Search Results (95)

This article deals with power supply linear and switching regulators commonly used in various applications for stabilizing an output voltage and ensuring a necessary power input to the load. It describes their basic parameters, performances, advantages, and disadvantages according to their topologies. We design a measurement chain for efficiency evaluation based on power monitors INA219 connected to an embedded system, Arduino UNO. Measurements were focused on evaluation linear regulators MA7805, LM317, switching regulators SZBK07, LM2575, SCW05B-05, and XH-M401. The resulting efficiency of linear and switching regulators was analyzed and errors in the measurement chain were evaluated. The second contribution is an innovative way of carrying out regulator noise estimation using a fast Allan variance method, focused on white noise and flicker noise (bias instability). The main contribution is employing a fast Allan variance method algorithm that dramatically decreases computation time by up to 11 s for 72 million measured (or generated) samples. It enables the analysis of large data sets of various physical quantities (for example, regulator output voltages). Full article

Landing a multi-rotor uncrewed aerial vehicle (UAV) on a moving target in the presence of partial observability, due to factors such as sensor failure or noise, represents an outstanding challenge that requires integrative techniques in robotics and machine learning. In this paper, we propose embedding a long short-term memory (LSTM) network into a variation of proximal policy optimization (PPO) architecture, termed robust policy optimization (RPO), to address this issue. The proposed algorithm is a deep reinforcement learning approach that utilizes recurrent neural networks (RNNs) as a memory component. Leveraging the end-to-end learning capability of deep reinforcement learning, the RPO-LSTM algorithm learns the optimal control policy without the need for feature engineering. Through a series of simulation-based studies, we demonstrate the superior effectiveness and practicality of our approach compared to the state-of-the-art proximal policy optimization (PPO) and the classical control method Lee-EKF, particularly in scenarios with partial observability. The empirical results reveal that RPO-LSTM significantly outperforms competing reinforcement learning algorithms, achieving up to 74% more successful landings than Lee-EKF and 50% more than PPO in flicker scenarios, maintaining robust performance in noisy environments and in the most challenging conditions that combine flicker and noise. These findings underscore the potential of RPO-LSTM in solving the problem of UAV landing on moving targets amid various degrees of sensor impairment and environmental interference. Full article

The expected acceleration in sea level rise (SLR) throughout this century poses significant threats to coastal cities and low-lying regions. Since the early 1990s, high-precision multi-mission satellite altimetry (SA) has enabled the routine measurement of sea levels, providing a continuous 30-year record from which the mean sea level rise (global and regional) and its variability can be computed. The latest reprocessed product from CMEMS span the period from 1993 to 2020, and have enabled the acquisition of accurate sea level data within the coastal range of 0–20 km. In order to fully utilize this new dataset, we establish a global virtual network consisting of 184 virtual SA stations. We evaluate the impact of different stochastic noises on the estimation of the velocity of the sea surface height (SSH) time series using BIC_tp information criterion. In the second step, the principal component analysis (PCA) allows the common mode noise in the SSH time series to be mitigated. Finally, we analyzed the spatiotemporal characteristics and accuracy of sea level change derived from SA. Our results suggest that the stochasticity of the SSH time series is not well described by a combination of random, flicker, and white noise, but is best described by an ARFIM/ARMA/GGM process. After removing the common mode noise with PCA, about 96.7% of the times series’ RMS decreased, and most of the uncertainty associated with the computed SLR decreased. We confirm that the spatiotemporal correlations should be accounted for to yield trustworthy trends and reliable uncertainties. Our estimated SLR is 2.75 ± 0.89 mm/yr, which aligns closely with recent studies, emphasizing the robustness and consistency of our method using virtual SA stations. We additionally introduce open-source software (SA_Tool V1.0) to process the SA data and reduce noise in surface height time series to the community. Full article

This study demonstrates the conversion of metallic titanium (Ti) to titanium oxide just by conducting electrical current through Ti thin film in vacuum and increasing the temperature by Joule heating. This led to the improvement of electrical and thermal properties of a microbolometer. A microbolometer with an integrated Ti thermistor and heater width of 2.7 µm and a length of 50 µm was fabricated for the current study. Constant-voltage stresses were applied to the thermistor wire to observe the effect of the Joule heating on its properties. Thermistor resistance ~14 times the initial resistance was observed owing to the heating. A negative large temperature coefficient of resistance (TCR) of −0.32%/K was also observed owing to the treatment, leading to an improved responsivity of ~4.5 times from devices with untreated Ti thermistors. However, this does not improve the noise equivalent power (NEP), due to the increased flicker noise. Microstructural analyses with transmission electron microscopy (TEM), transmission electron diffraction (TED) and energy dispersive X-ray (EDX) confirm the formation of a titanium oxide (TiO_x) semiconducting phase on the Ti phase (~85% purity) deposited initially, further to the heating. Formation of TiO_x during annealing could minimize the narrow width effect, which we reported previously in thin metal wires, leading to enhancement of responsivity. Full article

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS₂, WS₂, and ZrS₃) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing. Full article

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA). Full article

Stochastic computing (SC) is widely known for its high error tolerance and efficient computing ability of complex functions with remarkably simple logic gates. The noise of electronic devices is widely used to be the entropy source due to its randomness. Compared with thermal noise and random telegraph noise (RTN), flicker noise is favored by researchers because of its high noise density. Meanwhile, unlike using RRAM, PCRAM and other emerging memory devices as the entropy source, using logic devices does not require any additional process steps, which is significant for industrialization. In this work, we systematically and statistically studied the 1/f noise characteristics of 14 nm FinFET, and found that miniaturizing the channel area of the device or lowering the ambient temperature can effectively increase the 1/f noise density of the device. This is of great importance to improve the accuracy of the SC system and simplify the complexity of the stochastic number generator (SNG) circuit. At the same time, these rules of 1/f noise characteristics in FinFET devices can provide good guidance for our device selection in circuit design. Full article

We propose a new concept image sensor suitable for viewing and sensing applications. This is a report of a CMOS image sensor with a pixel architecture consisting of a 1.5 μm pixel with four-floating-diffusions-shared pixel structures and a 3.0 μm pixel with an in-pixel capacitor. These pixels are four small quadrate pixels and one big square pixel, also called quadrate–square pixels. They are arranged in a staggered pitch array. The 1.5 μm pixel pitch allows for a resolution high enough to recognize distant road signs. The 3 μm pixel with intra-pixel capacitance provides two types of signal outputs: a low-noise signal with high conversion efficiency and a highly saturated signal output, resulting in a high dynamic range (HDR). Two types of signals with long exposure times are read out from the vertical pixel, and four types of signals are read out from the horizontal pixel. In addition, two signals with short exposure times are read out again from the square pixel. A total of eight different signals are read out. This allows two rows to be read out simultaneously while reducing motion blur. This architecture achieves both an HDR of 106 dB and LED flicker mitigation (LFM), as well as being motion-artifact-free and motion-blur-less. As a result, moving subjects can be accurately recognized and detected with good color reproducibility in any lighting environment. This allows a single sensor to deliver the performance required for viewing and sensing applications. Full article

A new method to measure laser phase noise and frequency stability based on the phase-sensitive optical time-domain reflectometry is proposed. In this method, the laser under test is utilized in a phase-sensitive optical time-domain reflectometer, which employs phase-modulated dual pulses and acts as an optical frequency discriminator: laser frequency fluctuations are deduced from the analysis of the reflectometer data corresponded to phase fluctuations along the vibration-damped and thermally insulated fiber spool. The measurement results were validated by comparison with direct optical heterodyning of the tested and more coherent reference lasers. The use of dual pulses generated by an acousto-optic modulator makes it easy to adjust the time delay during measurements, which distinguishes favorably the proposed method from standard optical frequency discriminators. The method is suitable for testing highly coherent lasers and qualifying their parameters such as linear drift rate, random frequency walk rate, white frequency noise (which is directly related to laser instantaneous linewidth), and flicker noise level. Full article

In this work, the degradation of the random telegraph noise (RTN) and the threshold voltage ($V_t$) shift of an 8.3Mpixel stacked CMOS image sensor (CIS) under hot carrier injection (HCI) stress are investigated. We report for the first time the significant statistical differences between these two device aging phenomena. The $V_t$ shift is relatively uniform among all the devices and gradually evolves over time. By contrast, the RTN degradation is evidently abrupt and random in nature and only happens to a small percentage of devices. The generation of new RTN traps by HCI during times of stress is demonstrated both statistically and on the individual device level. An improved method is developed to identify RTN devices with degenerate amplitude histograms. Full article

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time–frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (−10 dB). Field experiments indicate that this approach can effectively reduce clutter power (−15 dB) and successfully extract micro-Doppler signals for identifying different UAVs. Full article

This paper presents an injection locked digitally controlled ring oscillator (IL-DCRO). To reduce jitter variations, minimize oscillator spurious signals, and eliminate periodical phase error, a double edge-injection (window injection) scheme with synchronized edge directions is proposed. A combinational edge generator is utilized to substitute the sequential edge generators for injection timing requirements relaxation. By biasing devices in deep triode, digitally controlled delay cells currents are adopted for frequency tuning. This helps reducing the devices flicker (1/f) noise and minimize the DCRO overall phase noise. At 1 MHz offset of frequency, the proposed oscillator has a measured phase noise of −125.95 dBc/Hz and −115.6 dBc/Hz at oscillation frequencies of 913.4 MHz and 432.6 MHz, respectively. Fabricated in 350 nm CMOS process, with a maximum power consumption of 3.3 mW, and oscillating at 913.4 MHz, this DCRO achieves a tuned oscillator figure of merit (FoM) of −197.35 dBc/Hz. The core area of this edge-injection-based DRCO is only 0.08 mm². Full article

This paper presents the design and testing of an ultra-low-noise transimpedance amplifier (TIA) for low-frequency noise measurements on low-impedance (below 1 kΩ) devices, such as advanced IR photodetectors. When dealing with low-impedance devices, the main source of background noise in transimpedance amplifiers comes from the equivalent input voltage noise of the operational amplifier, which is used in a shunt–shunt configuration to obtain a transimpedance stage. In our design, we employ a hybrid operational amplifier in which an input front end based on ultra-low-noise discrete JFET devices is used to minimize this noise contribution. When using IF3602 JFETs for the input stage, the equivalent voltage noise of the hybrid operational amplifier can be as low as 4 nV/√Hz, 2 nV/√Hz, and 0.9 nV/√Hz at 1 Hz, 10 Hz, and 1 kHz, respectively. When testing the current noise of an ideal 1 kΩ resistor, these values correspond to a current noise contribution of the same order as or below that of the thermal noise of the resistor. Therefore, in cases in which the current flicker noise is dominant, i.e., much higher than the thermal noise, the noise contribution from the transimpedance amplifier can be neglected in most cases of interest. Test measurements on advanced low-impedance photodetectors are also reported to demonstrate the effectiveness of our proposed approach for directly measuring low-frequency current noise in biased low-impedance electronic devices. Full article

Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevitably present in the camera’s output results. This noise can degrade the camera’s perception of events and the performance of algorithms for processing event streams. Moreover, since the output of event cameras is in the form of address-event representation, efficient denoising methods for traditional frame images are no longer applicable in this case. Most existing denoising methods for event cameras target background activity noise and sometimes remove real events as noise. Furthermore, these methods are ineffective in handling noise generated by high-frequency flickering light sources and changes in diffused light reflection. To address these issues, we propose an event stream denoising method based on salient region recognition in this paper. This method can effectively remove conventional background activity noise as well as irregular noise caused by diffuse reflection and flickering light source changes without significantly losing real events. Additionally, we introduce an evaluation metric that can be used to assess the noise removal efficacy and the preservation of real events for various denoising methods. Full article

Accurate noise model identification for GNSS time series is crucial for obtaining a reliable GNSS velocity field and its uncertainty for various studies in geodynamics and geodesy. Here, by comprehensively considering time span and missing data effect on the noise model of GNSS time series, we used four combined noise models to analyze the duration of the time series (ranging from 2 to 24 years) and the data gap (between 2% and 30%) effects on noise model selection and velocity estimation at 72 GNSS stations spanning from 1992 to 2022 in global region together with simulated data. Our results show that the selected noise model have better convergence when GNSS time series is getting longer. With longer time series, the GNSS velocity uncertainty estimation with different data gaps is more homogenous to a certain order of magnitude. When the GNSS time series length is less than 8 years, it shows that the flicker noise and random walk noise and white noise (FNRWWN), flicker noise and white noise (FNWN), and power law noise and white noise (PLWN) models are wrongly estimated as a Gauss–Markov and white noise (GGMWN) model, which can affect the accuracy of GNSS velocity estimated from GNSS time series. When the GNSS time series length is more than 12 years, the RW noise components are most likely to be detected. As the duration increases, the impact of RW on velocity uncertainty decreases. Finally, we show that the selection of the stochastic noise model and velocity estimation are reliable for a time series with a minimum duration of 12 years. Full article