The OECD Advisory Group on Endocrine Disrupters Testing and Assessment met on 16-17 October 2014 in Paris to discuss the development and update of Test Guidelines and related documents for endocrine disrupters testing and assessment.

Ahmedabad youth offers model for quicker detection

For Ujjawal Panchal, a 21-year-old undergraduate who is in his final year at a college in Chennai, getting stuck in his hometown of Ahmedabad due to the lockdown seems to have opened new doors.

A new Rococo 2 X-ray fluorescence detector was implemented into the cryogenic sample environment at the Hard X-ray Micro/Nano-Probe beamline P06 at PETRA III, DESY, Hamburg, Germany. A four sensor-field cloverleaf design is optimized for the investigation of planar samples and operates in a backscattering geometry resulting in a large solid angle of up to 1.1 steradian. The detector, coupled with the Xspress 3 pulse processor, enables measurements at high count rates of up to 106 counts per second per sensor. The measured energy resolution of ∼129 eV (Mn Kα at 10000 counts s−1) is only minimally impaired at the highest count rates. The resulting high detection sensitivity allows for an accurate determination of trace element distributions such as in thin frozen hydrated biological specimens. First proof-of-principle measurements using continuous-movement 2D scans of frozen hydrated HeLa cells as a model system are reported to demonstrate the potential of the new detection system.

Detection of heavy elements, such as metals, in macromolecular crystallography (MX) samples by X-ray fluorescence is a function traditionally covered at synchrotron MX beamlines by silicon drift detectors, which cannot be used at X-ray free-electron lasers because of the very short duration of the X-ray pulses. Here it is shown that the hybrid pixel charge-integrating detector JUNGFRAU can fulfill this function when operating in a low-flux regime. The feasibility of precise position determination of micrometre-sized metal marks is also demonstrated, to be used as fiducials for offline prelocation in serial crystallography experiments, based on the specific fluorescence signal measured with JUNGFRAU, both at the synchrotron and at SwissFEL. Finally, the measurement of elemental absorption edges at a synchrotron beamline using JUNGFRAU is also demonstrated.

Enhancement of X-ray excited optical luminescence in a 100 µm-thick diamond plate by introduction of defect states via electron beam irradiation and subsequent high-temperature annealing is demonstrated. The resulting X-ray transmission-mode scintillator features a linear response to incident photon flux in the range 7.6 × 108 to 1.26 × 1012 photons s−1 mm−2 for hard X-rays (15.9 keV) using exposure times from 0.01 to 5 s. These characteristics enable a real-time transmission-mode imaging of X-ray photon flux density without disruption of X-ray instrument operation.

Astronomers had a mystery on their hands. No matter where they looked, from inside the Milky Way to distant galaxies, they observed a puzzling glow […]

The post GBT Detection Unlocks Exploration of ‘Aromatic’ Interstellar Chemistry appeared first on Smithsonian Insider.

Increased research is the key to developing more widely applicable detection systems to find pipe bombs before they explode and to help catch the perpetrators when a bomb has gone off, says a new report from a committee of the National Research Council.

Good morning. On behalf of the National Academies and my colleagues on the committee, I welcome those of you in the room as well as those listening to the live audio webcast.

A test that can detect Parkinson's disease in the early stages of the illness has moved a step closer.

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Better methods are needed to monitor underwater gas leaks. A new study outlines a technique that uses sound to detect bubbles of escaped gas and could help produce more accurate measurements of gas leakage rates from carbon capture and storage (CCS) sites, pipelines and natural leakage sites.

Crayfish plague, spread by invasive North American crayfish, is currently devastating native European populations. However, while the disease is commonly diagnosed on the basis of diseased animals, free-living infective spores can contaminate water bodies. In the first study to test detection techniques for this disease in natural waterways, researchers found that invasive signal crayfish release low levels of plague spores, allowing it to spread undetected.

The presence of disinfection by-products (DBPs) in drinking water is an emerging health concern. DBPs come in many classes and are chemically diverse, making them challenging to monitor. Swedish researchers have evaluated a new method for the simultaneous determination of a broader range of DBPs than typically possible using other available techniques. The method uses gas chromatography (a laboratory technique that separates and analyses vaporisable compounds in a mixture), together with a halogen-specific detector (XSD). Having been tested in real water samples from two municipal waterworks in Sweden, the method has been optimised for the simultaneous determination of a wide range of neutral DBPs.

Groups of chemicals used as flame retardants were present in the bodies of Antarctic rock cod (Trematomus bernacchii), young gentoo penguin (Pygoscelis papua), and brown skua seabird (Stercorarius antarcticus) collected from King George Island, Antarctica. This study is the first to find some of these chemicals in Antarctica, confirming that they undergo long-range transport and can reach isolated areas where they are not widely produced or used.

New safety features can identify driver fatigue and deliver warnings before an accident happens.

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Superb Detection of Many Types of Transparent Objects(E3S-DB)

Temperature control with heater burnout detection in conjunction with a temperature input unit and PID instructions(NX-HB)

These globally-deployed products, integrated with the company's innovative sensors, enable rapid detection and identification of various threats

Faster authentication can reduce contact center costs, help eliminate the need for security questions, and contribute to an enhanced customer experience

Forest disturbances are increasing in extent and intensity, annually altering the structure and function of affected systems across millions of acres. Land managers need rapid assessment tools that can be used to characterize disturbance events across space and to meet forest planning needs.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious information imbalance. To acquire enough semantic information for shallow layers, Feature Pyramid Networks (FPN) is used to build a top-down propagated path. In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer. Our IPG-Net has two main parts: the image pyramid guidance transformation module and the image pyramid guidance fusion module. Our main idea is to introduce the image pyramid guidance into the backbone stream to solve the information imbalance problem, which alleviates the vanishment of the small object features. This IPG transformation module promises even in the deepest stage of the backbone, there is enough spatial information for bounding box regression and classification. Furthermore, we designed an effective fusion module to fuse the features from the image pyramid and features from the backbone stream. We have tried to apply this novel network to both one-stage and two-stage detection models, state of the art results are obtained on the most popular benchmark data sets, i.e. MS COCO and Pascal VOC.

A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important "on the fly". This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that t-SS3 is able to improve both current results and the richness of visual explanations.

Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.

Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76.

Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency. In particular, we consider three geometric factors, i.e., overlap area, normalized central point distance and aspect ratio, which are crucial for measuring bounding box regression in object detection and instance segmentation. The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases. The training of deep models using CIoU loss results in consistent AP and AR improvements in comparison to widely adopted $ell_n$-norm loss and IoU-based loss. Furthermore, we propose Cluster-NMS, where NMS during inference is done by implicitly clustering detected boxes and usually requires less iterations. Cluster-NMS is very efficient due to its pure GPU implementation, , and geometric factors can be incorporated to improve both AP and AR. In the experiments, CIoU loss and Cluster-NMS have been applied to state-of-the-art instance segmentation (e.g., YOLACT), and object detection (e.g., YOLO v3, SSD and Faster R-CNN) models. Taking YOLACT on MS COCO as an example, our method achieves performance gains as +1.7 AP and +6.2 AR$_{100}$ for object detection, and +0.9 AP and +3.5 AR$_{100}$ for instance segmentation, with 27.1 FPS on one NVIDIA GTX 1080Ti GPU. All the source code and trained models are available at https://github.com/Zzh-tju/CIoU

Diagnosis of high impedance fault (HIF) is a challenge for nowadays distribution network protections. The fault current of a HIF is much lower than that of a normal load, and fault feature is significantly affected by fault scenarios. A detection and feeder identification algorithm for HIFs is proposed in this paper, based on the high-resolution and synchronous waveform data. In the algorithm, an interval slope is defined to describe the waveform distortions, which guarantees a uniform feature description under various HIF nonlinearities and noise interferences. For three typical types of network neutrals, i.e.,isolated neutral, resonant neutral, and low-resistor-earthed neutral, differences of the distorted components between the zero-sequence currents of healthy and faulty feeders are mathematically deduced, respectively. As a result, the proposed criterion, which is based on the distortion relationships between zero-sequence currents of feeders and the zero-sequence voltage at the substation, is theoretically supported. 28 HIFs grounded to various materials are tested in a 10kV distribution networkwith three neutral types, and are utilized to verify the effectiveness of the proposed algorithm.

XSS is a security vulnerability that permits injecting malicious code into the client side of a web application. In the simplest situations, XSS vulnerabilities arise when a web application includes the user input in the web output without due sanitization. Such simple XSS vulnerabilities can be detected fairly reliably with blackbox scanners, which inject malicious payload into sensitive parts of HTTP requests and look for the reflected values in the web output.

Contemporary blackbox scanners are not effective against stored XSS vulnerabilities, where the malicious payload in an HTTP response originates from the database storage of the web application, rather than from the associated HTTP request. Similarly, many blackbox scanners do not systematically handle context-sensitive XSS vulnerabilities, where the user input is included in the web output after a transformation that prevents the scanner from recognizing the original value, but does not sanitize the value sufficiently. Among the combination of two basic data sources (stored vs reflected) and two basic vulnerability patterns (context sensitive vs not so), only one is therefore tested systematically by state-of-the-art blackbox scanners.

Our work focuses on systematic coverage of the three remaining combinations. We present a graybox mechanism that extends a general purpose database to cooperate with our XSS scanner, reporting and injecting the test inputs at the boundary between the database and the web application. Furthermore, we design a mechanism for identifying the injected inputs in the web output even after encoding by the web application, and check whether the encoding sanitizes the injected inputs correctly in the respective browser context. We evaluate our approach on eight mature and technologically diverse web applications, discovering previously unknown and exploitable XSS flaws in each of those applications.

Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.

Most optimization-based community detection approaches formulate the problem in a single or bi-objective framework. In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. In the first variant, named NSGA-III-KRM, we considered Kernel k means, Ratio cut, and Modularity, as the three objectives, whereas the second variant, named NSGA-III-CCM, considers Community score, Community fitness and Modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art approaches along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the addition of the third objective does not worsen the results of the other two objectives. We also propose a simple method to rank the Pareto solutions so obtained by proposing a new measure, namely the ratio of the hyper-volume and inverted generational distance (IGD). The higher the ratio, the better is the Pareto set. This strategy is particularly useful in the absence of empirical attainment function in the multi-objective framework, where the number of objectives is more than two.

Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different levels while seldom touching the inter-level correlation in the feature pyramid. Early computer vision methods extracted scale-invariant features by locating the feature extrema in both spatial and scale dimension. Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and spatial) features and outperforms other meticulously designed feature fusion modules. Based on the viewpoint of 3-D convolution, an integrated batch normalization that collects statistics from the whole feature pyramid is naturally inserted after the pyramid convolution. Furthermore, we also show that the naive pyramid convolution, together with the design of RetinaNet head, actually best applies for extracting features from a Gaussian pyramid, whose properties can hardly be satisfied by a feature pyramid. In order to alleviate this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that aligns the shared pyramid convolution kernel only at high-level feature maps. Being computationally efficient and compatible with the head design of most single-stage object detectors, the SEPC module brings significant performance improvement ($>4$AP increase on MS-COCO2017 dataset) in state-of-the-art one-stage object detectors, and a light version of SEPC also has $sim3.5$AP gain with only around 7% inference time increase. The pyramid convolution also functions well as a stand-alone module in two-stage object detectors and is able to improve the performance by $sim2$AP. The source code can be found at https://github.com/jshilong/SEPC.

A system and method that facilitates urban traffic state detection based on support vector machine (SVM) and multilayer perceptron (MLP) classifiers is provided. Moreover, the SVM and MLP classifiers are fused into a cascaded two-tier classifier that improves the accuracy of the traffic state classification. To further improve the accuracy, the cascaded two-tier classifier (e.g., MLP-SVM), a single SVM classifier and a single MLP classifier are fused to determine a final decision for a traffic state. In addition, fusion strategies are employed during training and implementation phases to compensate for data acquisition and classification errors caused by noise and/or outliers.

The method for detection of cyanide in water is a method for the detection of a highly toxic pollutant, cyanide, in water using ZnO2 nanoparticles synthesized locally by an elegant Pulsed Laser Ablation technique. ZnO2 nanoparticles having a median size of 4 nm are synthesized from pure zinc metal target under UV laser irradiation in a 1-10% H2O2 environment in deionized water. The synthesized ZnO2 nanoparticles are suspended in dimethyl formamide in the presence of Nafion, and then ultrasonicated to create a homogenous suspension, which is used to prepare a thin film of ZnO2 nanoparticles on a metal electrode. The electrode is used for cyanide detection.

Embodiments of apparatus and methods for error detection and correction are described. A codeword may have a data portion and associated check bits. In embodiments, one or more error detection modules may be configured to detect a plurality of error types in the codeword. One or more error correction modules coupled with the one or more error detection modules may be further configured to correct errors of the plurality of error types once they are detected by the one or more error detection modules. Other embodiments may be described and/or claimed.

In accordance with one or more aspects, an initial output string is generated by a random number generator. The initial output string is sent to a random number service, and an indication of failure is received from the random number service if the initial output string is the same as a previous initial output string received by the random number service. Operation of the device is ceased in response to the indication of failure. Additionally, entropy estimates for hash values of an entropy source can be generated by an entropy estimation service based on hash values of various entropy source values received by the entropy estimation service. The hash values can be incorporated into an entropy pool of the device, and the entropy estimate of the pool being updated based on the estimated entropy of the entropy source.

Systems, devices, processors, and methods are described which may be used for the reception of a wireless broadband signal at a user terminal from a gateway via a satellite. A wireless signal may include a series of physical layer frames, each frame including a physical layer header and payload. The received signal is digitized and processed using various novel physical layer headers and related techniques to synchronize the physical layer frames and recover data from physical layer headers for purposes of demodulation and decoding.

A method of detecting pre-determined phrases to determine compliance quality is provided. The method includes determining whether at least one of an event or a precursor event has occurred based on a comparison between pre-determined phrases and a communication between a sender and a recipient in a communications network, and rating the recipient based on the presence of the pre-determined phrases associated with the event or the presence of the pre-determined phrases associated with the precursor event in the communication.

A method for detecting and conveying an alarm signal, when an absorbent article is unfastened or, completely removed from the body of the wearer. The method is intended to be used in parallel with a method for detecting wetness in the absorbent article and further relates to an integrated detection-and-alarm method for detecting unfastening and/or wetness in an absorbent article. A system for detecting and conveying an alarm signal when an absorbent article is unfastened or removed from the body of the wearer and/or when the article is wet. The system includes (a) and absorbent article having at least one absorbent layer, the object to be displaced, such as a fastening system, one or more sensoring devices, one or more transmitting devices, and (b) a remote receiver. Furthermore, the system relates to the use of the system in the care of children and adults suffering from incontinence and/or psychological illnesses.

Techniques for handling version information using a copy engine. In one embodiment, an apparatus comprises a copy engine configured to perform one or more operations associated with a block memory operation in response to a command. Examples of block memory operations may include copy, clear, move, and/or compress operations. In one embodiment, the copy engine is configured to handle version information associated with the block memory operation based on the command. The one or more operations may include operating on data in a cache and/or modifying entries in a memory. In one embodiment, the copy engine is configured to compare version information in the command with stored version information. The copy engine may overwrite or preserve version information based on the command. The copy engine may be a coprocessing element. The copy engine may be configured to maintain coherency with other copy engines and/or processing elements.

A system, methods and server for monitoring health and safety of individuals in a population and sending alert notifications when exceptions are detected include comparing biometric data obtained from the individuals to a biometric model generated for the individual through computer-learning methods. Biometric data may be gathered by wireless biometric sensor devices which transmit biometric data to receiver devices, which relay the biometric data to a server. The biometric model may be maintained in the server and include nominal and threshold biometric parameters for each individual based on biometric sensor data gathered or analyzed over a period of time. An alert may be issued by the server when an individual's biometric data is outside a threshold in the biometric model. The transmitted alert may depend upon the nature of the exception, user settings and past notification experience. Alerts may be escalated when not answered within defined durations.

Systems and methods for processing magnetic resonance imaging (MRI) data are provided. A method includes receiving MRI data comprising a plurality of k-space points and deriving a plurality of image data sets based on the MRI data, each of the plurality of MRI image sets obtained by zeroing a different one of the plurality of k-space points. The method further includes computing image space metric values for each of the plurality of image data sets and adjusting a portion of the MRI data associated with ones of the image space metric values that fail to meet a threshold value to yield adjusted MRI data.

The disclosed embodiments relate to pulse oximetry. An exemplary pulse oximeter comprises a probe that is adapted to be attached to a body part of a patient to create a signal indicative of an oxygen saturation of blood of the patient, and a processor that is adapted to receive the signal produced by the probe, to calculate an SPO2 value based on the signal, to detect a plurality of pattern types of SPO2 indicative of pathophysiologic events, and to produce an output indicative of a detected one of the plurality of pattern types.

A device for detecting a cardiac event is disclosed. Detection of an event is based on a test applied to a parameter whose value varies according to heart rate. Both the parameter value and heart rate (RR interval) are filtered with an exponential average filter. From these filtered values, the average change in the parameter and the RR interval are also computed with an exponential average filter. Before computing the average change in the parameter, large changes in the parameter over short times, which may be caused by body position shifts, are attenuated are removed, so that the average change represents an average of small/smooth changes in the parameter's value that are characteristic of acute ischemia, one of the cardiac events that may be detected. The test to detect the cardiac event depends on the heart rate, the difference between the parameter's value and its upper and lower normal values, and its average change over time, adjusted for heart rate changes. The upper and lower normal parameter values as a function of heart rate are determined from long term stored data of the filtered RR values and parameter values. Hysteeresis related data and transitory deviations from normal (e.g. vasospasm related data) are excluded from the computation of normal upper and lower parameter bounds.

A device is described for measuring electrical characteristics of biological tissues with one or a plurality of electrodes and a processor controlling the stimulation and measurement in order to detect the presence of abnormal tissue masses in the breast and determine probability of tumors containing malignant cancer cells being present in a breast. The device has the capability of providing the location of the abnormality, at least to the quadrant. Either single or multiple source electrodes can be used. Either palpable lumps can be evaluated or screening or breasts, whether with palpable masses or not, can be accomplished. The method for measuring electrical characteristics includes placing electrodes and applying a voltage waveform in conjunction with a current detector. A mathematical analysis method is then applied to the collected data, which computes spectrum of frequencies and correlates magnitudes and phases with given algebraic conditions to determine mass presence and type.

The abnormality determination apparatus, which is for determining presence of an abnormality in an angle detection device configured to output an output signal having a value equivalent to a rotational angle of a rotating body, includes a smoothing device configured to receive the output signal of the angle detection device to smooth a dependent variable of a function whose independent variable is the rotational angle equivalent value, and a parameter calculation device for calculating an abnormality determination parameter based on the dependent variable smoothed by the smoothing device. The function is such that an integrated value of the rotational angle equivalent value over a predetermined time section is always positive or negative, and is configured to vary the dependent variable continuously in accordance with continuous variation of the independent variable in at least a part of the predetermined time section.

A system having a flame rod assembly for operation in a high temperature pilot burner. The assembly is designed for operation in temperatures from about −40 to 1100 degrees C. The system may operate in inclement weather involving high speed winds and significant amounts of moisture and rain to hurricane storm force levels and rates. The system incorporates an electrical apparatus which may provide flame sensing and ignition via the flame rod assembly incorporating a quick drying insulator around a rod of the assembly to ensure proper operation of the electrical apparatus.

A method includes scanning a lithography mask with a repair process, and measuring back-scattered electron signals of back-scattered electrons generated from the scanning. An endpoint is determined from the back-scattered electron signals. A stop point is calculated from the endpoint. The step of scanning is stopped when the calculated stop point is reached.

Wind instrument includes a tubular body having a plurality of tone holes, and a plurality of keys capable of opening and closing the tone holes. Via a retaining member, detector units are provided within the tubular body in corresponding relation to the keys, and each of the detector units is generally opposed to the back surface of the corresponding key. Each of the detector units detects a relative distance to the back surface and outputs an electrical signal, on the basis of which an opening/closing state of the key can be detected. The retaining member, accommodated in the tubular body, positions and retains each of the detector units in such a manner that the keys and tubular body and the individual detector units are kept in non-contacting relation to each other.