Balanced Detection Research Articles

Clinical application of chromosome microarray analysis and karyotyping in prenatal diagnosis in Northwest China

IntroductionKaryotyping and chromosome microarray analysis (CMA) are the two main prenatal diagnostic techniques currently used for genetic testing. We aimed to evaluate the value of chromosomal karyotyping and CMA for different prenatal indications.MethodsA total of 2084 amniocentesis samples from pregnant women who underwent prenatal diagnosis from 16 to 22 + 6 weeks of gestation between January 2021 and December 2022 were retrospectively collected. The pregnant women were classified according to different prenatal diagnostic indications and underwent CMA and karyotype analysis. Clinical data were collected, and the results of the CMA and karyotype analysis were statistically analyzed to compare the effects of the two diagnostic techniques.ResultsThe total detection rate of abnormal chromosomes was significantly higher using CMA than karyotype analysis. The detection rate of abnormal chromosomes using CMA was significantly higher than that using karyotyping for ultrasound abnormalities, high-risk serologic screening, adverse pregnancy history, positive noninvasive prenatal test (NIPT) screening, and ultrasound abnormalities combined with adverse pregnancy history indications. Among the fetuses with inconsistent results between the two testing methods, 144 had an abnormal CMA but a normal karyotype, with the highest percentage of pregnant women with ultrasound abnormalities at 38.89% (56/144). CMA had the highest detection rate for structural abnormalities combined with soft-index abnormalities among all ultrasound abnormalities. The highest detection rate of copy number variants in the group of structural abnormalities in a single system was in the genitourinary system (3/29, 10.34%).ConclusionCMA can improve the detection rate of chromosomal abnormalities in patients with ultrasound abnormalities, high-risk serologic screening, adverse maternal history, positive NIPT screening, and ultrasound abnormalities combined with adverse maternal history and can increase the detection rate of chromosomal abnormalities in karyotypic normality by 6.91% (144/2,084), this result is higher than similar studies. However, karyotype analysis remains advantageous over CMA regarding balanced chromosomal rearrangement and detection of low-level chimeras, and the combination of the two methods is more helpful in improving the detection rate of prenatal chromosomal abnormalities.

Cavity-enhanced Faraday rotation spectroscopy for interference-free measurement of OH radical at 2.8 μm

An instrument based on cavity-enhanced Faraday rotation spectroscopy (CE-FRS) operating at 2.8 μm has been developed for interference-free measurement of OH radicals in the laboratory. By off-axis coupling of a continuous-wave laser into a high finesse optical cavity, FRS signal is obtained from balanced detection of time-integrated light intensity leaking out of the cavity in the presence of magnetic field. Radio-frequency white noise (5–520 MHz) was injected into laser current which reduced intensity fluctuations in cavity transmission, thus improved the signal-to-noise ratio of the spectroscopic signal by a factor of 2. The setup provides a simple and robust spectroscopic instrument for in-situ and highly-selective detection of paramagnetic species. We demonstrated the instrument’s capabilities using OH radical with concentration in the range of 1012 molecule.cm−3, generated by microwave discharge of water vapor at low pressure. The CE-FRS instrument exhibited a limit of detection of ∼ 1010 molecule.cm−3 in an integration time of 20 s, which is enhanced by a factor of 2.5 compared to cavity-enhanced wavelength modulation spectroscopy involving an off-axis integrated cavity output spectroscopy approach. A time-resolved FRS signal was recorded in a pulsed microwave discharge regime, giving a millisecond time resolution for the measurement of OH concentration profile. The developed instrument provides a potential analytical tool for the measurement of OH concentration for chemical kinetic study in reactor cells.

Improving phase sensitivity of a hybrid interferometer with the two-mode squeezed coherent state

We investigate the enhancement of phase sensitivity of a nonlinear-linear hybrid interferometer with the input of the two-mode squeezed coherent state (TMSCS). With the TMSCS produced by four-wave mixing, the quantum Cramér-Rao bounds (QCRB) can beat the Heisenberg limit (HL). Under the phase matched conditions, the optimal phase sensitivity with the balanced homodyne detection measurement can beat the HL and approach QCRB. The effects of internal and external losses on the measurement accuracy are also discussed. The results demonstrate that the scheme is robustness against to internal losses and this protocol can resist external detection loss which is up to 39%. Our results improve the performance of hybrid interferometers and this scheme can find important practical applications in quantum metrology.

Robust algorithm for automatic surface-based outlier detection in MBES point clouds

Bathymetric multibeam echosounder systems (MBES) provide high-resolution mapping of underwater topography but are highly susceptible to errors due to harsh environmental conditions and the measurement process. Traditionally, manual post-processing is required to ensure data quality, a time-consuming, expensive, and subjective task. To address this issue, we propose a surface-based algorithm for pre-processing and cleaning MBES data that reduces manual intervention and improves consistency. A surface-based algorithm models the underwater topography as a surface instead of processing individual points. By assuming a continuous surface for underwater geometry, the algorithm easily identifies observations that deviate significantly from this model. The method combines a hierarchical B-spline surface with iterative robust estimation to automate data cleaning. Preliminary results on example datasets show a balanced outlier detection accuracy of 0.99, with manual processing time reduced from 2 days to just 30 min.

Balanced air-biased detection of terahertz waveforms.

A novel, to the best of our knowledge, balanced air-biased coherent detection scheme for capturing ultrabroadband terahertz (THz) waveforms is implemented. The balanced detection scheme allows for coherent detection at the full repetition rate of the laser system without requiring bias modulation, signal generators, or lock-in amplifiers while doubling the dynamic range and quadrupling the signal-to-noise ratio compared to conventional air-biased coherent detection. These advantages are achieved by rotating the bias electrodes by 90° relative to the conventional scheme. With a 1 kHz driving laser, the scheme enables sub-second, high-fidelity waveform acquisition with a continuously moving delay stage, demonstrated by collecting 200 waveforms in 100 s. The balanced detection scheme paves the way for much faster and higher quality 2D ultrabroadband terahertz spectroscopy.

Quantum phase estimation and realistic detection schemes in Mach-Zehnder interferometer using SU(2) coherent states

In quantum parameter estimation, the quantum Cramér-Rao bound (QCRB) sets a fundamental limit on the precision achievable with unbiased estimators. It relates the uncertainty in estimating a parameter to the inverse of the quantum Fisher information (QFI). Both QCRB and QFI are valuable tools for analyzing interferometric phase sensitivity. This paper compares the single-parameter and two-parameter QFI for a Mach-Zehnder interferometer (MZI) with three detection schemes: single-mode and difference intensity detection, neither has access to an external phase reference and balanced homodyne detection with access to an external phase reference. We use a spin-coherent state associated with the su(2) algebra as the input state in all scenarios and show that all three schemes can achieve the QCRB for the spin-coherent input state. Furthermore, we explore the utilization of SU(2) coherent states in diverse scenarios. Significantly, we find that the highest achievable precision occurs when the total angular momentum quantum number, j, is high. We further demonstrate that under these optimal conditions, all detection schemes can achieve the QCRB by utilizing SU(2) coherent states as input states.

Assessing actigraphy performance for daytime sleep detection following stroke: insights from inpatient monitoring in a rehabilitation hospital.

Stroke can result in or exacerbate various sleep disorders. The presence of behaviors such as daytime sleepiness poststroke can indicate underlying sleep disorders which can significantly impact functional recovery and thus require prompt detection and monitoring for improved care. Actigraphy, a quantitative measurement technology, has been primarily validated for nighttime sleep in healthy adults; however, its validity for daytime sleep monitoring is currently unknown. Therefore this study aims to identify the best-performing actigraphy sensor and algorithm for detecting daytime sleep in poststroke individuals. Participants wore Actiwatch Spectrum and ActiGraph wGT3X-BT on their less-affected wrist, while trained observers recorded daytime sleep occurrences and activity levels (active, sedentary, and asleep) during non-therapy times. Algorithms, Actiwatch (Autoscore AMRI) and ActiGraph (Cole-Kripke, Sadeh), were compared with on-site observations and assessed using F2 scores, emphasizing sensitivity to detect daytime sleep. Twenty-seven participants from an inpatient stroke rehabilitation unit contributed 173.5 hours of data. The ActiGraph Cole-Kripke algorithm (minute sleep time = 15 minutes, bedtime = 10 minutes, and wake time = 10 minutes) achieved the highest F2 score (0.59). Notably, when participants were in bed, the ActiGraph Cole-Kripke algorithm continued to outperform Sadeh and Actiwatch AMRI, with an F2 score of 0.69. The study demonstrates both Actiwatch and ActiGraph's ability to detect daytime sleep, particularly during bed rest. ActiGraph (Cole-Kripke) algorithm exhibited a more balanced sleep detection profile and higher F2 scores compared to Actiwatch, offering valuable insights for optimizing daytime sleep monitoring with actigraphy in stroke patients.

A bidirectional free space optical link for last-mile terrestrial access links employing a novel wavelength shift keying technique

In this study, we propose a novel wavelength shift keying (WSK) technique that is combined with the conventional intensity modulation scheme for the transmission of point-to-point bidirectional data at the rate of 10 Gbps in each direction. We observe that WSK technique has not been investigated for implementation in point-to-point free space optical (FSO) links. Therefore, to the best of our knowledge, our study is the first one to perform this investigation. Our proposed link uses WSK in the downlink direction while we re-use the optical carriers for the transmission of uplink data. The use of WSK in the downlink direction enables us to perform balanced detection at the receiver, resulting in 3 dB improvement in receiver sensitivity compared to simple direct detection. We present bit error rate (BER) results for the signals transmitted in both the directions under different turbulence conditions and FSO link lengths. It was observed that the downlink signals generally perform better compared to the uplink due to the use of balanced detection and higher intensity fluctuations induced over the re-used optical carrier transmitted in the uplink direction.Please check and confirm that all the author names (given name, family name) and initials are correctly identified. The author names and initials are CORRECT.

A chip-integrated homodyne detection system with enhanced bandwidth performance for quantum applications

The rapid development of quantum technology has driven the need for high-performance quantum signal processing modules. Balanced homodyne detector (BHD) is one of the most promising options for practical quantum state measurement, providing substantial advantages of cost-effectiveness, no cooling requirement, and system compactness. However, due to the stringent requirements in BHD design, it typically suffers from a relatively small operating bandwidth which limits the overall speed of a quantum system. In this study, we propose comprehensive modelling for the BHD in quantum applications and enhance the performance of BHDs based on our modelling. Specifically, we utilise a photonic chip approach and optimise the electronic design to create the integrated BHD, which significantly boosts the 3 dB bandwidth to 4.75 GHz and achieves a shot-noise-limited bandwidth of 23 GHz. We demonstrate the capability of this setup to generate quantum random numbers at a rate of 240 Gbit s−1, highlighting its potential for ultra-high-speed quantum communication and quantum cryptography applications.

Few-Shot Object Detection in Remote Sensing Images via Data Clearing and Stationary Meta-Learning

Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote sensing data availability. In view of various challenges posed by remote sensing images (RSIs) and FSOD, we propose a meta-learning-based Balanced Few-Shot Object Detector (B-FSDet), built upon YOLOv9 (GELAN-C version). Firstly, addressing the problem of incompletely annotated objects that potentially breaks the balance of the few-shot principle, we propose a straightforward yet efficient data clearing strategy, which ensures balanced input of each category. Additionally, considering the significant variance fluctuations in output feature vectors from the support set that lead to reduced effectiveness in accurately representing object information for each class, we propose a stationary feature extraction module and corresponding stationary and fast prediction method, forming a stationary meta-learning mode. In the end, in consideration of the issue of minimal inter-class differences in RSIs, we propose inter-class discrimination support loss based on the stationary meta-learning mode to ensure the information provided for each class from the support set is balanced and easier to distinguish. Our proposed detector’s performance is evaluated on the DIOR and NWPU VHR-10.v2 datasets, and comparative analysis with state-of-the-art detectors reveals promising performance.

Optimization of Balanced Detector for Coherent Receiver on Generic InP Platform by Particle Swarm Optimization

Optimization of Balanced Detector for Coherent Receiver on Generic InP Platform by Particle Swarm Optimization

Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection

Ensuring the structural integrity of steel construction is critical, necessitating effective methods for detecting bolt defects. Traditional inspection methods are reliable but require significant labor and struggle with the accessibility of complex structures. This research introduces a real-time bolt-defect detection system that integrates tiny machine learning (TinyML) with a magnetic climbing robot to enhance inspection efficiency and scope. The system employs the faster objects, more objects (FOMO) algorithm optimized for edge computing on microcontrollers. This approach enables accurate bolt identification, including normal, loose, and missing bolts. The average system accuracy is 82%, with precision and recall values from 0.57 to 0.89 and 0.67 to 0.87, respectively. The system demonstrates balanced detection, evidenced by an F1 score improvement of up to 62%. The FOMO model displays compelling performance on defect detection tasks, achieving an F1 score of approximately 75%, outperforming the MobileNetV2 Single Shot Multibox Detector with Feature Pyramid Networks Lite and You Only Look Once version 5 small. The efficiency of the FOMO model is highlighted by its low hardware requirements at less than 0.1 MB of flash memory and 893.8 KB of random access memory for the 32-bit floating-point data format and is reduced for the 8-bit integer data format, with inference times of 142 ms and 86 ms, respectively. These findings contrast with the higher resource demands and slower inference time of the compared models, indicating the suitability of the FOMO model for low-capacity microcontrollers and its feasibility for real-time applications. The performance analysis across scenarios confirms the high precision (average 0.77) and recall (average 0.76), validating the robustness of the model in diverse environmental conditions. The system offers an advancement over traditional bolt-defect detection methods in accuracy and efficiency by leveraging TinyML and a magnetic climbing robot, setting new benchmarks for real-time inspection technology. The quantitative results underscore the performance of the system, presenting a scalable, cost-effective solution for improving the safety and durability of steel structures.

Ключевые особенности протоколов квантового распределения ключей на непрерывных переменных

The key features of continuous variables quantum key distribution protocols are considered in the paper. The motivation for studying and developing methods of quantum cryptography is substantiated. The main aspects inherent to continuous-variable protocols are highlighted, and they are classified based on various characteristics, peculiarities, and implementation variants. A detailed examination of a protocol example with discrete modulation and signal registration through balanced homodyne detection is provided. A classification of attacks on the quantum protocol is presented. A general overview of post-processing methods is given. The role of noise is indicated, and approaches to assessing the level of security applicable to continuous-variable protocols are discussed. In conclusion, the main conclusions regarding the current status of this problem are presented, and aspects requiring further study are identified.

Measuring non-linear Faraday rotation in cold atoms in presence of persistent transverse fields using tunable differential imaging

Non-linear Faraday rotation in cold atoms promises precision magnetometry due to narrower magnetic resonance linewidth compared to the linear Faraday effect. Imaging techniques based on linear Faraday effect have emerged as a tool to characterize the dynamics of ultracold atomic clouds. Using a camera instead of balanced detectors, we can obtain the spatial distribution of polarization rotation in a uniformly intense optical beam. However, the finite dynamic range of the imaging device limits the sensitivity to measure non-linear Faraday rotation at a given incident power. Here, we experimentally demonstrate a differential imaging technique in which we can tune parameters to improve contrast and the sensitivity to the non-linear Faraday rotation signal by a factor of ≈7 over existing imaging methods. The atomic cloud experiences a uniform optical field even when shifted by persistent magnetic fields making the method robust. This allows us to study the effect of transverse fields on non-linear Faraday rotation in ultra-cold atoms, paving the way toward spatially resolved vector magnetometry.

Multi-channel delay sampling to extend imaging depth in high-speed swept-source OCT systems.

We present a multi-channel delay sampling method to extend imaging depth in high-speed swept-source optical coherence tomography (SS-OCT). A balanced detector captures interference signals, converting them into electrical signals, which are then split into N channels, each with fixed time delays determined by the length of electrical cables. Then, they are digitized by an N-channel acquisition card. A calibration procedure is utilized to compensate for non-uniform phase shifts resulting from fixed time delays. The N-channel signals are merged in k-space and resampled to obtain a linearized spectrum, which increases the sampling rate by a factor of N, thereby extending the ranging distance by N times, all without altering k-clock triggering or sacrificing other imaging performance. The signal-to-noise ratio and sensitivity within the original depth range also have been enhanced. This advancement contributes to the improvement of the overall performance of SS-OCT systems.

Chaotic dead-zone eliminating and time delay signature concealing optoelectronic chaotic laser system based on balanced detection

Chaotic dead-zone eliminating and time delay signature concealing optoelectronic chaotic laser system based on balanced detection

Balanced-detection interferometric cavity-assisted photothermal spectroscopy via collimating fiber-array integration

This work reports on the implementation of a fiber-array with attached microlenses in the balanced-detection Interferometric Cavity-Assisted Photothermal Spectroscopy (ICAPS) scheme, enabling a highly compact probe laser beam path. The sample probe and the reference beam were coupled into a 1-mm spaced Fabry-Perot interferometer with a parallel beam separation of only 1.25 mm. A quantum cascade laser was used to induce photothermal waves, which were detected via a sample probe beam. A reference probe beam was used to realize balanced detection, allowing for significant noise reduction. The sensor’s metrological figures of merit were evaluated by detection of nitric oxide. For the targeted absorption band centered at 1900.08 cm−1 a 1 σ minimum detection limit of 64 ppbv was achieved with a 1-second integration time. This performance corresponds to a normalized noise equivalent absorption of 5.7 ×10−8 cm−1 W Hz−1/2.

Construction site environment helmet detection method based on multi-scale context and attention fusion

Construction site environment helmet detection is of great significance for protecting workers’ lives and realizing the automation of safety management. Aiming at the current object detection methods for the complex construction site environment in the small-scale helmet object detection ability is insufficient. This paper proposes a construction site environment helmet detection method based on multi-scale context and attention fusion. The method is able to aggregate the multi-scale contextual semantics of deep image features through the proposed multi-scale context module and expand the receptive field in order to improve the network’s discriminative learning ability for small-scale helmet objects. Meanwhile, the proposed attention feature fusion module dynamically fuses features from shallow features and network decoding features to enhance the network’s ability to learn the expression of global feature dependencies and local spatial detail features of helmet objects, and further improve the network’s detection precision of helmet objects. The experimental results show that on the constructed safety helmet wearing dataset, the proposed method in this paper has good detection effect and balanced detection speed compared with the existing mainstream object detection methods.

Phase-locked dual-frequency microwave signal generation in an optoelectronic oscillator based on frequency mixing mutual injection

An approach to generating stable phase-locked dual-frequency microwave signals is proposed and demonstrated based on a dual-passband optoelectronic oscillator (OEO). Mode gain competition is broken by employing frequency mixing mutual injection effect to realize phase locking between the two oscillation signals, which is achieved by applying a single-tone signal to a microwave mixer in the OEO cavity. In addition, a dual-loop configuration with balanced detection is utilized to ensure a high side mode suppression ratio (SMSR) and ultra-low phase noise, which also enhances the stability of the generated signal. In the experiment, a phase-locked dual-frequency microwave signal at 9.9982 GHz and 10.1155 GHz is generated by using the proposed OEO scheme. The SMSR and the phase noise are 75 dB and –141 dBc/Hz@10 kHz, respectively. Additionally, the Allan deviation of the generated signal is in the order of 10−11@1 s. These parameters are superior to those based on the same OEO but with a single-loop configuration, which are also compared in detail.

Not all points are balanced: Class balanced single-stage outdoor multi-class 3D object detector from point clouds

Outdoor 3D object detection is a hot topic in autonomous driving. The mainstream pure point cloud method is down-sampling through different task-oriented strategies to retain representative foreground points. Although such strategies are conducive to finding instances, these methods still suffer from two issues: class points imbalance during down-sampling stages, and foreground/background points imbalance in the final retained point clouds. The former imbalance results in poor precision for small objects; and the latter ignores background points, leading to a false positive phenomenon. To tackle the unbalanced phenomenon, we propose a simple yet effective balanced 3D detector, termed CB-SSD, including two balanced strategies: class balance strategy (CBS) and foreground/background balance strategy (FBBS). It is important to note that we do not alter the distribution of point clouds. Instead, we guide the model’s attention towards different classes equally. CB-SSD shows better precision on small objects, reducing false positives where foreground points and background points are similar. Considering both speed and accuracy, CB-SSD achieves state-of-the-art based on pure point clouds (single-stage) on KITTI and ONCE datasets. On KITTI, CB-SSD attains a multi-class accuracy of 72.92 mAP with 81 FPS.