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The need for standardized guidelines and common evaluation techniques for assessing camouflage nets represents a challenge for the military operational community. This paper proposes a laboratory assessment methodology for the evaluation of camouflage nets. This method aims to be an accurate initial assessment before field tests, as the final stages for camouflage evaluation. Furthermore, it is more convenient to standardize laboratory indoor setups as the initial step because parameters can be controlled with precise equipment, eliminating any unexpected technical variables. Our approach combines radar testing in a semi-anechoic chamber and EO/IR (Electro-Optical/Infra-Red) assessment using an indoor laboratory setup. This method offers a more comprehensive and accurate evaluation compared to methods that focus on a single type of test. Following this procedure, we can assess the performance characteristics of camouflage nets by determining their behavior in all spectral domains associated with surveillance optical and radar equipment. For the EO/IR devices, we are interested in analyzing the nets in the $0.35-2.5\: \mu $ m wavelength band, and both the mid-wave infrared (MWIR) $3-5\: \mu $ m and long-wave infrared (LWIR) $8-12\: \mu $ m thermal infrared transmission bands. For the radar systems, we focus on the X-band frequency in the range of 9 GHz to 11 GHz. The paper presents the results obtained by measuring three different multispectral camouflage net samples. It is shown that Samples Under Test (SUT) have relatively consistent average radar transmission attenuation with a small range of 2.22 dB. Furthermore, the average attenuation values vary between 3.3 dB and 14.71 dB when the camouflage net is rotated for further evaluation. Moreover, radar reflection results characterized by reflection coefficient values, standard deviation, mean, and Coefficient of Variation (CV), show that all samples, regardless of orientation, deviate from the consistent reference data of ten runs. In the thermal infrared domain, the nets are similar in terms of solar loading, showing a similar temperature increase of 8 ± 1° C, but the transmission levels are different, between 11.7% and 21.8%. As for the reflectance measurements, the three SUT show different behaviors in terms of reflectance levels and contrast patterns, especially in the near and shortwave infrared bands of the EO domain.

This paper presents an ultra-broadband, low-loss, flexible liquid crystal polymer substrate-to-substrate interconnect with a bandwidth of more than 130 GHz. The transition discontinuity was minimized by maintaining both the reference impedance and the electromagnetic field conformity across the transition from alumina substrate to flip-chip foil. Therefore, more than 600 $\mu$m long flexible flip-chip interconnects can be realized for bridging millimeter-wave sub-modules and enabling ultra-broadband heterogeneous system design with a measured return loss of above 20 dB. Furthermore, the interconnect can realize ramp interconnections between monolithic microwave integrated circuits or substrates with different substrate heights due to the flexible foil substrate. Minimum parasitic radiation at the transition is realized through a closely spaced signal-to-ground connection. Furthermore, the robustness of the proposed interconnect against lateral misalignment in the assembly is presented through simulation and measurement. An outstanding insertion loss of less than 0.3 dB per transition over a bandwidth of more than 130 GHz is shown.

Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of “Production IT.” Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.

Surface reflectance is an important data product in imaging spectroscopy for obtaining surface information. The complex retrieval of surface reflectance, however, critically relies on accurate knowledge of atmospheric absorption and scattering, and the compensation of these effects. Furthermore, illumination and observation geometry in combination with surface reflectance anisotropy determine dynamics in retrieved surface reflectance not related to surface absorption properties. To the best of authors' knowledge, no comprehensive assessment of the impact of atmospheric, topographic, and anisotropy effects on derived surface information is available so far. This study systematically evaluates the impact of these effects on reflectance, albedo, and vegetation products. Using three well-established processing schemes (ATCOR F., ATCOR R., and BREFCOR), high-resolution APEX imaging spectroscopy data, covering a large gradient of illumination and observation angles, are brought to several processing states, varyingly affected by mentioned effects. Pixel-wise differences of surface reflectance, albedo, and spectral indices of neighboring flight lines are quantitatively analyzed in their respective overlapping area. We found that compensation of atmospheric effects reveals actual anisotropy-related dynamics in surface reflectance and derived albedo, related to an increase in pixel-wise relative reflectance and albedo differences of more than 40%. Subsequent anisotropy compensation allows us to successfully reduce apparent relative reflectance and albedo differences by up to 20%. In contrast, spectral indices are less affected by atmospheric and anisotropy effects, showing relative differences of 3% to 10% in overlapping regions of flight lines. We recommend to base decisions on the use of appropriate processing schemes on individual use cases considering envisioned data products.

Many robotic systems have emerged in the recent past as cutting-edge solutions to enhance the capabilities of ophthalmic surgeons in order not only to increase the quality of conventional operations but also to enable new and advanced interventions such as gene- and stem-cell-based therapies. Some of these operations require precise and stable delivery of therapeutics into the sub-retinal domain and therefore, automatic procedures with micron precision at the tooltip are essential. One of the most critical parameters to precisely maintain the tooltip in automated robotic retinal surgery is the appropriate configuration and control of the Remote Center of Motion (RCM). The RCM precision might be affected by any physical uncertainties, such as instrument assembling, or minor kinematic changes. Therefore, an accurate RCM identification requires an extensive calibration plan before each operation. This paper presents a novel preoperative evaluation-calibration method for kinematic-adjustable software-based RCM robots. Our key concept is decomposing the 3D workspace into two orthogonal working planes in order to reduce the complexity while adding robustness to RCM evaluation and software calibration. First, we propose an ablation-based RCM-related analysis method of the kinematics of the robot. Using a Convolutional Neural Network (CNN), we analyze image-based RCM along the instrument during a predefined RCM motion maneuver. Utilizing software calibration protocol by prior-analyzed RCM-related kinematic parameters, the software calibration is done automatically. The process is repeated until the RCM accuracy is set within a clinically acceptable range. Evaluation of the method on a highly accurate 5-DOF-Software-RCM robot demonstrated significant optimization in RCM error within an average of 4 minutes for each plane and 0.300 ± 0.20 mm accuracy.

The electromagnetic utilization of an electrical machine can be increased by raising the current density, with a limitation resulting from the maximum permissible temperature of the winding insulation. Conventional insulating materials only have a low specific thermal conductivity, which results in large temperature gradients in the winding cross section and consequently hotspots. This article presents the development and characterization of ceramic-like composite materials based on filled polysiloxanes for the dip coating of lamination stacks and the impregnation of the winding of electrical machines. The specific equivalent thermal conductivity is determined on composite winding samples, the thermal cycle stability is examined and the microstructure is analyzed with the scanning electron microscope. The manufacturability and the partial discharge behavior are examined based on test samples. The results of tests on an electric traction machine are used to show the potential for increasing the power density.

Time Reversal (TR) has been proposed as a competitive precoding strategy for low-complexity devices, relying on ultra-wideband waveforms. This transmit processing paradigm can address the need for low power and low complexity receivers, which is particularly important for the Internet of Things, since it shifts most of the communications signal processing complexity to the transmitter side. Due to its spatio-temporal focusing property, TR has also been used to design multiple access schemes for multi-user communications scenarios. However, in wideband time-division multiple access schemes, the signals received by users suffer from significant levels of inter-symbol interference as well as interference from uncoordinated users, which often require additional processing at the receiver side. This paper proposes an iterative TR scheme that aims to reduce the level of interference in wideband multi-user settings, while keeping the processing complexity only at the transmitter side. The performance of the proposed TR-based protocol is evaluated using analytical derivations. In addition, its superiority over the conventional Time Reversal Division Multiple Access (TRDMA) scheme is demonstrated through simulations as well as experimental measurements at 2.5 GHz carrier frequency with variable bandwidth values.

This article describes the design and performance of a vacuum-seal waveguide feedthrough with ultrawide RF band 67–116 GHz. We describe first the initial drivers behind the chosen design, then we present the results of the numerical simulations and optimization and provide thereafter the results of the RF and vacuum tests of the fabricated devices. The demonstrated RF performance is very close to the one expected from the simulation with an insertion loss less than 0.3 dB and a return loss better than 20 dB. Simultaneously, the feedthrough shows excellent vacuum isolation, Helium gas leak rate of < 2x10−8 mbar·L/s is demonstrated, which allows using such a device in various space and ground applications.

The main contribution of this paper is a numerical ship motion model of NTNU’s research vessel Gunnerus, capturing the surge, sway, roll, and yaw dynamics when sailing in uniform and steady currents. The model utilizes a crossflow drag formulation for the transverse viscous loads, and it includes a nonlinear formulation for the propulsion and steering loads provided by two azipod thrusters. A wide range of experimental data obtained from sea trials are used for model calibration and validation. The model is intended for development of Decision Support Systems (DSS) that provide the helmsman with recommendations for safe maneuvers. As a demonstration, the model is used to generate input to a previously proposed DSS solution, which uses offline simulations to create a database of the critical navigation area for different encounter scenarios. Additionally, we propose a DSS solution that uses online simulations to predict the future ship trajectory under guidance of a virtual autopilot. The virtual autopilot is designed using a novel hybrid control barrier function formulation to predict the need of evasive maneuvers for collision avoidance.

Face recognition systems are widely deployed for biometric authentication. Despite this, it is well-known that, without any safeguards, face recognition systems are highly vulnerable to presentation attacks. In response to this security issue, several promising methods for detecting presentation attacks have been proposed which show high performance on existing benchmarks. However, an ongoing challenge is the generalization of presentation attack detection methods to unseen and new attack types. To this end, we propose a new T-shirt Face Presentation Attack (TFPA) database of 1,608 T-shirt attacks using 100 unique presentation attack instruments. In an extensive evaluation, we show that this type of attack can compromise the security of face recognition systems and that some state-of-the-art attack detection mechanisms trained on popular benchmarks fail to robustly generalize to the new attacks. Further, we propose three new methods for detecting T-shirt attack images, one which relies on the statistical differences between depth maps of bona fide images and T-shirt attacks, an anomaly detection approach trained on features only extracted from bona fide RGB images, and a fusion approach which achieves competitive detection performance.

Electric power systems worldwide are undergoing a foundational transition from mechanical-backed generation technologies dominating the resource mixture, primarily synchronous generators, into a hybrid system consisting of periods of a preponderance of power electronics-backed generation technologies, primarily solar photovoltaics and wind power plants and battery storage systems. Almost all current inverter-based resources integrated into bulk power systems are grid-following technology, and there exists a knowledge gap concerning the impacts of large-scale integration of grid-following inverters on power system dynamics, which is a critical aspect of power system planning and operation. This paper serves as a tutorial and addresses the stability and reliability challenges pertinent to the integration of grid-following interfaced inverter-based resources. While considering both small-signal and large-signal stability problems, it demonstrates and explains the underlying interrelated dynamics of electric angle, frequency, and voltage, as well as the impacts that system inertia can have on system stability. Industry-grade electromagnetic transient simulations in Power Systems Computer-Aided Design (PSCAD) are utilized to demonstrate the concepts presented in this paper, and all the computer models have been made available to the public at no cost.

Bio-acoustic properties of speech show evolving value in analyzing psychiatric illnesses. Obtaining a sufficient speech sample length to quantify these properties is essential, but the impact of sample duration on the stability of bio-acoustic features has not been systematically explored. We aimed to evaluate bio-acoustic features’ reproducibility against changes in speech durations and tasks. We extracted source, spectral, formant, and prosodic features in 185 English-speaking adults (98 w, 87 m) for reading-a-story and counting tasks. We compared features at 25% of the total sample duration of the reading task to those obtained from non-overlapping randomly selected sub-samples shortened to 75%, 50%, and 25% of total duration using intraclass correlation coefficients. We also compared the features extracted from entire recordings to those measured at 25% of the duration and features obtained from 50% of the duration. Further, we compared features extracted from reading-a-story to counting tasks. Our results show that the number of reproducible features (out of 125) decreased stepwise with duration reduction. Spectral shape, pitch, and formants reached excellent reproducibility. Mel-frequency cepstral coefficients (MFCCs), loudness, and zero-crossing rate achieved excellent reproducibility only at a longer duration. Reproducibility of source, MFCC derivatives, and voicing probability (VP) was poor. Significant gender differences existed in jitter, MFCC first-derivative, spectral skewness, pitch, VP, and formants. Around 97% of features in both genders were not reproducible across speech tasks, in part due to the short counting task duration. In conclusion, bio-acoustic features are less reproducible in shorter samples and are affected by gender.

Prediction of Speech Intelligibility (SI) is a topic of interest for most speech processing applications, where intelligibility is of any importance, e.g., speech coding, transmission and enhancement. Traditionally, SI predictors have been based on signal processing methods and heuristics, but more recently, an increasing number of data-driven SI-predictors have been proposed. Data-driven prediction of SI requires large quantities of labelled data, ideally from many listening tests. Listening tests differ in factors such as vocabulary, talker, listener’s task, etc. collectively referred to as the paradigm. A naïve strategy of training SI-predictors directly on stimuli, pooled from different listening tests, is futile because the exact map from the stimulus to SI is determined, not only by the stimulus, but also by the paradigm. Data-driven SI-predictors trained in this way become specialized to the paradigms of the training data by erroneously attributing all paradigm influences on SI to the stimulus. The problem is fundamental and persists even in the idealized situation where training data is abundant. We propose a strategy for training data-driven SI-predictors that is independent of the paradigms, underlying the training data. The proposed strategy is to concatenate an SI-predictor and a layer of trainable dataset-specific mapping functions, each corresponding to a single paradigm in the training data. These mapping functions are trained jointly with the SI-predictor and serve to efficiently approximate the psychometric functions implied by each paradigm. The mapping functions prevent the predictor from specializing to these paradigms during training. We present an SI-predictor with a novel architecture that incorporates a convolutional network and an ESTOI back-end, train it with this strategy, compare it to naïve training and a range of existing non-data-driven predictors. The proposed training strategy and architecture results in higher performance overall and increased robustness to unseen paradigms.

In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. The main challenge of closed-domain QA is to develop a model that only requires small datasets for training since large-scale corpora may not be available. One approach is a flexible QA model that can adapt to different closed domains and train on their corpora. In this paper, we present a novel versatile reading comprehension style approach for closed-domain QA (called CA-AcdQA). The approach is based on pre-trained contextualized language models, Convolutional Neural Network (CNN), and a self-attention mechanism. The model captures the relevance between the question and context sentences at different levels of granularity by exploring the dependencies between the features extracted by the CNN. Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. The model can be tuned to different domains with a small training dataset for sentence-level QA. The approach is tested on four publicly-available closed-domain QA datasets: Tesla (person), California (region), EU-law (system), and COVID-QA (biomedical) against nine other QA approaches. Results show that the ALBERT model variant outperforms all approaches on all datasets with a significant increase in Exact Match and F1 score. Furthermore, for the Covid-19 QA in which the text is complicated and specialized, the model is improved considerably with additional biomedical training resources (an F1 increase of 15.9 over the next highest baseline).

A geostationary microwave sounder, capable of providing continuous monitoring of temperature, water vapor, clouds, precipitation, and wind in the presence of clouds and precipitation is now feasible. A design called the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR) has been developed at the Jet Propulsion Laboratory, and the required new technology has been developed and is sufficiently mature that a space mission can be initiated. GeoSTAR can be thought of as “AMSU in GEO,” i.e., it has capabilities in geostationary earth orbit (GEO) similar to those of microwave sounders currently operating in low earth orbit. Having such a capability in GEO will add tremendously to our ability to observe dynamic atmospheric phenomena, such as hurricanes and severe storms, monsoonal moisture flow, and atmospheric rivers. GeoSTAR will make measurements every 15 min or less instead of every 12 h and cover a large portion of the Earth continuously instead of with snapshots in a narrow swath. By tracking water vapor patterns, it is also possible to derive atmospheric wind speed and direction at altitudes from the surface to 10–15 km. All of this can be done regardless of cloud cover and weather conditions. During the latter half of 2020, a detailed study of GeoSTAR and its projected performance was undertaken as one of several such studies commissioned by the National Oceanic and Atmospheric Administration (NOAA) for the purpose of configuring NOAA's next generation of earth environmental satellite systems. We present a summary of our findings, including instrument characteristics, measurement accuracy and precision, and expected impact on weather prediction and applications.

Telemeetings such as audiovisual conferences or virtual meetings play an increasingly important role in our professional and private lives. For that reason, system developers and service providers will strive for an optimal experience for the user, while at the same time optimizing technical and financial resources. This leads to the discipline of Quality of Experience (QoE), an active field originating from the telecommunication and multimedia engineering domains, that strives for understanding, measuring, and designing the quality experience with multimedia technology. This paper provides the reader with an entry point to the large and still growing field of QoE of telemeetings, by taking a holistic perspective, considering both technical and non-technical aspects, and by focusing on current and near-future services. Addressing both researchers and practitioners, the paper first provides a comprehensive survey of factors and processes that contribute to the QoE of telemeetings, followed by an overview of relevant state-of-the-art methods for QoE assessment. To embed this knowledge into recent technology developments, the paper continues with an overview of current trends, focusing on the field of eXtended Reality (XR) applications for communication purposes. Given the complexity of telemeeting QoE and the current trends, new challenges for a QoE assessment of telemeetings are identified. To overcome these challenges, the paper presents a novel Profile Template for characterizing telemeetings from the holistic perspective endorsed in this paper.

We propose a basis splines-based based active contour method for the segmentation of lumen boundary and media adventitia boundary from transverse and longitudinal B-mode ultrasound images. Basis-spline has $M$ knots in the shape template and five free parameters i.e., a pair of center coordinates, scaling in the horizontal and vertical directions, and the rotation angle. The segmentation of the region of interest in ultrasound images is done by minimizing the local energy function using gradient descent technique. Further optimization is carried out using Green’s theorem. Automatic localization poses an equal importance as segmentation and is achieved using sum of absolute difference method. The result of experimental validation has been done on the Signal Processing Lab, Brno University’s database of transverse and longitudinal B-mode ultrasound images consisting of 971 and 84 images, respectively. We attained accurate segmentation of the lumen boundary from transverse and longitudinal B-mode ultrasound images with an accuracy of 99.68% and 96.98% and Dice index of 93.33% and 91.70%, respectively. In addition, we attained an accuracy of 99.29% with Dice index of 91.78% for the segmentation of media adventitia boundary from transverse B-mode ultrasound images, which is the highest among the previously proposed methods.

Connecting critical infrastructure assets to the network is absolutely essential for modern industries. In contrast to the apparent advantages, network connectivity exposes other infrastructure vulnerabilities that can be exploited by attackers. To protect the infrastructure, precise countermeasure identification is necessary. In this regard, the objective for the security officers is to identify the optimal set of countermeasures under a variety of budgetary restrictions. Our approach is based on the Meta Attack Language framework, which allows for convenient modelling of said infrastructures, as well as for automatic generation of attack graphs describing attacks against them. We formalize the problem of the selection of countermeasures in this context. The formalization makes it possible to deal with an arbitrary number of budgets, expressing available resources of both monetary and time-like nature, and to model numerous dependencies between countermeasures, including order dependencies, mutual exclusivity, and interdependent implementation costs. We propose a flexible and scalable algorithm for the problem. The whole methodology is validated in practice on realistic models.

Brain-computer interfaces (BCIs) can serve as a means for stroke rehabilitation, but low BCI performance can decrease agency (users’ perceived control), frustrate users and thereby hamper rehabilitation. In such rehabilitative tasks BCIs can implement fabricated input (preprogrammed positive feedback) that improve agency and frustration. Two substudies with healthy subjects and stroke patients investigated this potential through completion of a game and a simple task with: 1) 16 healthy subjects using motor imagery-based online BCI and 2) 13 stroke patients using a surrogate BCI system based on eye-blink detection through an eye-tracker to have a highly reliable input signal. Substudy 1 measured perceived control and frustration in four conditions: 1) unaltered BCI control, 2) 30% guaranteed positive feedback from fabricated input 3) 50% guaranteed negative feedback, and 4) 50% guaranteed negative feedback and 30% guaranteed positive feedback. In substudy 2, stroke patients had 50% control over outcomes and four conditions added from 0% to 50% positive feedback. In both substudies, positive feedback improved participants’ perceived control and reduced frustration with increasing improvements when the amount of positive fabricated input increased. The stroke patients did not react as much to the fabricated input as the healthy participants. Fabricated input can be concealed in both online and surrogate BCIs which can be used to improve perceived control and frustration in a game-based interaction and simple task. This suggests that BCI designers can exercise artistic freedom to create engaging motor imagery-based interactions of narrative-based games or simpler gamified interactions to facilitate improved training efforts.

We propose and investigate theoretically the use of bi-directional propagation of light in silica integrated coupled waveguide structures for linear optical processing of fiber-coupled photons. We show that the class of linear operations that can be implemented in such a system is given by symmetric and unitary matrices. We present how an arbitrary 4 × 4 coupler of this form can be realized by a linear sequence of more fundamental 2 × 2 couplers and single-waveguide phase shifters and discuss in detail the implementation in a silica integrated platform utilizing direct UV-written waveguides and long- and short-period grating couplers with tilted gratings for optimized coupling efficiency.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industry 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators, but new use cases such as moving robots or drones demand a higher flexibility on communication services. Wireless technologies, especially 5th generation wireless communication systems (5G) are best suited to address these new requirements. Furthermore, this facilitates the renewal of brownfield deployments to enable a smooth migration to Industry 4.0. This paper presents results from the Tactile Internet 4.0 (TACNET 4.0) project and introduces a tailored architecture that is focused on the communication needs given by representative Industry 4.0 use cases while ensuring parallel compliance to latest developments in relevant standardization.

A fast method to simultaneously calibrate multiple MEMS Magnetic Inertial Measurement Units (MIMUs) accurately in the field is needed in many application areas. The MEMS MIMUs require calibration of systematic errors of bias, sensitivity, non-orthogonality and misalignment, which vary with temperature and use. Even after calibration, the sensors undergo stochastic errors in static and dynamic conditions and thus uncertainty of output must also be modeled. We propose a method for easy and fast calibration of multiple MIMUs together, while mounted on a single platform. The precise alignment of sensors is not assumed. Our method calibrates both fixed array of MEMS MIMUs or many independent MIMUs simultaneously using kinematic constraints. The novelty of our approach is that the uncertainty of sensors output is also learned as part of our model. Compared with existing state-of-art methods, our algorithm gives more consistent readings of all MIMUs and our framework also predicts the associated uncertainty of the sensor output. The uncertainty prediction of individual sensors is particularly helpful in the sensor fusion.

We propose a deep learning based framework that learns data-driven temporal priors to perform 3D human pose estimation from six body worn Magnetic Inertial Measurement units sensors. Our work estimates 3D human pose with associated uncertainty from sparse body worn sensors. We derive and implement a 3D angle representation that eliminates yaw angle (or magnetometer dependence) and show that 3D human pose is still obtained from this reduced representation, but with enhanced uncertainty. We do not use kinematic acceleration as input and show that it improves the generalization to real sensor data from different subjects as well as accuracy. Our framework is based on Bi-directional recurrent autoencoder. A sliding window is used at inference time, instead of full sequence (offline mode). The major contribution of our research is that 3D human pose is predicted from sparse sensors with a well calibrated uncertainty which is correlated with ambiguity and actual errors. We have demonstrated our results on two real sensor datasets; DIP-IMU and Total capture and have come up with state-of-art accuracy. Our work confirms that the main limitation of sparse sensor based 3D human pose prediction is the lack of temporal priors. Therefore fine-tuning on a small synthetic training set of target domain, improves the accuracy.

Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.

With recent advances in both AI and IoT capabilities, it is possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, masked faces, suspicious objects and traffic. This system could transform surveillance cameras from passive sentries into active observers which would help in preventing a possible mass shooting in a school, stadium or mall. In this paper, we present a prototype implementation of such systems, Hawk-Eye, an AI-powered threat detector for smart surveillance cameras. Hawk-Eye can be deployed on centralized servers hosted in the cloud, as well as locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images to take place on-site, which reduces the communication overheads and enables swift security actions. At the cloud side, we built a Mask R-CNN model that can detect suspicious objects in an image captured by a camera at the edge. The model can generate a high-quality segmentation mask for each object instance in the image, along with the confidence percentage and classification time. The camera side used a Raspberry Pi 3 device, Intel Neural Compute Stick 2 (NCS 2), and Logitech C920 webcam. At the camera side, we built a CNN model that can consume a stream of images directly from an on-site webcam, classify them, and displays the results to the user via a GUI-friendly interface. A motion detection module is developed to capture images automatically from the video when a new motion is detected. Finally, we evaluated our system using various performance metrics such as classification time and accuracy. Our experimental results showed an average overall prediction accuracy of 94% on our dataset.

In rugged terrain, topography substantially influences the illumination and observation geometry, and thus, the bidirectional reflectance distribution function (BRDF) of a surface. While this problem has been known and investigated for spaceborne optical data since the 1980s, it has led to several well-known topographic correction methods. To date, the methods developed for spaceborne data were equivalently applied to airborne data with distinctly higher spatial resolution, illumination/observation angle configurations and finally (instantaneous) field of view (FOV). On the one hand, this article evaluates, whether such a transfer of methods from spaceborne to airborne acquisitions is reasonable. On the other hand, a new Lambertian/statistical-empirical correction method is introduced. While in the spaceborne case the Modified Minnaert (MM) and the Statistical-Empirical (SE) methods performed best, MM led to the statistically and visually best compromise for the airborne data. Our results suggest further that with a higher spatial resolution various effects come into play (FOV widening; changing the fraction of geometric, volumetric and isotropic scattering, etc.), compromising previously successful methods, such as the SE method.

Hand-eye calibration enables proper perception of the environment in which a vision guided robot operates. Additionally, it enables the mapping of the scene in the robots frame. Proper hand-eye calibration is crucial when sub-millimetre perceptual accuracy is needed. For example, in robot assisted surgery, a poorly calibrated robot would cause damage to surrounding vital tissues and organs, endangering the life of a patient. A lot of research has gone into ways of accurately calibrating the hand-eye system of a robot with different levels of success, challenges, resource requirements and complexities. As such, academics and industrial practitioners are faced with the challenge of choosing which algorithm meets the implementation requirements based on the identified constraints. This review aims to give a general overview of the strengths and weaknesses of different hand-eye calibration algorithms available to academics and industrial practitioners to make an informed design decision, as well as incite possible areas of research based on the identified challenges. We also discuss different calibration targets, which is an important part of the calibration process that is often overlooked in the design process.

As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional constant false alarm rate (CFAR) detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in nonlinear target detection, In this article, we propose a novel high performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms ordered statistics CFAR (OS-CFAR) (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multitarget CFAR detectors and show an improvement of 16% in probability of detection compared to censored harmonic averaging CFAR, with even larger improvements compared to both outlier-robust CFAR and truncated statistics log-normal CFAR in our particular indoor scenario. To the best of authors’ knowledge, this article improves the state-of-the-art for high-performance yet low-complexity radar detection in critical indoor sensing applications.

As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation.

As each quantum algorithm requires a specific initial quantum state, quantum state preparation is an important task in quantum computing. The preparation of quantum states is performed by a quantum circuit consisting of controlled-NOT (CNOT) and single-qubit gates. Known algorithms to prepare arbitrary $n$-qubit quantum states create quantum circuits in $O(2^n)$ runtime and use $O(2^n)$ CNOTs, which are more expensive than single-qubit gates in NISQ architectures. To reduce runtime and the number of CNOTs, we simplify the problem by considering an important family of quantum states, which are uniform quantum states (UQSs). We map UQSs to Boolean functions and propose a UQS preparation (UQSP) method. Preparing UQSs using Boolean functions allows us to utilize different representations of Boolean functions. We utilize decision diagrams to reduce runtime and enable preparation for a larger number of qubits where the state-of-the-art methods are not applicable. To further reduce the number of CNOTs, we utilize variable reordering and functional dependencies among the variables. Our state preparation method requires an exponential number of CNOTs in the worst case but it reduces CNOTs significantly for practical benchmarks. Moreover, our method generates an exact representation of quantum states without using free-qubits. We compare our algorithm with QisKit. The comparison shows that our UQSP method is capable to reduce the average number of CNOTs by 75.31% for the practical benchmarks. The runtime is almost reduced by a factor of 2.

Reconfigurable intelligent surfaces (RISs) have the potential of realizing the emerging concept of smart radio environments by leveraging the unique properties of metamaterials and large arrays of inexpensive antennas. In this article, we discuss the potential applications of RISs in wireless networks that operate at high-frequency bands, e.g., millimeter wave (30-100 GHz) and sub-millimeter wave (greater than 100 GHz) frequencies. When used in wireless networks, RISs may operate in a manner similar to relays. The present paper, therefore, elaborates on the key differences and similarities between RISs that are configured to operate as anomalous reflectors and relays. In particular, we illustrate numerical results that highlight the spectral efficiency gains of RISs when their size is sufficiently large as compared with the wavelength of the radio waves. In addition, we discuss key open issues that need to be addressed for unlocking the potential benefits of RISs for application to wireless communications and networks.

In obstetrics, ultrasound is used for assessment of fetal development during pregnancy. The images generated by ultrasound are used to obtain measurements of fetal head length, body size, and the analysis of fetal movements, to identify and prevent the onset of congenital disease. This work presents the development of a new method for the segmentation of two-dimensional ultrasound images of fetal skulls based on a V-Net architecture called Fully Convolutional Neural Network - Combination (VNet-c). We created a new combination of strategies using a 3D V-Net as base, such as pre-processing, use of Batch Normalization and Dropout, and evaluation of distinct activation layers, activation function, data augmentation, loss function, and network depth. The computational results reveal the feasibility of the proposal in the correct segmentation of fetal skulls and head circumference measurements, reaching up to 97.91% of correctness, overcoming states-of-the-art methods.

In recent years, Boolean methods in logic synthesis have been drawing the attention of EDA researchers due to the continuous push to advance quality of results. Boolean methods require high computational cost, as they rely on complete functional properties of a logic circuit (e.g., don't cares), but usually result in better optimization. In particular, Boolean resubstitution is considered one of the most powerful Boolean methods in logic synthesis. In this paper, we present three novel Boolean resubstitution algorithms designed to be scalable and runtime-effective in a modern synthesis flow. They make use of circuit partitioning techniques and Boolean filtering to be fast and computationally tractable. We also discuss different data structures and reasoning engines, namely truth tables, binary decision diagrams, and satisfiability, that are required to gather don't cares and functional information. As the choice of the engine determines the scalability of Boolean resubstitution we present different scenarios in which the Boolean methods are best driven by one or the other of these. We have implemented the presented resubstitution techniques together with state-of-the-art methods in an industrial logic optimization engine to create a novel resynthesis flow. Our global resynthesis flow achieves significant synthesis results: Within the EPFL synthesis competition, we improve the best-known area results when mapped into LUT-6; when embedded in a commercial EDA flow, the new Boolean resynthesis flow results in 3.12% combinational area savings and 1.34% WNS reduction after physical implementation, at contained (w.r.t. the time of the entire flow) runtime cost.

Inchworms use their morphology and evolved behaviors to crawl and climb various complex surfaces. This has inspired the development of different robots that can demonstrate similar capabilities for various applications such as the inspection of a complex environment. One of the key challenges in designing these robots is to enable them to be practically deployable with a compact design for providing continuous adaptability to a complex terrain such as an outer-pipe surface. Taking this into consideration, we present a new design for an inchworm-inspired crawling robot (iCrawl). The 5 DOF robot relies on two legs; each with an electromagnetic foot, in order to crawl on the metal pipe surfaces. The robot uses a passive foot-cap underneath an electromagnetic foot, enabling it to be a versatile pipe-crawler. The foot-cap design is an abstraction of the leg posture in an inchworm adapting to a round surface. The proposed foot-caps give the robot adaptability and stability for crawling on metal pipes of various curvatures. A state-machine based controller was developed to produce the required motor signals for the two inchworm-inspired crawling gaits: i) the step gait, and ii) the sliding gait. Both gaits were tested on the robot, eventually leading to it effectively crawling on the pipes and flat surfaces, climbing a metal wall and a pipe, and succeeding in obstacle avoidance during crawling. Experimental results also show that the robot has the ability to crawl on the metal pipes of various curvatures using the foot-caps and an appropriate gait. The robot can be used as a new robotic solution to assist close inspection outside the pipelines, thus minimizing downtime in the oil and gas industry.

Pressure on the biodiversity of ecosystems along many rivers is growing continuously due to the increasing number of hydropower facilities regulating downstream flow and sediment regimes. Despite a thorough understanding of the short-term processes and interactions at this hydro-biosphere interface, long-term analyses of the impacts on floodplain dynamics are lacking. We used interannual Landsat 4, 5, 7, and 8 time series to analyze the effects of hydrological events on floodplain vegetation in four mountainous floodplains in the Swiss Alps. Using a spectral mixture analysis approach, we demonstrate that the floodplain vegetation dynamics of mountainous rivers can be recovered at a spatial resolution of 30 m. Our results suggest that interactions between floods and floodplain vegetation are complex and not exclusively related to flood magnitude. Of the four reaches analyzed, only data gathered along the submountainous reach with a quasi-natural flow regime show a clear link between remotely sensed vegetation indices and floods. In addition, our 29-year time series shows a continuous upward trend in vegetation indices along the floodplains, strongest in the reaches affected by hydropower facilities. The approach presented in this study can be easily replicated in other mountain ranges by providing available flow data to verify the impact of hydropower on floodplain vegetation dynamics.