Search Results (50)

Autonomous vehicles, submersible robotic systems and drones, and other human-carrying equipment consistently adhere to a safety perimeter, ensuring collision-free navigation amidst surrounding objects. In contrast, roller coaster vehicles, despite being constrained to a predetermined track, necessitate frequent safety distance detection owing to the variability introduced by trees and decorative installations. Passengers’ limbs may protrude beyond vehicle boundaries, posing a collision hazard. The motion range of limbs, influenced by vehicle-specific conditions, mismatches standardized safety volumes (cylindrical, cubic, and rectangular) designed for mobile entities. The roller coaster industry’s current practice involves a moving safety frame, which visually inspects for collisions to assess safety distances, which is cumbersome and prone to oversight in intricate settings. Therefore, this study introduces a novel safety envelope detector (SE-detector). It creates a customer-defined virtual safety envelope around the roller coaster vehicle and measures the safety distance based on LiDAR (Light Detection and Ranging) to detect the intrusions of obstacles. Meanwhile, this SE-detector also innovatively integrated an accelerometer to synchronously measure the acceleration of the vehicle. The measured acceleration will be aligned with simulated sequences by dynamic time warping (DTW) algorithms to pinpoint intrusion location. Additionally, a wide-angle camera is also deployed to enhance perception of the surrounding environment. The SE-detector developed in this study has the capability to record inspection results. It is expected to enhance the inspection capabilities of the safety envelope for roller coasters, thereby improving the efficiency of safety distance inspection. Full article

Ultrasonic flowmeters are essential sensor devices widely used in remote metering systems, smart grids, and monitoring systems. In these environments, a low-power design is critical to maximize energy efficiency. Real-time data collection and remote consumption monitoring through remote metering significantly enhance network flexibility and efficiency. This paper proposes a low-complexity structure that ensures an accurate time-of-flight (ToF) estimation within an acceptable error range while reducing computational complexity. The proposed system utilizes Hilbert envelope detection and a differentiator-based parallel peak detector. It transmits and collects data through ultrasonic transmitter and receiver transducers and is designed for seamless integration as a node into wireless sensor networks (WSNs). The system can be involved in various IoT and industrial applications through high energy efficiency and real-time data transmission capabilities. The proposed structure was validated using the MATLAB software, with an LPG gas flowmeter as the medium. The results demonstrated a mean relative deviation of 5.07% across a flow velocity range of 0.1–1.7 m/s while reducing hardware complexity by 78.9% compared to the conventional FFT-based cross-correlation methods. This study presents a novel design integrating energy-efficient ultrasonic flowmeters into remote metering systems, smart grids, and industrial monitoring applications. Full article

This paper presents a passive Envelope Detector (ED) to be used for reception of OOK-modulated signals, such as in Wake-Up Receivers employed within Wireless Sensor Networks, widely used in the IoT. The main goal is implementing a temperature compensation mechanism in order to keep the passive ED input resistance roughly constant over temperature, making it a constant load for the preceding matching network and ultimately keeping the overall receiving chain sensitivity constant over temperature. The proposed ED was designed using STMicroelectronics 90 nm CMOS technology to receive 1 kbps OOK-modulated packets with a 433 MHz carrier frequency and a 0.6 V supply. The use of a block featuring a Proportional-to-Absolute Temperature (PTAT) current yields a 5 dB reduction in sensitivity temperature variation across the −40 °C to 120 °C range. Moreover, two different implementations were compared, one targeting minimal mismatch and the other one targeting minimal area. The minimal area version appears to be better in terms of estimated overall chain sensitivity at all temperatures despite a higher sensitivity spread. Full article

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions. Full article

This study introduces a rectenna, functioning as an RF envelope detector, utilizing a 16 nm bulk MOS transistor (metal-oxide-semiconductor field-effect transistor) for nonlinear detection. A circuit architecture is presented alongside a detailed design methodology and simulations. The detector efficiently demodulates a 2.4 GHz OOK (On/Off Keying) encoded signal, comprising a 32-bit word, within 320 μs. Remarkably, the circuit operates passively, requiring no voltage supply or bias current, and functions effectively with $-53$ dBm input power at the antenna. This capability enables the decoding of 32-bit unsigned integer radio packets as a wakeup radio event. The effectiveness of the envelope detector is substantiated through comprehensive simulations. Full article

This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures of merit were proposed: System Dynamic Range and System Tuning Envelope. To examine the conflict detection’s performance variability under different uncertainty levels and predictor types, simple multi-horizon trajectory predictors trained with two machine learning techniques of different characteristics are assessed at various look-ahead times: extreme gradient boosting with a discrete nature and a multi-layer perceptron regressor with a continuous nature. The results highlight the interdependence between the performances of the trajectory predictor and the conflict detector, and the quantification of this relationship can be represented through a sigmoid function. In addition, the two proposed figures of merit are effective for selecting suitable operating points in an adaptive conflict detector, based on dynamic thresholds and the performance requirements necessary for the trajectory predictors to achieve the expected detection performance at different look-ahead time. Full article

This paper presents the development of a miniaturized sensor device for selective detection of pathogens, specifically Influenza A Influenza virus, as an enveloped virus is relatively vulnerable to damaging environmental impacts. In consideration of environmental factors such as humidity and temperature, this particular pathogen proves to be an ideal choice for our study. It falls into the category of pathogens that pose greater challenges due to their susceptibility. An impedance biosensor was integrated into an existing platform and effectively separated and detected high concentrations of airborne pathogens. Bio-functionalized hydrogel-based detectors were utilized to analyze virus-containing particles. The sensor device demonstrated high sensitivity and specificity when exposed to varying concentrations of Influenza A virus ranging from 0.5 to 50 $\mathsf{\mu }$g/mL. The sensitivity of the device for a 0.5 $\mathsf{\mu }$g/mL analyte concentration was measured to be 695 $\mathsf{\Omega }·$ mL/$\mathsf{\mu }$g. Integration of this pathogen detector into a compact-design air quality monitoring device could foster the advancement of personal exposure monitoring applications. The proposed sensor device offers a promising approach for real-time pathogen detection in complex environmental settings. Full article

InAs/AlSb is a material system that can be used as a low-noise avalanche detector and operates in the short-wave infrared band. The interface parameters determine the wave function overlap (WFO). Maximizing the WFO of InAs/AlSb superlattices improves the quantum efficiency (QE) of infrared avalanche photodetectors (APDs). However, this remains a huge challenge. Here, the 8-band k·p perturbation method based on Bloch wave envelope function approximation was used to calculate the energy level structure of InAs/AlSb superlattices. The results indicate that the WFO is enhanced with increasing InSb interface thickness or when the InSb (or AlAs) interface is far from the intersection of InAs and AlSb. As the AlAs interface thickness increases, the WFO enhances and then reduces. The maximum increase in WFO is 15.7%, 93%, and 156.8%, respectively, with three different models. Based on the stress equilibrium condition, we consider the interface engineering scheme proposed for enhancing WFO with an increase of 16%, 114%, and 159.5%, respectively. Moreover, the absorption wavelength shift is less than ±0.1 μm. Therefore, the interface layer thickness and position can be designed to enhance the WFO without sacrificing other properties, thereby improving the QE of the device. It provides a new idea for the material epitaxy of APDs. Full article

Wake-up receivers are gaining importance in power-aware wireless sensor networks, as they significantly reduce power consumption during RF reception, enabling asynchronous communication with low latency. However, the performance of wake-up receivers still lags behind that of off-the-shelf RF transceivers. There is a growing demand for higher sensitivity, enhanced reliability, and lower latency while maintaining the lowest power consumption. In this article, our goal is to advance the performance of wake-up receivers based on off-the-shelf components and low-frequency pattern matchers. Through a systematic investigation, we proposed multiple improvements aimed at enhancing wake-up receiver performance and reliability. We introduced an improved passive envelope detector and realized a wake-up receiver for the 868 MHz band, which achieves a power consumption of $5.71$ μW and latency of $9.02$ ms. Our proposed wake-up receiver is capable of detecting signals down to an average power level of $-61.6$ dBm. These achievements represent significant advancements compared to the existing state of research on wake-up receivers based on low-frequency pattern matchers. Recent articles have not been able to attain such improved values in signal detection, power consumption, and latency. Full article

The Absorbing Aerosol Sensor (AAS) is carried on the Gao-Fen 5B (GF-5B) satellite, and it allows for the measurement of solar backscatter radiation by the atmosphere in the UV–Vis bands. The AAS is an imaging spectrometer that employs CCD for capturing both a continuous spectrum and the cross-track orientation with a 114° wide swath. The broad field of view provides daily global envelopment with a 4 km spatial resolution at the nadir. This paper mainly analyzes the initial working status of the instrument in orbit, including wavelength calibration, radiometric calibration, detector performance, and product availability. Preliminary observations indicate the ability of the AAS to monitor absorbing aerosols like dust, biomass burning, volcano ash, and some pollution aerosols and to identify the aerosol events in China and other regions with high spatial resolution. Full article

In 1995, Glendenning, Kettner and Weber postulated the existence of a new class of compact stars resembling white dwarfs but containing a small strange quark-matter core surrounded by hadronic layers attaining much higher densities than those found in white dwarfs. In our previous study, we have shown that it could be possible to unmask these so-called strange dwarfs through gravitational-wave observations with future space-based detectors such as the Laser Interferometer Space Antenna. We calculated more realistic equations of state for the hadronic envelope, but the quark core was treated using the simplest MIT bag model. In this paper, we investigate more closely the role of the possibly solid core in the structure and the tidal deformability of strange dwarfs in the full general relativistic framework by considering different models of strange quark matter in the crystalline color -superconducting phase. We find that the effect of the extreme rigidity of the elastic core on the tidal deformability is almost completely canceled by the surrounding hadronic layers. However, in all cases, the tidal deformability of strange dwarfs remains sufficiently lower than that of white dwarfs, to be potentially observable with gravitational waves despite the uncertainties in the strange quark-matter equation of state. Full article

As China’s urbanization has shifted from high-speed to high-quality development, Urban Land Use Efficiency (ULUE) has become an important scale for evaluating urban connotative development. However, existing research has paid less attention to errors caused by different urban environmental factors and random disturbances in ULUE. Therefore, the purpose of this study is to eliminate the impact of environmental factors and random disturbances on ULUE measurement results by placing different cities under the same environmental conditions. First, a three-stage DEA envelopment analysis (T-DEA) model is introduced to calculate the ULUE of 16 prefecture-level cities in Anhui Province from 2001 to 2020. On this basis, the kernel density estimation model, gravity center model, and geographic detector models are used to study the spatial and temporal evolution and driving factors. The results show that (1) the ULUE increases nonlinearly with time, with an increase of 12.74%; (2) the overall peak of ULUE is on the rise, and changes from a single peak to a multi-peak, indicating that ULUE is constantly improving and that there is multi-level differentiation between different cities; (3) during the study period, the center of gravity of the ULUE value moved 22.66 km to the northwest; the overall moving distance was small, and the moving rate was slow; and (4) the influence of the interaction of double driving factors on ULUE is significantly greater than that of a single driving factor, and the factors of urban built-up area and degree of openness, as the key driving factors affecting ULUE, also have a degree of duality. In addition, to achieve efficient urban land use and to coordinate the environmental differences faced by different cities, the government must formulate systematic policies and development strategies considering the spatial characteristics of urban land use efficiency and the complexity of the driving factors. Full article

For the ultra-wideband chaotic radio pulses an analytical model of an energy detector is designed and closed-form expressions for the optimal threshold and the detection probabilities are derived. The analytical solution is compared with the results of the numerical simulation. The model proves to be well-suited for the chaotic radio pulses with large base B (or processing gain, or the number of freedom degrees), namely, B ≥ 100. Potential applications areas of the proposed model are ultra-wideband communications with chaotic pulse carriers, e.g., ultra-wideband systems of 802.15.x standards, and multi-element chaotic communication systems. Full article

Amino acids belong to the most important compounds for life. They are structural components of proteins and required for growth and maintenance of cells. Essential amino acids cannot be produced by the organism and must be ingested through the nutrition. Therefore, the detection of amino acids is of great interest when analyzing cell culture media and nutrition. In this work, we present a split-ring resonator as a simple but sensitive detector for amino acids. Split-ring resonators are RLC resonant circuits with a split capacitance and thus a resonance frequency that depends on the electromagnetic properties of a liquid sample at the split capacitance. Here, the split capacitance is an interdigital structure for highest sensitivity and covered with a fluidic channel for flow through experiments. First measurements with a vector network analyzer show detection limits in the range from 105 µM for glutamic acid to 1564 µM for isoleucine, depending on the electromagnetic properties of the tested amino acids. With an envelope detector for continuous recording of the resonance frequency, the split-ring resonator can be used in ion chromatography. At a flow rate of 0.5 mL/min, it reaches limits of detection of 485 µM for aspartic acid and 956 µM for lysine. Full article

This paper introduces an unsupervised deep learning-driven scheme for mental tasks’ recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to EEG signals as an artifact removal algorithm to achieve robust recognition. Then, a quadratic time-frequency distribution (QTFD) was applied to extract effective time-frequency signal representation of the EEG signals and catch the EEG signals’ spectral variations over time to improve the recognition of mental tasks. The QTFD time-frequency features are employed as input for the proposed deep belief network (DBN)-driven Isolation Forest (iF) scheme to classify the EEG signals. Indeed, a single DBN-based iF detector is constructed based on each class’s training data, with the class’s samples as inliers and all other samples as anomalies (i.e., one-vs.-rest). The DBN is considered to learn pertinent information without assumptions on the data distribution, and the iF scheme is used for data discrimination. This approach is assessed using experimental data comprising five mental tasks from a publicly available database from the Graz University of Technology. Compared to the DBN-based Elliptical Envelope, Local Outlier Factor, and state-of-the-art EEG-based classification methods, the proposed DBN-based iF detector offers superior discrimination performance of mental tasks. Full article