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Methanol concentration as an aging indicator can assess cellulose aging and estimate cellulose degree of polymerization (DP). Despite the advantages of methanol (MeOH), its major disadvantage is that its concentration in the oil decreases after some time. This point is called the MeOH peak in this article. Therefore, using this indicator when MeOH concentration is reduced leads to incorrect results. When using methanol, the main problem is that whenever the methanol concentration in a transformer is measured, it cannot be identified that the measured concentration belongs to the left or right side of the MeOH peak. If the measured concentration belongs to the right side of the MeOH peak, the estimated DP is incorrect. In this article, an empirical equation is established to estimate the DP value as a function of methanol concentration when methanol concentration belongs to the left side of the MeOH peak. Then, a new index called methanol peak detector index (MPDI) composed of a 2-furfural/MeOH ratio and normalized DP is presented to ensure the validity of the calculated DP value. If it is found that the measured concentration belongs to the left side of the MeOH peak, then the estimated DP by the presented equation is valid. Finally, the application of this method is verified by applying it to measurement data samples indicating the validity of the method. [ABSTRACT FROM AUTHOR]

Over the last decade, the rapid development in health care industry and security has driven innovations in the field of wearable electronics and smart sensors. This report demonstrates the fabrication of molybdenum diselenide (MoSe2) by a facile, low-cost and scalable hydrothermal synthesis method followed by a vacuum filter deposition on cellulose paper. The fabricated device is utilized as a pressure and strain sensor. The fabricated device is passivated using polydimethylsiloxane (PDMS), and also, PDMS film enhances the flexibility of the device. The calculated sensitivity of pressure sensor is ~0.3 kPa−1, which is comparable and relatively better than the pressure sensor fabricated using sophisticated techniques. Furthermore, the device exhibited significant of merit for strain sensing with a gauge factor of ~6.84. The fabricated device showed excellent resilience in 500 cycle bending tests, which promises the robustness of the device. As an application, the fabricated sensor is used to develop a wireless keyboard by integrating the sensor on fingertips of an individual and a real-time human motion monitoring application in which the data are transmitted wirelessly to a smartphone with a dedicated android application. The successful fabrication of MoSe2/paper-based cost-effective, flexible, and biodegradable pressure-strain sensor shows tremendous applications in future intelligent user interface systems, human security, and personal health care monitoring. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of inkjet printing of circuit and microwave structures on paper-based substrates is investigated for the first time in the implementation of a complete low-cost wireless platform for sensors. First, the system-level design of the module including the amplifier characterization were carried out to ensure optimum performance of the sensor modules in the UHF bands used in RF identification communication. These results were then used to design two different antenna structures, which are printed on paper along with their respective circuit layouts using inket-printing technology. Different techniques were investigated for the assembly of circuit components on the silver printed layouts. Finally, wireless link measurements on the assembled prototypes verified the good performance on the wireless and sensing sides. [ABSTRACT FROM AUTHOR]

Sheet tension control is crucial in the paper winding process in order to get properly formed shipping roll(s) produced in the winder. Typically, the tension loop uses load cells as tension sensors, which are expensive and require frequent maintenance and calibration. This paper develops two new tension estimators for a two-drum electric-braking-generator production winder, based on unwinding and rewinding variables, respectively. The estimators consider all the essential dynamic, friction, and inertial variation effects. Both estimators are evaluated in typical trapezoidal winder operating cycles. Multiple sensitivity analyses show that the proposed estimators are robust to normal parameter variations, and give stable and precise sheet tension control without the use of load cell tension loops. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates two robust sheet tension estimation algorithms based on unwind and rewind variables, respectively. The proper sheet tension estimation is guaranteed by a continuous monitoring of the differences between the actual estimated values and the values predicted from the last calibration of the estimation parameters. The evaluation was performed with the aid of a developed winder emulator that allowed the offline testing in a real-time field environment, including the initial setting of the estimation constants, and evaluation of both four- and six-shipping-roll cycles. The sheet tension estimation results confirm the precise and stable operation of the proposed robust estimation algorithms, in tests conducted with signals from a winder model, and with tests involving actual field signals from an operational winder. [ABSTRACT FROM AUTHOR]

Recently developed monitoring-systems show the need as also the possibilities for online- and offline-onsite-diagnostics. The limit in sensitivity for electrical pd-measurements according to IEC 60270 which is reached using filters for sinusoidal disturbances and compensation for corona impulses is still not satisfactory. The acoustic partial discharge measurement (pd-measurement) is also a well known and reliable method often used by transformer manufacturers in testing. In laboratory setups a gain in sensitivity of the acoustic in comparison to the electrical pd measurements could be established. This is reached by adapting the sensors to the propagation path, a modern but moderately priced acquisition hardware and the introduction of new signal processing algorithms. The developed sophisticated measuring, systems show remarkable results in online acoustic pd-measurements onsite. [ABSTRACT FROM AUTHOR]

PD measurements in dielectric liquids have been normally performed in the past only using conventional narrow bandwidth detectors. The more recent use of ultra-wide bandwidth instruments has permitted to support once again the thesis that the PDs in oils are the consequence of the streamer (vapor phase) formation and its development. In the paper simultaneous PD measurements in insulating liquids using both narrow and ultra-wide bandwidth detectors are presented. Whilst PD detection with narrow bandwidth instruments gives only integrated signals, the use of ultra-wide bandwidth techniques permits to have much more precise information on PD phenomena, which appear to be in the form of pulses (bursts) of ascending magnitude as the bubble within the liquid expands in volume. As reported in the paper, the use of the ultra-wide bandwidth instruments permits also to distinguish the PD pulses occurring in regular voids of oil-paper insulations and PD pulses formed directly in the liquids as consequence of the streamer development. Also in the paper the meaning of the PDIV in oils is discussed, showing its dependence with the definition used, the unpredictable level of the background noise as well as the randomness of the PD initiation process. [ABSTRACT FROM AUTHOR]

In recent years, triboelectric nanogenerators (TENGs) have been increasingly attracting attention owing to the ability for converting multiple mechanical energy into electricity. In this paper, we first propose a multifunctional conductive copper tape-based TENG (CCT-TENG) that is constructed completely using one piece of conductive copper tape. The triboelectric pairs, supporting structure, and conductive electrode were composed using bottom paper, silicone oil, and copper foil, all of which came from one roll of conductive copper tape. The approximate output power density of the CCT-TENG reached $240.1~\mu \text{W}$ /cm2. By spraying the aqueous solution of LiCl onto the surface of the bottom paper, a CCT-TENG incorporating LiCl was fabricated and used as a self-powered active sensor to detect the relative humidity (RH) of the environment. The obtained results revealed that the humidity sensor had good humidity response, response linearity, and reversibility. In addition, the RH was reflected by the brightness of light-emitting diodes (LEDs) that was driven by the humidity sensor. This paper realizes a promising, affordable, and portable integration of power supply systems and sensing systems, which can promote the application of TENG in the field of electronic detection device and environment monitoring. [ABSTRACT FROM AUTHOR]

Discusses the application of space-time adaptive processing (STAP) and related adaptive array techniques in improving sensor performance by signal diversity. History of adaptive array research; Factors considered in STAP analysis.

The article presents information on the 2005 Communications Society and Information Theory Society Joint Paper Award, which is given annually to an outstanding paper that was published in any of the two societies' publications during the preceding year. This year the award was given to researchers Nihar Jindal, Sriram Vishwanath, and Andrea Goldsmith for their paper "On the Duality of Gaussian Multiple-Access and Broadcast Channels," which appeared in the 2004 issue of the journal "IEEE Transaction on Information Theory." Jindal has done his B.S. in electrical engineering and computer science from the University of California in Berkeley, in 1999. His research work is in the field of the applications of communication and information theory to wireless communication, with a particular focus on multiple-antenna multi-user channels, dynamic resource allocation, sensor and ad hoc networks. Vishwanath's is currently working as an assistant professor in the Department of Electrical and Computer Engineering at the University of Texas in Austin. Goldsmith works as an assistant professor of electric engineering at Stanford University. She has done research in the field of wireless communication, information theory and adaptive resource allocation in wireless networks.

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN. [ABSTRACT FROM AUTHOR]

Since the introduction of adaptive steganography, most of the recent research works seek at designing cost functions that are evaluated against steganalysis methods. While those approaches have been successful, they rely on intuitive principles and ad-hoc costs associated with each pixel or Discrete Cosine Transform (DCT) coefficient. Beyond the empirical assessments, the insights one can get from such approaches are very limited. On the opposite, this paper presents an original method for steganography in JPEG images that exploits a statistical model of the DCT coefficients. Within the framework of hypothesis testing theory, we use a statistical model of covers to derive the analytical expression of the most powerful detector. The objective of the steganographer is to minimize the statistical performance of this “omniscient detector” which represents a “worst-case” scenario for security. This paper shows how this method allows designing effective steganography, in terms of both security and computational complexity, in the two main use cases: when having only one single JPEG image and when the uncompressed image is available, case also known as Side-Informed (SI). A wide range of numerical comparisons shows that the proposed method outperforms the current state-of-the-art especially against the latest and most accurate steganalysis approaches based on Deep Learning. [ABSTRACT FROM AUTHOR]

In array processing, sparse arrays are capable of resolving $\mathcal {O}(N^2)$ uncorrelated sources with $N$ sensors. Sparse arrays have this property because they possess uniform linear array (ULA) segments of size $\mathcal {O}(N^2)$ in the difference coarray, defined as the differences between sensor locations. However, the coarray structure of sparse arrays is susceptible to sensor failures and the reliability of sparse arrays remains a significant but challenging topic for investigation. In the companion paper, a theory of the $k$ -essential family, the $k$ -fragility, and the $k$ -essential Sperner family were presented not only to characterize the patterns of $k$ faulty sensors that shrink the difference coarray, but also to provide a number of insights into the robustness of arrays. This paper derives closed-form characterizations of the $k$ -essential Sperner family for several commonly used array geometries, such as ULA, minimum redundancy arrays (MRA), minimum holes arrays (MHA), Cantor arrays, nested arrays, and coprime arrays. These results lead to many insights into the relative importance of each sensor, the robustness of these arrays, and the DOA estimation performance in the presence of sensor failure. Broadly speaking, ULAs are more robust than coprime arrays, while coprime arrays are more robust than maximally economic sparse arrays, such as MRA, MHA, Cantor arrays, and nested arrays. [ABSTRACT FROM AUTHOR]

This paper proposes a metamaterial inspired radio frequency touchpad sensor system based on two split ring resonators (SRRs) realized on a Rogers/Duroid 5870 with 2.33 dielectric constant = 2.33, loss tangent = 0.0012, and thickness = 0.8 mm. The touchpad consists of two SRRs resonating at $f_{1} = 2.74$ GHz and $f_{2} = 3.86$ GHz. When a finger is placed on SRR 1, $f_{1}$ changes from 2.74 to 2.57 GHz due to electromagnetic coupling between the SRR and the finger, and similarly, $f_{2}$ changes from 3.86 to 3.76 GHz. The proposed touchpad system can detect a finger wearing a glove, in contrast to capacitive touchpads utilized in contemporary smart phones. We demonstrated 56- and 49-MHz frequency shifts for $f_{1}$ and $f_{2}$ , respectively, with 1.66-mm total thickness (0.69-mm-thick photo paper and 0.97-mm-thick glass slide). [ABSTRACT FROM AUTHOR]

Techniques for retrieving cloud optical properties, i.e., the optical depths and particle size distributions, using atmospheric "window" channels in the visible and near-infrared spectral regions are well established. For partially transparent thin cirrus clouds, these "window" channels receive solar radiances scattered by the surface and lower level water clouds. Accurate retrieval of optical properties of thin cirrus clouds requires proper modeling of the effects from the surface and the lower level water clouds. In this paper, we describe a new concept using two strong water vapor absorption channels near 138 and 1.88 μm, together with one window channel, for remote sensing of cirrus optical properties. Both the 138- and 1.88-μm channels are highly sensitive in detecting the upper level cirrus clouds. Both channels receive little scattered solar radiances from the surface and lower level water clouds because of the strong water vapor absorption below cirrus. The 1.88-μm channel is quite sensitive to changes in ice particle size distributions, while the 138-μm channel is less sensitive. These properties allow for simultaneous retrievals of optical depths and particle size distributions of cirrus clouds with minimal contaminations from the surface and lower level water clouds. Preliminary tests of this new concept are made using hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer. The addition of a channel near 1.88 μm to future multichannel meteorological satellite sensors would improve our ability in global remote sensing of cirrus optical properties. [ABSTRACT FROM AUTHOR]

The 35th IEEE International Instrumentation and Measurement Technical Conference (I2MTC) was held in Houston, TX, USA, on May 22–25, 2018. I2MTC is the flagship conference of the IEEE Instrumentation and Measurement Society held every year since 1986. It is dedicated to advances in measurement methodologies, measurement systems, instrumentation, and sensors in all areas of science and technology. The I2MTC is proposed as a catalyst to promote interactions between industry and academia; a wide spectrum of academic research results is presented, with potential practical applications in current industrial technology, as well as industry and application-driven developments. [ABSTRACT FROM AUTHOR]

As you may know, the M Barry Carlton Award is bestowed on the authors of the best paper published in the IEEE Transactions of Aerospace and Electronic Systems (T-AES) for a given calendar year. The winners for the 2011 Award were announced in the February issue earlier this year. It is now my privilege to announce that the 2012 M Barry Carlton award winners are Kevin (Jim) Sangston, Fulvio Gini, and Maria (Sabrina) Greco for their paper entitled, "Coherent Radar Target Detection in Heavy-Tailed Compound-Gaussian Clutter." [ABSTRACT FROM AUTHOR]

Many applications of hyperspectral remote sensing involve automatic detection of solid targets. The design and applicability of such systems depend on their detection performance under various deployment scenarios; thus, it is useful to develop an accurate and realistic performance model. In this paper, we present a hyperspectral target detection performance prediction model for the common matched filter and normalized matched filter detection algorithms. A statistical model for hyperspectral data is discussed, which includes a replacement model for target pixels and a finite mixture of $t$ -elliptically contoured distributions for background materials. For this general input model, analytic forms for detector output distributions are not always available. The main contribution of this paper is to develop an efficient and robust performance simulation technique using Monte Carlo (MC) with importance sampling. Compared to standard MC, our simulation technique requires a substantially smaller sample size to obtain accurate performance estimates at low false alarm rates. The proposed technique is useful for predicting detection performance over a wide range of input models when analytic solutions are unavailable. [ABSTRACT FROM AUTHOR]

The measurement of water inside a transformer is essential to maintain its reliable operation, as the water content can increase as the transformer ages. This measurement is frequently performed by a utility as a part of its periodic maintenance activities. In this paper, the application of optical fiber sensors as an online method to measure water content of paper inside an operating transformer is presented. There are several advantages of using these sensors, including their size, lightweight, flexibility, multiplexing, no-electromagnetic interference, etc. A unique benefit of fiber optic technology is that sensors can be embedded into the winding structure and operate in areas of high electric field strength. For this work, fiber optic water and temperature sensors are investigated by comparing their measurements to a commercial water activity probe immersed in oil. The study found that the determination of water content by fiber optic sensors compared closely with other techniques, which lead to the conclusion that they can be an effective technology for water measurement in an operational transformer. [ABSTRACT FROM AUTHOR]

Using the barometer for height estimation often requires the use of external reference to correct biases in measurement. These biases are often caused by the change of the ambient pressure environment. The barometric height estimation is especially challenging in tactical and rescue applications where high temperatures or sudden large pressure shocks can change the pressure rapidly. We assess the suitability of barometers for infrastructure-free navigation in tactical applications. First, this paper investigates the effect of transition in seamless indoor/outdoor navigation. Second, we measure the effects of pressure shocks, caused by explosions or firearms, on low-cost and lightweight micro-electromechanical barometers to ensure that the sensors are capable of operating under these conditions. Finally, we investigate the use of sonar measurement to estimate the vertical speed as an alternative to the reference barometer for infrastructure-free navigation. The fusion of barometer and sonar achieved on average 0.46-m root-mean-square error (RMSE) while simple barometric height estimation had a RMSE of 0.65 m. The fusion method had no errors over 1.5 m during the test. This accuracy is generally sufficient to find the correct floor level which is crucial for tactical situational awareness. The goal of this paper is to develop the methods for seamless indoor/outdoor navigation, and therefore the most important result of this paper is that the error caused when transitioning between outdoor and indoor environments is visibly reduced. [ABSTRACT FROM AUTHOR]

This paper investigates the noncoherent detection in a two-way relay channel operated with physical-layer network coding (PNC), assuming FSK modulation and short-packet transmissions. For noncoherent detection, the detector has access to the magnitude but not the phase of the received signal. For conventional communication in which a receiver receives the signal from a transmitter only, the phase does not affect the magnitude, and hence the performance of the noncoherent detector is independent of the phase. PNC, on the other hand, is a multiuser system in which a receiver receives signals from multiple transmitters simultaneously. The relative phase of the signals from different transmitters affects the received signal magnitude through constructive-destructive interference. In particular, for good performance, the noncoherent detector of a multiuser system such as PNC must take into account the influence of the relative phase on the signal magnitude. Building on this observation, this paper delves into the fundamentals of PNC noncoherent detector design. To avoid excessive overhead, we assume a set-up in which the short packets in the PNC system do not have preambles. We show how the relative phase can be deduced directly from the magnitudes of the received data symbols, and that the knowledge of the relative phase thus deduced can in turn be used to enhance performance of noncoherent detection. Our overall detector design consists of two components: 1) a channel gains estimator that estimates channel gains without preambles; and 2) a detector that builds on top of the estimated channel gains to jointly estimate relative phase and detect data using a belief propagation algorithm. Numerical results show that our detector performs nearly as well as a “fictitious” optimal detector that has perfect knowledge of the channel gains and relative phase. Although this paper focuses on PNC with FSK modulation, we believe that the insight of this paper applies generally to noncoherent detection in other multiuser systems with other modulations. Specifically, our insight is that the relative phase of overlapped signals affects the signal magnitude in multiuser systems, but fortunately the relative phase can be deduced from the magnitudes and this knowledge can be used to improve the detection performance. [ABSTRACT FROM AUTHOR]

The future of autonomous driving is slowly approaching, but there are still many steps to take before it can become a reality. It is crucial to pay attention to road infrastructure, because without it, intelligent vehicles will not be able to operate reliably, and it will never be possible to dispense of driver’s control. This paper presents the work carried out for the detection of road markings damage using computer vision techniques. This is a complex task for which there are currently not many papers and large image sets in the literature. This study uses images from the public Road Damage Detection dataset for the D44 defect and also provides 971 new labelled images for Spanish roads. For this purpose, three detectors based on deep learning architectures (Faster RCNN, SDD and EfficientDet) have been used and single-source and mixed-source models have been studied to find the model that best fits the target images. Finally, F1-score values reaching 0.929 and 0.934 have been obtained for Japanese and Spanish images respectively which improve the state-of-the-art results by 25%. It can be concluded that the results of this study are promising, although the collection of many more images will be necessary for the scientific community to continue advancing in the future in this field of research. [ABSTRACT FROM AUTHOR]

Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept. [ABSTRACT FROM AUTHOR]

In this paper, we design two efficient quadrature amplitude modulation (QAM) signal detectors for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). The content of the paper is summarized as follows: first, we transform the maximum-likelihood detection model to a non-convex sharing optimization problem for massive MIMO-QAM systems, where a high-order QAM constellation is decomposed to a sum of multiple binary variables, integer constraints are relaxed to box constraints, and quadratic penalty functions are added to the objective function to result in a favorable integer solution; second, a customized ADMM algorithm, called PS-ADMM, is presented to solve the formulated non-convex optimization problem. In the implementation, all variables in each vector can be solved analytically and in parallel; and third, in order to solve the penalty-sharing distributively, we improve the proposed PS-ADMM algorithm to a distributed one, named DPS-ADMM. In the end, performance analyses of the proposed two algorithms, including convergence properties and computational cost, are provided. Simulation results demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

With the development of Cyber-Physical Systems (CPSs), many industrial facilities have realized remote control and monitoring. However, the widespread of CPSs has brought new issues and challenges in terms of security. Attackers can exploit vulnerabilities induced by network communication, tamper with transmitted data, and cause serious accidents through carefully designed covert attacks. This paper proposes a residual-driven comprehensive defense scheme based on the coprime factorization technique to address the threat posed by concealed CPS attacks. The novel scheme protects CPS from stealth cyber-physical attacks through secure transmission and attack detection. In particular, a secure transmission method is first introduced to prevent information leakage from the source. The pivotal idea is to convert confidential transmission control and measurement signals into non-essential filtered residual signals. It contributes to the reduction of information leakage and helps reduce the risks of stealth attacks. Then, under the same residual-driven framework, a stealth attack detection approach is put forward. It can eliminate false alarms caused by system faults, and therefore, achieve superior efficacy in detection accuracy under stealth attacks. Finally, simulation research is conducted on the F-404 engine to verify the effectiveness and performance of the proposed scheme and approach. [ABSTRACT FROM AUTHOR]

This paper proposes a condition-based maintenance (CBM) policy for a deteriorating system whose state is monitored by a degraded sensor. In the literature of CBM, it is commonly assumed that inspection of system state is perfect or subject to measurement error. The health condition of the sensor, which is dedicated to inspect the system state, is completely ignored during system operation. However, due to the varying operation environment and aging effect, the sensor itself will suffer a degradation process and its performance deteriorates with time. In the presence of sensor degradation, the Kalman filter is employed in this paper to progressively estimate the system and the sensor state. Since the estimation of system state is subject to uncertainty, maintenance solely based on the estimated state will lead to a suboptimal solution. Instead, predictive reliability is used as a criterion for maintenance decision-making, which is able to incorporate the effect of estimation uncertainty. Preventive replacement is implemented when the estimated system reliability at inspection hits a specific threshold, which is obtained by minimizing the long-run maintenance cost rate. An example of wastewater treatment plant is used to illustrate the effectiveness of the proposed maintenance policy. It can be concluded through our research that: 1) disregarding the sensor degradation while it exists will significantly increase the maintenance cost and 2) the negative impact of sensor degradation can be diminished via proper inspection and filtering methods. Note to Practitioners—This paper was motivated by the observation of sensor degradation in wastewater treatment plants but the developed approach also applies to other systems such as manufacturing systems, chemical plants, and pharmaceutical factories, where sensors are dedicated to a long-time operation in a harsh environment. This paper investigates the impact of sensor degradation on CBM and suggests that the effect of sensor degradation should be carefully addressed while making maintenance decisions. Otherwise, it will lead to a suboptimal maintenance decision and increase the operating cost. An optimal maintenance decision, which contains the optimal inspection interval and reliability threshold, is achieved via minimizing the long-run cost rate. In the presence of measurement noise and intrinsic uncertainty from degradation, a stochastic filtering approach is employed to estimate the system and sensor state. Based on the estimated states and the calculated reliability, a dynamic maintenance decision is obtained at each inspection. This paper can be further extended considering non-Gaussian noise and alternative degradation processes. [ABSTRACT FROM AUTHOR]

Recent cybersecurity incidents such as Stuxnet and Irongate alert us to the threats faced by critical cyber-physical systems. These attacks compromise the control signals to push the system to unsafe regions and meanwhile, inject fake sensor measurements to cover the ongoing attack. Detecting these Stuxnet-like (SL) attacks still remains an open research issue. This paper analyzes the taxonomy, construction, and implication of SL attacks in CPS control loops. We propose to apply the moving target defense (MTD) approach that actively changes the system configuration to detect SL attacks, since these attacks are generally constructed based on the knowledge about the system’s configuration. We analyze the basic conditions for MTD to be successful. Finally, as a case study, we apply MTD for the secondary voltage control of power grids and present simulation results based on the IEEE 39-bus test system under realistic settings. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to extract spectral information of radiated THz signals to enable a fully integrated broadband THz spectroscope in silicon. A classical down-conversion spectrum analysis architecture requires on-chip generation of LO signals covering GHz-THz frequencies to analyze an incident spectrum covering that range. This paper presents a method to exploit the interaction between the front-end antenna and the incident signal to extract spectral information eliminating the need for extremely wideband LO generation and the entire receiver architecture following the antenna. The central premise is that the incident THz signal excites a spectrum-dependent current distribution on the antenna surface and this work presents a method to measure and then estimate the incident spectrum from the impressed current distribution on an on-chip antenna. The chip is implemented in 0.13~\mu \text m SiGe BiCMOS technology and measures 2.6 mm $\times 1.9$ mm. Measurement results are presented for various incident spectra between 40–330 GHz. In addition, the paper presents a method for extracting time-domain information by exploiting the variable nonlinearities of the integrated detectors. By modifying a classical single-port antenna into a 2D multi-port scatterer, the paper presents a synthesizer-free THz spectroscope which consists of an integrated scatterer and multitude of low-power sensors capable of sub-wavelength measurement of near-field interactions which are exploited for spectral estimation. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber-security problem for autonomous vehicles under sensor attacks. In particular, a model-based framework is proposed which can detect sensor attacks and identify their sources in order to achieve the secure localization of self-driving vehicles. To ensure robustness of the vehicle against cyber-attacks, sensor redundancy is introduced, that is to deploy multiple sensors, each of which provides real-time pose observations of the vehicle. A bank of attack detectors is developed to capture anomalies in each sensor measurement, which is a combination of an extended Kalman filter (EKF) and a cumulative sum (CUSUM) discriminator. EKFs are employed to estimate the vehicle position and orientation recursively, while each CUSUM discriminator is designed to analyze the residual generated by its combined EKF to detect the possible deviation of the sensor measurement from the expected pose derived according to the mathematical model of the vehicle. To monitor the inconsistency amongst multiple sensor measurements, an auxiliary detector is introduced which fuses observations from multiple sensors. Based on the results of all the detectors, a rule-based isolation scheme is developed to identify the source anomalous sensor. The effectiveness of our proposed framework has been demonstrated on real vehicle data. [ABSTRACT FROM AUTHOR]

It has been a widely growing interest in using silicon carbide (SiC) in high-power electronic devices. Yet, SiC wafers may contain killer defects that could reduce fabrication yield and make the device fall into unexpected failures. To prevent these failures from happening, it is very important to develop inspection tools that can detect, characterize and locate these defects in a non-invasive way. Current inspection techniques such as Dark Field or Bright field microscopy are effectively able to visualize most such defects; however, there are some scenarios where the inspection becomes problematic or almost impossible, such as when the defects are too small or have low contrast or if the defects lie deep into the substrate. Thus, an alternative method is needed to face these challenges. In this paper, we demonstrate the application of coherent Fourier scatterometry (CFS) as a complementary tool in addition to the conventional techniques to overcome different and problematic scenarios of killer defects inspection on SiC samples. Scanning electron microscopy (SEM) has been used to assess the same defects to validate the findings of CFS. Great consistency has been demonstrated in the comparison between the results obtained with CFS and SEM. [ABSTRACT FROM AUTHOR]

Spectre attacks can be catastrophic and widespread because they exploit common design flaws caused by the speculative capabilities in modern processors to leak sensitive data through side channels. Completely fixing the problem would require a redesign of the architecture for transient execution or the implementation of a new design on re-configurable hardware. However, such fixes cannot be backported to old machines with fixed hardware design. Completely replacing those machines will take a long time. Moreover, existing software patches may cause significant performance overhead. This paper proposes to detect Spectre by monitoring deviations in microarchitectural events using hardware performance counters with promising accuracy above 90 percent under a variety of workload conditions. However, the attacker may attempt to evade detection by slowing down the attack or mimicking benign programs. This paper thus compares different evasion strategies quantitatively and demonstrates that it is possible for the attacker to avoid detection when operating the attacks at a lower speed while maintaining a reasonable attack success rate. Then, we show that, in order to resist evasion, the original detector must be enhanced by randomly switching between a set of detectors using different features and sampling periods so we can keep the detection accuracy above 80 percent. [ABSTRACT FROM AUTHOR]

This paper proposes an end-to-end trainable one-stage parking slot detection method for around view monitor (AVM) images. The proposed method simultaneously acquires global information (entrance, type, and occupancy of parking slot) and local information (location and orientation of junction) by using a convolutional neural network (CNN), and integrates them to detect parking slots with their properties. This method divides an AVM image into a grid and performs a CNN-based feature extraction. For each cell of the grid, the global and local information of the parking slot is obtained by applying convolution filters to the extracted feature map. Final detection results are produced by integrating the global and local information of the parking slot through non-maximum suppression (NMS). Since the proposed method obtains most of the information of the parking slot using a fully convolutional network without a region proposal stage, it is an end-to-end trainable one-stage detector. In experiments, this method was quantitatively evaluated using the public dataset (PS2.0) and outperforms previous methods by showing both recall and precision of 99.77%, type classification accuracy of 100%, and occupancy classification accuracy of 99.31% while processing 60 frames per second. [ABSTRACT FROM AUTHOR]

Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations. [ABSTRACT FROM AUTHOR]

This paper presents the development of a wake-up receiver (WuRX) at nanowatt power levels for event-driven applications. This paper improves the state of the art, obtaining higher sensitivity than previous work in the 151.8- and 433-bands, low-power operation, and robustness to interference due to an integrated offset compensation algorithm operating without any external calibration. Simultaneous low-power operation and high sensitivity are achieved through a passive detector design based upon a terminal impedance boundary condition-based optimization of the detector dictated by the terminal impedances of the detector. This paper is implemented in a 130-nm CMOS process and obtains −76 dBm at the 151.8-MHz multi-use radio service (MURS) band and −71 dBm at the 433-MHz Industrial, Scientific and Medical (ISM) band with a total dc power draw of just 7.6 nW from 1.0- and 0.6-V supplies. [ABSTRACT FROM AUTHOR]

With recent development in sensor technology, multiple sensors have been widely adopted to monitor the degradation of a single unit simultaneously. The challenge of multisensor degradation modeling lies in that the sensor signals are often correlated and may contain only partial or even no information on the degradation status of a unit. To address these issues, this paper proposes a novel data fusion method that constructs a 1-D health index (HI) via automatically selecting and combining multiple sensor signals to better characterize the degradation process. In particular, this paper develops a new latent linear model that constructs the HI and selects informative sensors in a unified manner. Compared to the existing literature, the proposed method enjoys several unique advantages: 1) being able to derive the best linear unbiased estimator of the fusion coefficients; 2) offering high computational efficiency; 3) not requiring to know the exact value of the failure threshold; and 4) exhibiting general applicability in practice by not imposing restrictive assumptions on the degradation process. Simulation studies are presented to illustrate the effectiveness and evaluate the sensitivity of the proposed method. A case study on the degradation of aircraft gas turbine engines is also performed which shows a better prognostic performance of the proposed method compared with existing approaches. Note to Practitioners—This paper is motivated by the practical issue of degradation modeling and prognostics when multiple sensors simultaneously monitor the degradation status of a unit. Specifically, there are two fundamental questions involved, including: 1) how to screen out noninformative sensors and 2) how to properly combine the information from the selected sensor signals to accurately estimate the underlying degradation status of the unit. The novelty of this paper lies in developing an innovative latent model that tackles these two challenging questions in an integrated manner. There are four main steps involved when implementing the proposed method: 1) collecting multiple sensor signals and failure time of historical units; 2) selecting the informative sensors and deriving the optimal weight for each selected sensor; 3) constructing the health indices (HIs) of in-service units; and 4) predicting the remaining useful life of the in-service units using the constructed HIs. The proposed method is very useful when the degradation is under a single failure mode in a single environmental condition. In the future research, we will study the extension of the proposed model when sensor signals have a nonlinear relationship, as well as when the degradation process is under more complex scenarios such as multiple failure modes and multiple operation conditions. [ABSTRACT FROM AUTHOR]

This paper presents a calibration-free 12-bit 50-MS/s full-analog SAR analog-to-digital (A-to-D) converter (ADC) in 40-nm CMOS, which integrates the functions of comparator, SAR logic, and digital-to-analog converter (DAC) switches into multiple feedback zero-crossing detectors (FB-ZCDs). By eliminating all the digital circuits, the full-analog bit conversions operate with the asynchronous amplification-to-regeneration (A-to-R) operation that highly relaxes the requirements of the analog circuits and reference generator. This paper is implemented without any calibration, where the non-ideal effects (offset mismatch, insufficient residue, and noise) are mitigated and eliminated with the proposed techniques as follows. The offset mismatches among the FB-ZCDs are covered by the redundancies of the sub-radix DAC arrangement in the coarse ADC. The bottom-plate coupling compensators (BPCCs) are proposed to correct the consecutive SAR decision errors as quantizing the insufficient residue. The shared FB-ZCD with capacitor coupling decision (CCD) network is developed to obtain the identical offset in the fine ADC. Amplification extension (AE) technique is proposed to enhance the precision of the shared FB-ZCD by reconfiguring as a complementary inverter-based amplifier with the extended amplification period to enhance the output SNR. This paper occupies an active area of 0.01 mm2 and achieves the Nyquist-rate signal-to-noise-and-distortion ratio (SNDR), spurious-free dynamic range (SFDR), Walden figure-of-merit (FoMW), and Schreier FoM (FoMS) of 64.1, 75.6 dB, 5.3 fJ/conversion step, and 172.8 dB, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a local area influence probabilistic (LAIP) detector for estimating magnetic grain interactions with coded data bits in two-dimensional magnetic recording is combined with a 2-D Bahl–Cocke–Jelinek–Raviv (BCJR)-based detector for joint removal of intertrack interference (ITI) and intersymbol interference (ISI). The LAIP detector sends log-likelihood ratio estimates of coded bits and an estimate of the local ISI/ITI convolution mask to a BCJR-based ISI/ITI detector followed by an irregular-repeat-accumulate decoder. Simulation results on a random Voronoi grain media model with ISI and ITI show that the concatenated LAIP/BCJR system, which detects three tracks simultaneously, achieves user information bit areal densities competitive or higher than results reported in a previous paper that employed the LAIP detector alone on a Voronoi grain channel without ISI/ITI. Simulation results on a grain-flipping probability media model based on micromagnetic simulations show that the proposed detector achieves an 11.3% bit error rate reduction compared to a recently proposed system with a 2-D linear equalizer followed by a two-track BCJR detector with 2-D pattern-dependent noise prediction. [ABSTRACT FROM AUTHOR]

A generalized framework of spectrum sensing disruption for a power-limited adversary is proposed in this paper. In the literature, a conventional sensing attack typically assumes that the adversary has perfect knowledge of the spectral usage status. The framework in this paper considers a more general case where there are uncertainties in the estimates at the adversary. These uncertainties are modeled utilizing the probability of detection and the probability of false alarm. Then, the sum of the conditional probabilities of false detection at the secondary within the spectral range of interest, conditioned on the adversary’s estimated spectrum usage status, is maximized. It is shown that the optimal sensing attack, given perfect estimation is a special case of the proposed framework. When the adversary has perfect spectrum usage information, this framework reduces to a previously demonstrated optimal sensing disruption. When the adversary has imperfect information on the spectral status, the proposed framework is significantly more robust than conventional sensing attacks. Further, when the adversary’s power budget increases, it asymptotically approaches the sensing disruption performance upper bound. [ABSTRACT FROM AUTHOR]

This paper considers a low-complexity Gaussian message passing (GMP) Multi-User Detection (MUD) scheme for a coded massive multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (massive MIMO-NOMA), in which a base station with $N_{s}$ antennas serves $N_{u}$ sources simultaneously in the same frequency. Both $N_{u}$ and $N_{s}$ are large numbers, and we consider the overloaded cases with $N_{u}>N_{s}$. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. The GMP is attractive as its complexity order is only linearly dependent on the number of users, compared to the cubic complexity order of linear minimum mean square error (LMMSE) MUD. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. We prove that the variances of the GMP definitely converge to the mean square error (MSE) of the LMMSE multi-user detection. Second, the means of the traditional GMP will fail to converge when $N_{u}/N_{s}< (\sqrt {2}-1)^{-2}\approx 5.83$. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any $N_{u}/N_{s}>1$ , and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, the numerical results are provided to verify the validity and accuracy of the theoretical results presented. [ABSTRACT FROM AUTHOR]

This paper develops a structural diagnosis approach for fault detection and isolation in hybrid systems. Hybrid systems are characterized by continuous behaviors that are interspersed with discrete mode changes in the system, making the analysis of behaviors quite complex. In this paper, we address the mode detection problem in hybrid systems as the first step in diagnoser design. The proposed method uses analytic redundancy methods to detect the operating mode of the system even in the presence of system faults. We define hybrid minimal structurally overdetermined (HMSO) sets for hybrid systems. For residual generation, we develop the HMSO selection problem, formulated as a binary integer linear programming optimization problem to minimize the number of selected HMSOs and reduce online computational costs of the diagnosis algorithm. The proposed structural approach does not require preenumeration of all possible modes in the diagnoser design step. Therefore, our approach is feasible for hybrid systems with a large number of switching elements, implying that the system can have a large number of operating modes. The case study demonstrates the effectiveness of our approach. We discuss the results of our case study, and present directions for future work. Note to Practitioners—Developing feasible approaches for online monitoring, fault detection, and fault isolation of complex hybrid and embedded systems, such as automobiles, aircraft, power plants, and manufacturing processes, is essential in securing their safe, reliable, and efficient operation. Frequent changes in the operational modes of these systems because of operator actions, such as changing gears in an automobile, or environmental changes, such as driving on a wet or icy road make the fault detection and isolation task in these systems challenging. It is important to detect and isolate faults in all the operating modes, and at the same time, not mistake a mode change as a fault in the system. In this paper, we propose an approach that exploits the equation structure of hybrid systems behavior to combine mode detection and diagnosis in nonlinear hybrid systems. The proposed algorithm is scalable and efficient. We demonstrate its effectiveness using a case study of a reverse osmosis subsystem in an advances life support system for long duration manned space missions. Important challenges that can affect the success of our approach include the need for sufficiently detailed hybrid models that capture nominal and faulty behavior, and a sufficient number of sensors to make simultaneous mode detection and fault isolation possible. [ABSTRACT FROM AUTHOR]

This paper presents the results of the experiment with an overmassed wire-array $Z$ -pinch load that is powered by a current pulse (amplitude ~4.3 MA and rise time $\sim 0.9~\mu \text{s}$) from a multidisk magnetocumulative generator. Interesting feature of the experimentally recorded soft X-ray (SXR) pulse is short (~10 ns) duration at a microsecond current rise time with total energy of several hundreds of kilojoules. Obtained results are the evidence of high-quality implosion of the current-carrying plasma. The 2-D magnetohydrodynamic code FLUX-rz was used for the numerical simulation and analysis of the obtained results. The calculations indicated that only ~30% of the initial load mass took part in the SXR generation. The main peculiarities of the overmassed $Z$ -pinch implosion are discussed in this paper. [ABSTRACT FROM AUTHOR]

Pedestrian detection has remained an important research topic in both the computer vision and multimedia communities because of its importance in practical applications, such as driving assistance and video surveillance. Existing methods compare the response score with a fixed threshold to determine whether a candidate region contains pedestrians and produce dissatisfactory results that contain either missed detections or false detections, which are difficult to balance. This situation has a serious impact under the condition of variable scale. This paper investigates the functional relationship between the scores and scales of pedestrians. By designing experiments with multiple scales, we have found a discriminant surface in the score scale space. Pedestrians can be distinguished at various scale levels according to their locations on the discriminant surface. The proposed approach is evaluated using four challenging pedestrian detection datasets, including Caltech, INRIA, ETH, and KITTI, and the superior experimental results are achieved when compared with baseline methods. [ABSTRACT FROM AUTHOR]

Many applications of wireless sensor networks (WSNs) require deploying sensors in hostile environments, where an adversary may eavesdrop communications. To secure communications in WSNs, the $q$ -composite key predistribution scheme has been proposed in the literature. In this paper, we investigate secure $k$ -connectivity in WSNs operating under the $q$ -composite scheme, in consideration of the unreliability of wireless links. Secure $k$ -connectivity ensures that any two sensors can find a path in between for secure communication, even when $k-1$ sensors fail. We present conditions on how to set the network parameters such that the network has secure $k$ -connectivity asymptotically almost surely. The result is given in the form of a sharp zero–one law. [ABSTRACT FROM AUTHOR]

Indoor drone or Unmanned Aerial Vehicle (UAV) operations, automated or with pilot control, are an upcoming and exciting subset of drone use cases. Automated indoor flights tighten the requirements of stability and localization accuracy in comparison with the classic outdoor use cases which rely primarily on (RTK) GNSS for localization. In this paper the effect of multiple sensors on 3D indoor position accuracy is investigated using the flexible sensor fusion platform OASE. This evaluation is based on real-life drone flights in an industrial lab with mm-accurate ground truth measurements provided by motion capture cameras, allowing the evaluation of the sensors based on their deviation from the ground truth in 2D and 3D. The sensors under consideration for this research are: IMU, sonar, SLAM camera, ArUco markers and Ultra-Wideband (UWB) positioning with up to 6 anchors. The paper demonstrates that using this setup, the achievable 2D (3D) indoor localization error varies between 4.4 cm and 21 cm (4.9 cm and 67.2 cm) depending on the selected set of sensors. Furthermore, cost/accuracy trade-offs are included to indicate the relative importance of different sensor combinations depending on the (engineering) budget and use case. These lab results were validated in a Proof of Concept deployment of an inventory scanning drone with more than 10 flight hours in a 65 000 m2 warehouse. By combining lab results and real-life deployment experiences, different subsets of sensors are presented as a minimal viable solution for three different indoor use cases considering accuracy and cost: a large drone with little weight- and cost restrictions, one or more medium sized drones, and a swarm of weight and cost restricted nano drones. [ABSTRACT FROM AUTHOR]

The Sensors Applications Symposium (SAS) 2020 was the 15th edition of the flagship professional event in the sensor technology and application area organized by the IEEE Instrumentation and Measurement Society (IMS). It brought together sensor developers, innovators, and users, providing a forum for the exploration of new ideas, developments, implementations, and applications in the field. SAS 2020 was scheduled to take place on March 9–11, 2020, in Kuala Lumpur, Malaysia. However, the outbreak of the Covid-19 lead to the sudden and necessary decision of converting the event to a virtual conference. Indeed, IEEE SAS 2020 was arguably the first IMS and one of the first IEEE conferences that took place in that way, thus acting as a model for subsequent events. Nonetheless, the 2020 edition of the IEEE SAS has drawn significant professional interest worldwide leading to receiving 96 submissions of which 73 were accepted for the presentation at the virtual event. The review process of the symposium was designed to ensure the high quality of accepted papers authored by specialists from all over the world. Accepted papers were divided for presentation into ten virtual Oral Sessions and one large virtual Poster Session available for online access by the attendees during the three weeks of the virtual event. [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate that novel additive manufacturing (AM) technologies like metal adhesive laminate and multilayer inkjet printing can be effectively exploited to fabricate high-performing radio-frequency passive components on flexible substrates. Both processes are substrate independent and therefore suitable for manufacturing circuits on several unconventional materials, such as photo-paper. In addition, their complementary features can be combined to develop a novel hybrid process. Proof-of-concept AM prototypes of passive components, such as capacitors and inductors, exhibiting quality factors over 70, never achieved before on paper, and self-resonant frequencies beyond 4 GHz are described. The maximum inductance and capacitance per unit area are 1.4 nH/mm2 and 6.5 pF/mm2, respectively. Moreover, an AM RF mixer with a conversion loss below 10 dB is demonstrated still on paper substrate. The mixer, fabricated with the copper adhesive laminate method, operates at 1 GHz and exploits a lumped balun transformer connected to two packaged diodes in series. [ABSTRACT FROM PUBLISHER]

This paper provides a model-based method for analysis and hypothesis testing for paired data where one source of data has to be validated against another source of data that contains subjective and dynamic errors. This study deals with human-observed flow counts collected from traffic videos of freeway cameras. The available videos are mainly used for the purpose of manual observation by transportation personnel in case of emergency. This amounts to a varying inconsistency of the quality of the videos, which presents an additional challenge when analyzing the data. Video processing cannot be performed due to the mentioned issues with regard to the video quality. The processing has to be manually performed by humans who unfortunately have an inherent bias. If the video data have to be used for validating flow detector sensors, then a technique that performs validation with subjective and dynamic erroneous data as a result of the human bias is needed. This paper presents a methodology to deal with this issue. It is based on statistical testing with heteroscedasticity, which is demonstrated through a case study using data from traffic flow detectors and traffic cameras installed on highways in the Southern Nevada Region. A model for the relationship between the video ratings and the distribution of the human errors is developed taking into consideration the human bias. A method for identification of faulty detectors is also demonstrated based on the developed technique. [ABSTRACT FROM PUBLISHER]

Presents a study on a developed measurement system allowing researchers to measure the velocity of different two-dimensional (2-D) objects. What the measurement system was tested for; Contents of the measurement system; Presentation of different measurements at a test stand and at an industrial paper machine to demonstrate the accuracy of the measurement system and its flexibility and robustness.

The papers in this special section focus on new solutions and research initiatives in the area of media authentication and forensics. Media manipulation is now a pressing societal problem with broad implications. In the past, it required significant skill to create compelling manipulations because editing tools, such as Adobe Photoshop, required experienced users to alter images convincingly. Over the last several years, the rise of machine learning-based technologies has dramatically lowered the skill necessary to create compelling manipulations. For example, Generative Adversarial Networks (GANs) can create photo-realistic faces with no skill by an end-user other than the ability to refresh a web page. Deepfake algorithms, such as autoencoders to swap faces in a video, can create manipulations much more easily than previous generation video tools. While these ML techniques have many positive uses, they have also been misused for darker purposes such as to perpetrate fraud, to create false personas, and to attack personal reputations. Clearly, these are pressing and important problems for which technical solutions must play a role. [ABSTRACT FROM AUTHOR]