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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]

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]

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]

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]

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]

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 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]

Signed quadrature spatial modulation (SQSM) was recently introduced to enhance the throughput of spatial modulation (SM) in multiple-input multiple-output (MIMO) systems. This strategy involves independently transmitting the real and imaginary components of a symbol and its inverse from four independent antennas. In this paper, an extended SQSM (ESQSM) system is proposed. It combines $K$ SQSM constellations and transmits them from the same transmit-antenna set with the aim of realizing a $K$ -fold improvement in the spectral efficiency (SE). The proposed ESQSM employs the average power of the $K$ SQSM constellations as an additional dimension for transmission. Performance of the proposed ESQSM surpasses that of other state-of-art SM systems such as the QSM, DSM, IQSM SQSM, and EQSM by 13 dB, 11 dB, 10.55 dB, 10 dB, and 6.8 dB, respectively. In this paper, we also propose an efficient ESQSM with a multi-user (ESQSM-MU) system inspired by the ESQSM ability in increasing the SE. The proposed ESQSM-MU system offers a significant bit error rate (BER) improvement and a higher capacity (i.e., $5K$ -users) compared to the current non orthogonal multiple access-SM (NOMA-SM) and SM-assisted multi-antenna NOMA (SM-AMA-NOMA) systems. Moreover, an efficient low-complexity detector is introduced for the ESQSM signal detection, promising a 91.2% reduction in the computational complexity of ESQSM and ESQSM-MU systems relative to the maximum likelihood (ML) with a trivial loss in performance. [ABSTRACT FROM AUTHOR]

This paper introduces a batch fabrication method to manufacture Micro-Electro-Mechanical System (MEMS) platforms for 3D integration of sensors spatially-distributed on a 2D plane. In the heart of the concept is a foldable MEMS structure with polymer hinges which is used to support and guide the assembly of the discrete planar sensors by means of folding them into a 3D shape, like origami, providing controlled distribution of sensors in space. Flexible hinges carrying the electrical interconnects are a critical structural element of the platform and material selection study for those is the main focus of this paper. We analyzed different materials for flexible hinges fabrication, including photo-definable polyimide and parylene-C. Three approaches for sensor integration are presented: 1) co-fabrication; 2) sensor drop-in; and 3) transfer bonding. The prototypes of structures with different flexible hinge materials were fabricated and evaluated based on their mechanical flexibility, chemical compatibility, and material outgassing. Parylene-C exhibited similar or better performances compared to polyimide, demonstrating in each of the experiments a higher degree of thermal flexibility up to 350 °C, a superior chemical resistance against hydrofluoric acid, and 2.9 times lower outgassing. [2020-0301] [ABSTRACT FROM AUTHOR]

The thermographic diagnostic of substation insulators can facility to detect their insulation status in time. However, the detection effect of infrared insulator images is usually poor due to the complex background of substation scene images. In order to solve this problem and improve the automatic detection capability of substation equipment, an infrared insulator image detection model based on the improved feature fusion single shot multibox detector is proposed in this paper. The model combines multi-scale feature maps to generate a new feature pyramid, and designs a new feature enhancement module in the shallow network of the model to improve its ability to extract features of infrared images of substation insulators. Meanwhile, the clustering algorithm is used to calculate the target aspect ratio information of the dataset to realize the adaptive change of the aspect ratio of the default box. Furthermore, the transfer learning is employed to further improve the learning efficiency of the model. Before the formal experiment, the design scheme of feature enhancement module is confirmed by ablation studies. Experiments are performed on the collected infrared insulator images, instrument transformer images and Pascal VOC 2007 dataset. It is demonstrated experimentally that this model achieves excellent detection results. [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]

Detecting large animals on roadways using automated systems such as robots or vehicles is a vital task. This can be achieved using conventional tools such as ultrasonic sensors, or with innovative technology based on smart cameras. In this paper, we investigate a vision-based solution. We begin the paper by performing a comparative study between three detectors: 1) Haar-AdaBoost; 2) histogram of oriented gradient (HOG)-AdaBoost; and 3) local binary pattern (LBP)-AdaBoost, which were initially developed to detect humans and their faces. These detectors are implemented, evaluated, and compared to each other in terms of accuracy and processing time. Based on our evaluation and comparison results, we design a two-stage architecture which outperforms the aforementioned detectors. The proposed architecture detects candidate regions of interest using LBP-AdaBoost in the first stage, which offers robustness to false positives in real-time conditions. The second stage is based on support vector machine classifiers that were trained using HOG features. The training data are generated from our novel dataset called large animal dataset, which contains common and thermographic images of large road-animals. We emphasize that no such public dataset currently exists. [ABSTRACT FROM PUBLISHER]

The presence of overvoltage can cause the insulation deterioration and excite the partial discharges (PD) in gas-insulated switchgear (GIS), and it is necessary to detect the overvoltage and PDs simultaneously to evaluate the insulation performance. A novel GIS internal composite sensor for PD detection and overvoltage measurement is proposed in this paper. The proposed sensor is mainly formed with a disc-type monopole UHF sensor and an overvoltage sensor. The effectiveness and sensitivity of the sensor are tested on a designed platform under power frequency voltage, switching impulse, lightning impulse, and oscillating lightning impulse, respectively. Experiment results show that the proposed sensor can be used to detect overvoltage and UHF signals effectively and is expected to help in the GIS condition evaluation. [ABSTRACT FROM AUTHOR]

Multilevel converters (MLCs) are pioneers in the field of electrical energy conversion. From the classification of MLCs, modular multilevel converter (MMC) is top-rated due to its attractive structure and performance. The modular nature and no need for isolated DC resources are two significant advantages of this converter. However, the need for many sensors to control its performance is a considerable drawback. In addition, the internal circulating current in the MMC causes losses, decreases efficiency, and increases the voltage ripple of capacitors. This paper proposes combining a new capacitor voltage sorting method and an open-loop controller based on arms energy estimation to control the MMC using two voltage sensors in the arms and one current sensor at the output. The proposed method ensures continuous voltage balancing of the capacitors and removes multiple voltage sensors from sub-modules (SMs). The simulation and experimental results verify the accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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.

This paper is a nontrivial extension of prior work on rate and position table leveling and inertial and magnetic sensors calibration techniques. The table leveling is essential for a class of calibration techniques using known stimuli, herein referred to as classical techniques. Such techniques are effective for the calibration of inertial sensors as they usually lead to closed-form solutions and body frame calibrations. However, the classical techniques are negatively affected by table leveling and azimuth orientation errors or by changes in the direction of the reference field. These errors are collectively referred to as field alignment errors in this paper. The impact of field alignment errors is usually avoided by using reference-free calibrations which rely on satisfying the invariance of the field modulus constraint. This constraint works well for orthogonal sensor triads as it leads to unique solutions. There is a great body of work dedicated to studying such calibration techniques for orthogonal sensor triads. However, redundant sensor configurations have too many degrees of freedom to satisfy the invariance of the modulus constraint; therefore, require special techniques. This paper introduces a novel approach that can be used to produce body frame calibrations for skew redundant sensor configurations in the presence of field alignment errors. Unlike prior work, no simplifying assumptions are made on the redundant sensor configuration (e.g., multiple orthogonal triads). The new method is showcased for the dodecahedron configuration, one of the most complex skew redundant configurations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration. The proposed algorithm utilizes the majorization-minimization (MM) algorithm and the duality principle to find the optimal configuration. Unlike the state-of-the-art approaches which are mainly geometrical in nature and restricted to sensors’ noise being uncorrelated, the proposed algorithm gives the exact orientations of the sensors and can easily deal with the cases of correlated noise. The proposed algorithm has been implemented and tested via numerical simulation in the MATLAB. The simulation results show that the algorithm converges to the optimal configurations and shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in hand movements in various application fields. However, the human hand is too dexterous and only has limited space. Developing a proper method to monitor and capture hand motion is meaningful. Conventionally, an optical measurement method is widely used to capture hand gestures. Nevertheless, this method is costly and has the problem of line-of-sight shielding. In this paper, a sensor fusion algorithm is proposed to calculate a more accurate result by calculating acceleration, magnetic field strength, and angular speed from the inertial measurement unit (IMU). The core concept of this algorithm is based on the idea of feedback control. To validate the performance, the active rotary platform is set up to compare the measured results to the references directly. Most of the error results are less than 3°, and the standard deviations are less than 1°. The measurement results ensure the feasibility of the inertial measurement method with the sensor fusion algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Reduced sensor systems are a topic receiving increasing attention as manufacturers seek to reduce costs by removing components from systems. One popular option is to remove phase current sensors and to estimate the missing currents using the ones remaining. This paper proves that it is possible to estimate the three-phase currents of an anisotropic permanent-magnet synchronous machine drive when only one-phase current sensor is present. The necessary conditions for observability and stability have been derived, providing certainty for the design of a permanent-magnet synchronous machine drive controller. The concept has been verified with high-fidelity simulations and experiments that show the one-phase current sensor observer is capable of controlling the drive system in the presence of sensor noise and significant parameter errors. The proposed phase current observer can be used to remove one sensor from a drive system by employing the two sensor observer with the one sensor observer acting as a failsafe. [ABSTRACT FROM AUTHOR]

In the companion paper, a model to detect failures leading to an increase of the water content in transformer oil was proposed. In this paper, the experimental verification of the Model is presented. The Model was tested in a 1500-kVA test transformer constructed as a reduced scale model of a 60-MVA 220-kV transformer. In order to load the Transformer to have the capability of varying load and power factor over a wide range, the opposition method described in IEC 60076-2 Standard was used. Sensors to measure temperature and moisture in oil were installed in the Test Transformer. The Model was adjusted and validated using a set of data corresponding to different operating conditions. The Transformer was submitted to an accelerated aging process studying the Model response in such a case. In order to verify the Model's ability to detect failures, a water ingress in the Transformer was simulated. [ABSTRACT FROM AUTHOR]

This paper is concerned with detecting an integer parameter vector inside a box from a linear model that is corrupted with a noise vector following the Gaussian distribution. One of the commonly used detectors is the maximum likelihood detector, which is obtained by solving a box-constrained integer least squares problem, that is NP-hard. Two other popular detectors are the box-constrained rounding and Babai detectors due to their high efficiency of implementation. In this paper, we first present formulas for the success probabilities (the probabilities of correct detection) of these three detectors for two different situations: the integer parameter vector is deterministic and is uniformly distributed over the constraint box. Then, we give two simple examples to respectively show that the success probability of the box-constrained rounding detector can be larger than that of the box-constrained Babai detector and the latter can be larger than the success probability of the maximum likelihood detector when the parameter vector is deterministic, and prove that the success probability of the box-constrained rounding detector is always not larger than that of the box-constrained Babai detector when the parameter vector is uniformly distributed over the constraint box. Some relations between the results for the box constrained and ordinary cases are presented, and two bounds on the success probability of the maximum likelihood detector, which can easily be computed, are developed. Finally, simulation results are provided to illustrate our main theoretical findings. [ABSTRACT FROM AUTHOR]

Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. However, existing methods can hardly differentiate the target from its neighboring objects within the same bounding box region due to their similar textures and low-contrast boundaries. To deal with this problem, in this paper, we propose an object-guided instance segmentation method. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. To perform segmentation, an object-guided coarse-to-fine segmentation branch is built along with the detection branch. The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region. To further improve the segmentation quality, we design an auxiliary feature refinement module that densely samples and refines point-wise features in the boundary regions. Experimental results on three biological image datasets demonstrate the advantages of our method. The code will be available at https://github.com/yijingru/ObjGuided-Instance-Segmentation. [ABSTRACT FROM AUTHOR]

The application of Hall-based BLDC motors ranges from automotive accessory components such as oil and water pumps to high speed compressors. This paper proposes a new twelve-step unsymmetrical 160° commutation strategy using a single Hall-effect sensor method for brushless DC (BLDC) motor drives. The signal from the Hall-effect sensor is used to generate a number of pseudo Hall signals via a proposed speed-position estimator. The pseudo signals are then employed to control switching logic of a voltage source inverter (VSI). The effectiveness of the single Hall sensor method is examined on the unconventional 160° strategy, and on conventional 120°, 150° and 180° strategies with promising results. The paper presents a detailed comparison of the twelve-step unsymmetrical 160° commutation strategy with six-step voltage space vector that is used in conventional 120° and 180° strategies, and twelve-step symmetrical voltage space vector that is used in 150° strategy. Simulation results are validated via experimental measurements on a laboratory set-up using a real time dSPACE control environment, a representative BLDC motor prototype, a VSI and associated control components. [ABSTRACT FROM AUTHOR]

This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT. [ABSTRACT FROM AUTHOR]

In this paper, the use of laser modulation for characterizing the sensitivity of infrared sensors and autonomously adjusting frame rates are discussed. Lasers modulated at frequencies near the frame rate of the sensor under test demonstrated a more significant effect than modulation frequencies further from the frame rate. A method to quantify the effectiveness of a particular laser modulation frequency was developed. Experimental runs with and without laser tracking are compared in order to demonstrate how the laser affects the tracking algorithms of the sensor. The effect of the relationship between the modulation frequency and the frame rate on the sensor’s accuracy is also determined. An algorithm was developed to improve a sensor’s ability to detect signals modulated at its frame rate. The paper concludes with discussions on applications of these results to commonly available sensor systems. [ABSTRACT FROM PUBLISHER]

Consider a continuous signal that cannot be observed directly. Instead, one has access to multiple corrupted versions of the signal. The available corrupted signals are correlated because they carry information about the common remote signal. The goal is to reconstruct the original signal from the data collected from its corrupted versions. Known as the indirect or remote reconstruction problem, it has been mainly studied in the literature from an information theoretic perspective. A variant of this problem for a class of Gaussian signals, known as the “Gaussian CEO problem,” has received particular attention; for example, it has been shown that the problem of recovering the remote signal is equivalent with the problem of recovering the set of corrupted signals (separation principle). The information theoretic formulation of the remote reconstruction problem assumes that the corrupted signals are uniformly sampled and the focus is on optimal compression of the samples. On the other hand, in this paper, we revisit this problem from a sampling perspective. More specifically, assuming restrictions on the sampling rate from each corrupted signal, we look at the problem of finding the best sampling locations for each signal to minimize the total reconstruction distortion of the remote signal. In finding the sampling locations, one can take advantage of the correlation among the corrupted signals. The statistical model of the original signal and its corrupted versions adopted in this paper are similar to the one considered for the Gaussian CEO problem; i.e., we restrict to a class of Gaussian signals. Our main contribution is a fundamental lower bound on the reconstruction distortion for any arbitrary nonuniform sampling strategy. This lower bound is valid for any sampling rate. Furthermore, it is tight and matches the optimal reconstruction distortion in low and high sampling rates. Moreover, it is shown that in the low sampling rate region, it is optimal to use a certain nonuniform sampling scheme on all the signals. On the other hand, in the high sampling rate region, it is optimal to uniformly sample all the signals. We also consider the problem of finding the optimal sampling locations to recover the set of corrupted signals, rather than the remote signal. Unlike the information theoretic formulation of the problem in which these two problems were equivalent, we show that they are not equivalent in our setting. Finally, another contribution of this paper is a new reverse majorization inequality that might be of independent interest. [ABSTRACT FROM PUBLISHER]