Transient Signals Research Articles

Pipeline leakage localization method based on transient signal detection and data fusion from multi-sensor

ABSTRACT Pipeline leaks pose significant risks to industries, the environment, and individuals, so localizing pipeline leaks is crucial for enhancing pipeline safety during operation. This paper applies a method for localizing pipeline leakages, integrating transient signal detection with multi-sensor data fusion. Addressing the challenges in detecting small leaks amidst strong noise and uncertainty, the method employs the Dempster–Shafer evidence framework for data fusion and an algorithm to analyze transient pressure waves. Comparing it with three spectrum-based methods, the performance of the fusion method is discussed in the single-leakage and multi-leakage cases. In the single-leakage case, even with high levels of noise, the fusion algorithm delivers precise localization estimates. The fusion method excels over the other three methods in the multi-leakage case. The approach significantly enhances the accuracy of leak localization in water pipelines. Extensive simulations demonstrate the method's effectiveness, particularly in noisy environments, offering a promising solution for maintaining pipeline integrity and reducing resource wastage.

Coherent Ultrafast Stimulated X-Ray Raman Spectroscopy of Dissipative Conical Intersections.

The quantum coherent dynamics of a vibronic wave packet in a molecule passing through a conical intersection can be revealed using attosecond transient coherent Raman spectroscopy. In particular, the time evolution of the electronic coherence can be monitored in the presence of vibrational dynamics. So far, the technique has been investigated without including environmental quantum noise. Here, we employ the numerically exact hierarchy equation of motion approach to show that the transient coherent Raman signals are robust and accessible on times of up to a few hundred femtoseconds with respect to electonic and vibrational dephasing.

Collision cross-section measurements of small molecules via transient decay profiles observed in Orbitrap mass analyzers.

Collision cross section (CCS) of organic compounds can be measured via Fourier transform-based mass spectrometry (MS) by modeling the decay rate of transient signals in the analyzer. Deriving CCS values of low-mass molecules (mass < 2000 Da and CCS < 500 Å2) with Orbitrap MS is challenging due to their high axial frequencies and small absolute variances in cross-sectional profiles. Here, we acquired mass spectra of progressively more complex low-mass analytes using commercial Orbitrap mass spectrometers. The transient signals were processed using Fast Fourier transform (FFT) and short-time Fourier transform (StFFT) to derive decay constants of multiple select ionic species from a single MS full-scan experiment. Decay constants were translated into CCS values using at least two internal standards in the same mass spectrum. Our results suggest target ionic species should have high S/N in order to derive CCS values with ≤0.5% uncertainty. Limitations in the precision of CCS measurements reflect local space charge effects that disturb ion motion in the analyzer. The derived CCS values of polymer like fragments of Ultramark 1621 and small molecules such as individual protonated amino acids can achieve average ±1% error with selection of internal standards across a wide mass range. Future studies need to optimize the strategy to select internal standards in order to improve the precision and accuracy of CCS measurements for small molecules via Orbitrap MS.

High-frequency transients in buried insulated wires: Transmission line simulations and experimental validation

This paper presents experimental results on the transient response of a buried insulated wire subjected to fast transient signals. It reports measurements of voltage and current at the sending end, as well as the voltage at the receiving end of the insulated wire. The obtained experimental results are utilized to assess the accuracy of recent formulations proposed for computing the ground-return impedance and admittance of underground cables, which are important for simulation of transients using models based on the transmission line theory. The good agreement between measurements and simulations bolsters confidence in incorporating these newly proposed expressions for computing the cable's ground-return parameters into EMT-type simulators.

Artificial Amacrine Retinal Circuits.

Event-based imaging represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current complementary metal oxide semiconductor-based machine vision. Realizing such imaging systems has previously been sought using very large-scale integration technologies that have complex circuitries consisting of many photodiodes, differential amplifiers, capacitors, and resistors. Here, we demonstrate that event-driven sensing can be achieved using a simple one-resistor, one-capacitor (1R1C) circuit, where the capacitor is modified with colloidal quantum dots (CQDs) to have a photoresponse. This sensory circuit emulates the motion-tracking function of the biological retina, in which the amacrine cells in the bipolar-to-ganglion synaptic pathway produce a transient spiking signal only in response to changes in light intensity but remain inactive under constant illumination. When extended to a 2D imaging array, the individual sensors work independently and output signals only when a change in the light intensity is detected; hence, the concept of the frame in image processing is thereby removed. In this work, we present the fabrication and characterization of a CQD photocapacitor-based 1R1C circuit that has a spectral response at 1550 nm in the short-wave infrared (SWIR). We report on the key performance parameters including peak responsivity, noise, and optical noise equivalent power and discuss the operating mechanism that is responsible for spiking responses in these artificial retinal circuits. The present work sets the foundation for expanding the bioinspired vision sensor capability toward midwave infrared (MWIR) and long-wave infrared (LWIR) spectral regions that are invisible to human eyes and mainstream semiconductor technologies.

Transient road signals simulation strategy in a Stewart platform

In this article, a transient road signals simulation strategy in a Stewart platform is presented. Real world road signals are transient signals and thus the accurate simulation of its frequency, amplitude and damping components is key for further simulation of comfort. This simulation strategy combines a kinematic model and a transient signal reconstruction strategy using an autoregressive method. First, the developed experimental kinematic model on a Stewart platform is presented. Then, the procedure to simulate real-world transient road signals in the driving simulator is explained. Finally, the developed strategy has been validated under real road signals combined in the 6 degrees-of-freedom to prove the accuracy between the real world experimental measured signal and the simulated signal in the hexapod. The final goal of the presented transient road signals simulation strategy is to perform further vehicle dynamics and ride and comfort studies in the simulation phase using electric Stewart platforms.

Minimally Destabilizing Corridor for Resection of Dumbbell Nerve Sheath Tumors: A Novel Surgical Technique.

Current surgical strategies for dumbbell nerve sheath tumors (DNSTs) with cord compression have primarily involved wide spinal exposures with total laminectomy and unilateral facetectomy, often leading to spinal destabilization and requiring fusion, or staged procedures separately addressing the intraspinal and extraforaminal tumor components. This study highlights technical nuances of a novel approach for DNST resection to minimize spinal destabilization and avoid fusion while facilitating safe, single-stage complete resection. A retrospective chart review was conducted on patients undergoing DNST resection. Using unilateral subperiosteal dissection, hemilaminotomy and medial facetectomy procedures are performed. The extradural tumor component is resected, followed by internal decompression of the intradural tumor. A small horizontal incision at the origin of the nerve root sleeve releases the underlying dural stricture, facilitating delivery of the remaining intradural tumor and allowing section of the nerve root of origin. Ultrasonography confirms complete tumor resection and return of cord pulsation, and excludes intradural hemorrhagic complications. The dura is reconstructed using a dural substitute bolstered with fat graft and sealant. Twelve consecutive patients undergoing this approach from 2014 to 2021 were included. Mean patient age was 53.5 years, and 58.3% were male. Nine tumors were cervical and 3 were lumbar. Five patients presented with myelopathy, 4 with radiculopathy, and 4 with axial pain. Two cases had transient intraoperative neuromonitoring signal changes. Eleven tumors were diagnosed as schwannomas and 1 as neurofibroma. All patients had complete resection of the intraspinal component; 2 had far distal extraforaminal residual. No patient has had recurrence, progression of residual, or signs of spinal instability during follow-up (median 28.5 months, range 6-66 months). This study highlights technical considerations for DNST resection, focusing the approach at the center of the tumor, with minimal bone removal and ligamentous disruption. Intraoperative ultrasound is instrumental in the safety of this approach.

Locating Insulation Defects in HV Substations Using HFCT Sensors and AI Diagnostic Tools.

In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown.

Horizontal rearrangement frequency domain chirplet transform: algorithm and applications

Abstract Based on the idea of optimal time–frequency analysis (TFA) theory, a time–frequency post-processing method called horizontal rearrangement frequency domain chirplet transform (HRFCT) is proposed. The proposed method is designed to analyze non-stationary transient signals with nonlinear group delay (GD), aiming at obtaining energy-concentrated and accurate time–frequency representation (TFR). First, based on the image characteristics of the constructed amplitude function, the selection criteria of GD are defined, that is, the local maximum of amplitude function and the local minimum of absolute value of derivative of amplitude function are calculated. Next, according to the calculated GD, the coefficients on the GD trajectory are adaptively extracted, and the coefficients that cause the TFR to be blurred are discarded. Finally, the analysis result of a two-component simulation signal demonstrates the satisfactory performance of the HRFCT, and compared with four advanced TFA methods, the competitiveness of the HRFCT is verified. Additionally, the application of the HRFCT in the processing of constant and variable speed bearing signals highlights its prospects in transient signal analysis and rotating machinery condition monitoring.

(Invited) Disentangling the Nonequilibrium Dynamics of Excitations in 2D Material Interfaces Under Bias: Leveraging Theory, Electrochemistry, and Time-Resolved Spectroscopy

A fundamental challenge in elucidating, controlling, and exploiting nonequilibrium relaxation in condensed phase systems is the ability to find and employ physically transparent models. Through such models, one can develop a compelling assignment and interpretation of the spectral signatures of processes spanning charge and energy transfer, quasiparticle formation, and even chemical reactivity. Two-dimensional materials, such as transition metal dichalcogenides (TMDs), offer one such challenge, especially under applied fields. In this talk, I will describe our recent work combining theory, electrochemical measurements, and ultrafast spectroscopy to demonstrate hot carrier extraction in photoelectrochemical cells composed of monolayer MoS2 on an ITO electrode exposed to electrolyte solution. In addition, I will discuss our critical assessment of the ability of a physically intuitive Hamiltonian, consisting of an infinitely heavy exciton immersed in a fermi sea of conduction band electrons, to capture and offer an interpretation of the spectral features in the linear and transient absorption optical signals of this material under an applied bias. Importantly, we leverage our analysis to identify, physically, how trion formation moves, broadens, and resizes the “A exciton” absorption of our working device. We further unify the interpretation over various sources of such TMD spectral shifts: applied bias, fluence, and (TA) delay time. Our work thus demonstrates hot carrier extraction in 2D photoelectrochemical cells, outlines a path to exploit this phenomenon in semiconductor devices, delineates open questions that are only now becoming possible to address with theory and experiment, and identifies the underlying physical process responsible for previously misidentified spectral features in 2D materials that changed with applied voltage, fluence, and time.

A 640 nA IQ Output-Capacitor-Less Low Dropout (LDO) Regulator with Sub-Threshold Slew-Rate Enhancement for Narrow Band Internet of Things (NB-IoT) Applications.

An ultra-low quiescent current output-capacitor-less low dropout (OCL-LDO) regulator for power-sensitive applications is proposed in this paper. To improve the gain of the OCL-LDO feedback loop, the error amplifier employs a combination of a cross-coupled input stage for boosting the equivalent input transconductance and a negative resistance technique to improve the gain. Meanwhile, in order to address the issue of transient response of the ultra-low quiescent current OCL-LDO, a sub-threshold slew-rate enhancement circuit is proposed in this paper, which consists of a transient signal input stage and a slew-rate current increase branch. The proposed OCL-LDO is fabricated in a 0.18 μm CMOS process with an effective area of 0.049 mm2. According to the measurement results, the proposed OCL-LDO has a maximum load current of 100 mA and a minimum quiescent current of 640 nA at an input voltage of 1.2 V and an output voltage of 1 V. The overshoot and undershoot voltages are 197 mV and 201 mV, respectively, and the PSR of the OCL-LDO is -72.4 dB at 1 kHz when the load current is 100 μA. In addition, the OCL-LDO has a load regulation of 7.6 μV/mA and a line regulation of 0.87 mV/V.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

Ultrafast Scintillator Based on Zirconium‐Doped Cesium Zinc Chloride Single Crystals and Their Charge Carrier Dynamics

AbstractScintillators have been widely used for high‐energy radiation imaging. It is in great demand and challenge to develop scintillator materials with fast decay time, high scintillation light yield, and low detection limit for high‐resolution imaging applications such as time‐of‐flight positron emission tomography. However, it is still a challenge to develop the ultrafast carrier dynamics to achieve 100% ultrafast decay time with high scintillation light yield. To meet the demand, a series of component‐tunable Cs2ZnCl4: x%Zr single crystals as promising ultrafast scintillators is successfully developed. With 8% of Zr‐dopant (Cs2ZnCl4: 8%Zr), the single crystal exhibits high light yield (28000 photons MeV−1) and low detection limit (51 nGy s−1) under X‐ray excitation. Photo‐induced transient absorption signals on sub‐nanosecond and nanosecond scales ensure almost 100% fast decay time (≈3 ns) in nanoseconds under γ‐ray excitation. In particular, the different radiative transition processes are achieved under ultraviolet and high‐energy radiation due to the tunable carrier dynamics from the shallow trapped state to the self‐trapped exciton (STE) state.

Efficient design of complex-valued neural networks with application to the classification of transient acoustic signals.

A paper by the current authors Paul and Nelson [JASA Express Lett. 3(9), 094802 (2023)] showed how the singular value decomposition (SVD) of the matrix of real weights in a neural network could be used to prune the network during training. The paper presented here shows that a similar approach can be used to reduce the training time and increase the implementation efficiency of complex-valued neural networks. Such networks have potential advantages compared to their real-valued counterparts, especially when the complex representation of the data is important, which is the often case in acoustic signal processing. In comparing the performance of networks having both real and complex elements, it is demonstrated that there are some advantages to the use of complex networks in the cases considered. The paper includes a derivation of the backpropagation algorithm, in matrix form, for training a complex-valued multilayer perceptron with an arbitrary number of layers. The matrix-based analysis enables the application of the SVD to the complex weight matrices in the network. The SVD-based pruning technique is applied to the problem of the classification of transient acoustic signals. It is shown how training times can be reduced, and implementation efficiency increased, while ensuring that such signals can be classified with remarkable accuracy.

Simultaneous tracking of ultrafast surface and gas-phase dynamics in solid-gas interfacial reactions.

Real-time detection of intermediate species and final products at the surface and near-surface in interfacial solid-gas reactions is critical for an accurate understanding of heterogeneous reaction mechanisms. In this article, an experimental method that can simultaneously monitor the ultrafast dynamics at the surface and above the surface in photoinduced heterogeneous reactions is presented. This method relies on a combination of mass spectrometry and femtosecond pump-probe spectroscopy. As a model system, the photoinduced reaction of methyl iodide on and above a cerium oxide surface is investigated. The species that are simultaneously detected from the surface and gas-phase present distinct features in the mass spectra, such as a sharp peak followed by an adjacent broad shoulder. The sharp peak is attributed to the species detected from the surface, while the broad shoulder is due to the detection of gas-phase species above the surface, as confirmed by multiple experiments. By monitoring the evolution of the sharp peak and broad shoulder as a function of the pump-probe time delay, transient signals are obtained that describe the ultrafast photoinduced reaction dynamics of methyl iodide on the surface and in the gas-phase. Finally, SimION simulations are performed to confirm the origin of the ions produced on the surface and in the gas-phase.

Psychoacoustic model for detecting the sensation of impulsivity in acoustic signals from refrigerators

This study investigates the perception of impulsivity in audio signals specifically caused by thermal expansion (cracking noise) in domestic refrigerators. It employs a multifaceted approach encompassing an analysis of sensitivity to impulsive events, an assessment of the prominence of impulse signals, and a correlation analysis of these data. The investigation revealed different responses to various sound samples, highlighting subjective variations. Using linear regression, correlation analysis demonstrated a robust and positive relationship (Pearson correlation coefficient of 0.851) between perceived impulsivity and the Impulsivity Prediction Model (IPM). This alignment underscores the reliability of the developed IPM in capturing and predicting subjective perceptions of low-amplitude transient signals. Comparisons between groups of participants, conducted using both Analysis of Variance (ANOVA) and t-tests, explored potential disparities related to gender, age, and acoustic knowledge. The results indicated no statistically significant differences in the perception of impulsivity concerning gender, age groups, or acoustic knowledge. In conclusion, this study provides insights into the perceptual aspects of impulsivity in audio signals from home refrigerators, specifically addressing thermal expansion noises, and establishes the reliability of the Impulsivity Prediction Model (IPM) as a tool for objective assessment. The congruence between subjective judgments and objective metrics enhances the applicability of IPM in diverse fields, from acoustic engineering to psychoacoustic research.

Laser-Induced Interference to Infrared Detector Using Continuous Wave and Short-Pulse Lasers.

The response of a DPbS3200 infrared detector irradiated by a nanosecond pulsed laser and CW laser has been investigated to study laser-induced interference. A laser interference experiment system was constructed to measure the time-varying response signal. A nanosecond pulsed laser and a CW laser of 10 Hz were used, with a 1064 nm wavelength and a millimeter-scale irradiation spot diameter. Firstly, the characteristics of transient interference signals induced by pulsed lasers were analyzed. Then, the characteristics of response signal interference by both CW laser and pulsed laser irradiation were further investigated. The results showed that the pulsed laser only produced transient interference. However, the CW laser led to a significant amplitude reduction of the response signal, which could continuously interfere in the operating time. For transient interferences, the amplitude of the interference signal increased linearly with the laser fluence. The relation between the pulse repetition rate of the incident laser and the operating frequency of the detector determined the numbers of transient interference signals in one response period; for the interference induced by both the CW laser and pulsed laser, CW laser interference played a leading role when CW laser power density increased to 4.1 W/cm2 or more. As the CW laser fluence reached 6.1 W/cm2, the PbS infrared detector was no longer able to detect any signal, which caused temporary blindness. In the end, a probit model was used to determine the interference threshold.

Spiking neural networks for nonlinear regression of complex transient signals on sustainable neuromorphic processors

In recent years, spiking neural networks were introduced in science as the third generation of artificial neural networks leading to a tremendous energy saving on neuromorphic processors. This sustainable effect is due to the sparse nature of signal processing in-between spiking neurons leading to much less scalar multiplications as in second-generation networks. The spiking neuron’s efficiency is even more pronounced by their inherently recurrent nature being useful for recursive function approximations. We believe that there is a need for a general regression framework for SNNs to explore the high potential of neuromorphic computations. However, besides many classification studies with SNNs in the literature, nonlinear neuromorphic regression analysis represents a gap in research. Hence, we propose a general SNN approach for function approximation applicable for complex transient signal processing taking surrogate gradients due to the discontinuous spike representation into account. However, to pay attention to the need for high memory access during deep SNN network communications, additional spiking Legrendre Memory Units are introduced in the neuromorphic architecture. Path-dependencies and evolutions of signals can be tackled in this way. Furthermore, interfaces between real physical and binary spiking values are necessary. Following this intention, a hybrid approach is introduced, exhibiting an autoencoding strategy between dense and spiking layers. However, to verify the presented framework of nonlinear regression for a wide spectrum of scientific purposes, we see the need for obtaining realistic complex transient short-time signals by an extensive experimental set-up. Hence, a measurement technique for benchmark experiments is proposed with high-frequency oscillations measured by capacitive and piezoelectric sensors resulting in wave propagations and inelastic solid deformations to be predicted by the developed SNN regression analysis. Hence, the proposed nonlinear regression framework can be deployed to a wide range of scientific and technical applications.

High-Spin State of a Ferrocene Electron Donor Revealed by Optical and X-ray Transient Absorption Spectroscopy.

Ferrocene is one of the most common electron donors, and mapping its ligand-field excited states is critical to designing donor-acceptor (D-A) molecules with long-lived charge transfer states. Although 3(d-d) states are commonly invoked in the photophysics of ferrocene complexes, mention of the high-spin 5(d-d) state is scarce. Here, we provide clear evidence of 5(d-d) formation in a bimetallic D-A molecule, ferrocenyl cobaltocenium hexafluorophosphate ([FcCc]PF6). Femtosecond optical transient absorption (OTA) spectroscopy reveals two distinct electronic excited states with 30 and 500 ps lifetimes. Using a combination of ultraviolet, visible, near-infrared, and short-wave infrared probe pulses, we capture the spectral features of these states over an ultrabroadband range spanning 320 to 2200 nm. Time-dependent density functional theory (DFT) calculations of the lowest triplet and quintet states, both primarily Fe(II) (d-d) in character, qualitatively agree with the experimental OTA spectra, allowing us to assign the 30 ps state as the 3(d-d) state and the 500 ps state as the high-spin 5(d-d) state. To confirm the ferrocene-centered high-spin character of the 500 ps state, we performed X-ray transient absorption (XTA) spectroscopy at the Fe and Co K edges. The Fe K-edge XTA spectrum at 150 ps shows a red shift of the absorption edge that is consistent with an Fe(II) high-spin state, as supported by ab initio calculations. The transient signal detected at the Co K-edge is 50× weaker, confirming the ferrocene-centered character of the excited state. Fitting of the transient extended X-ray absorption fine structure region yields an Fe-C bond length increase of 0.25 ± 0.1 Å in the excited state, as expected for the high-spin state based on DFT. Altogether, these results demonstrate that the high-spin state of ferrocene should be considered when designing donor-acceptor assemblies for photocatalysis and photovoltaics.

A 0.91-[formula omitted]A-quiescent-current capacitor-less LDO with sub-threshold transient enhancement and bulk-driven feed-forward supply ripple cancellation

This paper presents a capacitor-less low-dropout (OCL-LDO) regulator that features a low quiescent current for low power applications. To enhance the transient response of proposed OCL-LDO, a sub-threshold transient enhancement circuit including a transient signal input stage, a current subtractor, and a current amplifier is suggested. Meanwhile, a circuit made up of a bulk-driven ripple feed-forward circuit and a ripple introduction circuit is developed in order to improve PSR of the OCL-LDO. The proposed OCL-LDO is fabricated in 0.18 μm standard CMOS process and has an active area of 0.053 mm2. Based on measurement results, the proposed OCL-LDO has a maximum load current of 100 mA at 1.2 V input and 1 V output, and a minimum quiescent current of 0.91 μA. The overshoot voltage and undershoot voltage are 199 mV and 204 mV, respectively. PSR of the OCL-LDO is −71.8 dB at 1 KHz when the load current is 100 mA. Furthermore, the OCL-LDO exhibits a load regulation of 11.1 μV/mA and a line regulation of 1.05 mV/V.