established operational boundaries or uses artificial intelligence (as opposed to purely human intelligence) as part of the dynamic implementation of learning. In this work, we provide a vision for the development of an automated learning control architecture to extend the adaptability and flexibility capabilities of manufacturing systems. As part of this vision, we describe a set of requirements and objectives that, if addressed, provide an environment to allow distributed and automated learning across the manufacturing ecosystem. We then provide an example communication and control architecture that meets these requirements and objectives by gathering information, building a dynamic knowledge base, distributing intelligence, making decisions, and adapting the control commands sent to the equipment and people across the manufacturing ecosystem. The example architecture leverages both centralized and distributed control strategies and the ability to switch between the strategies to gather and learn from information in the system. Example case studies are provided illustrating how this architecture can be used to improve manufacturing system performance.

Open Process Automation (OPA) and Digital Twin (DT) technologies show great promise to reduce risk, downtime, and energy consumption, while improving safety and efficiency in manufacturing systems. OPA defines a reference architecture for the construction of scalable, reliable, interoperable, and secure automation systems with products from multiple vendors as a single, cohesive system. DTs are purpose-driven dynamic digital replicas of physical assets, processes, systems, or products. Both technologies enable increased options and competition for accelerating future innovation. However, there are significant challenges to adopting these technologies, including “plug-and-play” interoperability, access to data, access to equipment, and the combination of different DTs for system-wide improvements. This paper demonstrates and evaluates a DT Framework solution for performance monitoring in process manufacturing systems that aims to avoid unplanned downtime, a prevalent challenge that pressures profitability in manufacturing. The DT framework is built and demonstrated through an OPA testbed system that allows seamless gathering and analyzing of data from a process manufacturing line. The proposed DT framework solution provides guidelines to develop, test, and evaluate new system-wide DT solutions without interrupting production operations and without costly R&D investments.

This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently.

This paper presents a study of supply modulation in a Doherty power amplifier (DPA). To validate a simplified theoretical model, a 3.5 GHz conventional symmetrical DPA using a 6-W packaged GaN pHEMT is designed for supply modulation of the main and/or auxiliary amplifiers. The DPA is characterized in CW operation over a range of supply voltages, and shows up to 15 percentage point efficiency improvement at 12 dB output back-off when both main and auxiliary amplifiers are varied simultaneously. A comparison of three cases of supply variation is shown and potential benefits for improving back-off efficiency are discussed.

An ensemble Kalman filter is used to assimilate Advanced Microwave Scanning Radiometer-2 (AMSR2) observations of passive microwave (PMW) brightness temperatures (spectral differences, $\Delta T_b$) into land surface model estimates of snow mass over northwestern high mountain Asia (HMA). Trained support vector machines serve as the observation operator and map the geophysical modeled variables into $\Delta T_b$ space within the assimilation framework. Evaluation of the assimilation routine is carried out through comparison of assimilated snow mass estimates with an in situ dataset. The assimilation framework helps improve the land surface model estimates through PMW $\Delta T_b$ assimilation, particularly in terms of decreasing the domain-wide bias. The assimilation framework proved more effective during the (dry) snow accumulation season and decreased the bias and root-mean-square error (RMSE) in snow mass estimates at 76% and 58% of the comparative pixels, respectively. During the snow ablation season, the PMW brightness temperature signal contained less information related to snow mass due to the presence of other concurrent geophysical features that effectively serve as noise during the snow mass update. The utilization of PMW $\Delta T_b$ for accurate snow mass estimation in complex terrain such as HMA is dependent on a multitude of factors for optimal results; however, it does add utility to the land surface model if the relevant pitfalls are taken into consideration prior to the state variable update.

Digital Twin (DT) is an emerging technology that has recently been cited as an underpinning element of the digital transformation. DTs are commonly defined as digital replicas of components, systems, products, and services that receive data from the field to support intelligent decision-making. Although several frameworks for DT application in manufacturing have been proposed, there is no systematic methodology in the literature that supports the development of scalable, reusable, interoperable, interchangeable, and extensible DT solutions, while taking into account specific manufacturing environment needs and conditions. This paper introduces a DT solution development methodology as a generic procedure for analyzing and developing DTs for manufacturing systems. The methodology is based on the well-known System Development Life Cycle (SDLC) process and takes into consideration: (1) the specificity of DT characteristics and requirements, (2) an understanding of the manufacturing context in which the DTs will operate, and (3) the object-oriented aspects required to achieve DT capabilities of scalability, reusability, interoperability, interchangeability, and extensibility. A case study illustrates the advantages of the proposed methodology in supporting manufacturing DT solutions.

The National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADxSM) Tech initiative to support the development and commercialization of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) point-of-care test devices. The primary objective of the Clinical Studies Core (CSC) was to perform SARS-CoV-2 device studies involving diverse populations and settings. Within a few months, the infrastructure for clinical studies was developed, including a master protocol, digital study platform, data management system, single IRB, and multi-site partnerships. Data from some studies are being used to support Emergency Use Authorization of novel SARS-CoV-2 test devices. The CSC reduced the typical time and cost of developing medical devices and highlighted the impactful role of academic and NIH partnership in addressing public health needs at a rapid pace during a global pandemic. The structure, deployment, and lessons learned from this experience are widely applicable to future in vitro diagnostic device clinical studies.

The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 a.m. versus 6 p.m. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions.

Among the tenets of Smart Manufacturing (SM) or Industry 4.0 (I4.0), digital twin (DT), which represents the capabilities of virtual representations of components and systems, has been cited as the biggest technology trend disrupting engineering and design today. DTs have been in use for years in areas such as model-based process control and predictive maintenance, however moving forward a framework is needed that will support the expected pervasiveness of DT technology in the evolution of SM or I4.0. A set of requirements for a DT framework has been derived from analysis of DT definitions, DTs in use today, expected DT applications in the near future, and longer-term DT trends and the DT vision in SM. These requirements include elements of re-usability, interoperability, interchangeability, maintainability, extensibility, and autonomy across the entire DT lifecycle. A baseline framework for DT technology has been developed that addresses many aspects of these requirements and enables the addressing of the requirements more fully through additional specification. The baseline framework includes a definition of a DT and an object-oriented (O-O) architecture for DTs that defines generalization, aggregation and instantiation of DT classes. Case studies using and extending the baseline framework illustrate its advantages in supporting DT solutions and trends in SM.

Attacks on software systems occur world-wide on a daily basis targeting individuals, corporations, and governments alike. The systems that facilitate maritime shipping are at risk of serious disruptions, and these disruptions can stem from vulnerabilities in the software and processes used in these systems. These vulnerabilities leave such systems open to cyber-attack. Assessments of the security of maritime shipping systems have focused on identifying risks but have not taken the critical (and expensive) next step of actually identifying vulnerabilities present in these systems. While such risk assessments are important, they have not provided the detailed identification of security issues in the systems that control these ports and their terminals. In response, we formed a key collaboration between an experienced academic cybersecurity team and a well-known commercial software provider that manages maritime shipping. We performed an analysis of the information flow involved in the maritime shipping process, and then executed an in-depth vulnerability assessment of the software that manages freight systems. In this paper, we show the flow of information involved in the freight shipping process and explain how we performed the in-depth assessment, summarizing our findings. Like every large software system, maritime shipping systems have vulnerabilities.

This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running.

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here. [ABSTRACT FROM AUTHOR]