External modulation method for generating accurate linear optical FMCW

Frequency modulation continuous wave (FMCW) lasers are key components in modern optical imaging. However, current intracavity modulation lasers do not exhibit low-frequency jitter rate and high linearity due to the inherent relaxation oscillations. Although this may be compensated in a direct modulation laser diode using an optoelectronic feedback loop, the available sweep speed is moderately small. In this letter, a special external modulation method is developed to improve the performance of FMCW. Since only the first sideband optical field is used during the entire generation process, phase noise is kept to a minimum and is also independent of the sweep speed. We demonstrate that the linearity and jitter rates do not deteriorate appreciably when the sweep speed is changed over three orders of magnitude, even up to the highest sweep speed of 2.5 GHz/ μs

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, {\color{blue}which can greatly improve the spectrum efficiency and effectively relieve the massive access problem of 5G and beyond networks. In this paper, we investigate the performance of SGF schemes under more practical settings.} Firstly, we study the outage performance of the best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. Then, a fair SGF scheme is proposed by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), which can also make full use of multi-user diversity. Moreover, by employing the theories of order statistics and stochastic geometry, we analyze the outage performances of both BU-SGF and CS-SGF schemes. Results show that full diversity orders can be achieved only when the served users' data rate is capped, which severely limit the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive closed-form expressions of the two schemes' outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses.Comment: 41 pages, 8 figure

Cotton boll localization method based on point annotation and multi-scale fusion

Cotton is an important source of fiber. The precise and intelligent management of cotton fields is the top priority of cotton production. Many intelligent management methods of cotton fields are inseparable from cotton boll localization, such as automated cotton picking, sustainable boll pest control, boll maturity analysis, and yield estimation. At present, object detection methods are widely used for crop localization. However, object detection methods require relatively expensive bounding box annotations for supervised learning, and some non-object regions are inevitably included in the annotated bounding boxes. The features of these non-object regions may cause misjudgment by the network model. Unlike bounding box annotations, point annotations are less expensive to label and the annotated points are only likely to belong to the object. Considering these advantages of point annotation, a point annotation-based multi-scale cotton boll localization method is proposed, called MCBLNet. It is mainly composed of scene encoding for feature extraction, location decoding for localization prediction and localization map fusion for multi-scale information association. To evaluate the robustness and accuracy of MCBLNet, we conduct experiments on our constructed cotton boll localization (CBL) dataset (300 in-field cotton boll images). Experimental results demonstrate that MCBLNet method improves by 49.4% average precision on CBL dataset compared with typically point-based localization state-of-the-arts. Additionally, MCBLNet method outperforms or at least comparable with common object detection methods

Label-free microfluidic paper-based electrochemical aptasensor for ultrasensitive and simultaneous multiplexed detection of cancer biomarkers

Simultaneous detection of multiple tumor biomarkers in body fluids could facilitate early diagnosis of lung cancer, so as to provide scientific reference for clinical treatment. This paper depicted a multi-parameter paper-based electrochemical aptasensor for simultaneous detection of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) in a clinical sample with high sensitivity and specificity. The paper-based device was fabricated through wax printing and screen-printing, which enabled functions of sample filtration and sample auto injection. Amino functional graphene (NG)-Thionin (THI)- gold nanoparticles (AuNPs) and Prussian blue (PB)- poly (3,4- ethylenedioxythiophene) (PEDOT)- AuNPs nanocomposites were synthesized respectively. They were used to modify the working electrodes not only for promoting the electron transfer rate, but also for immobilization of the CEA and NSE aptamers. A label-free electrochemical method was adopted, enabling a rapid simple point-of-care testing. Experimental results showed that the proposed multi-parameter aptasensor exhibited good linearity in ranges of 0.01-500 ng mL for CEA (R  = 0.989) and 0.05-500 ng mL for NSE (R  = 0.944), respectively. The limit of detection (LOD) was 2 pg mL for CEA and 10 pg mL for NSE. In addition, the device was evaluated using clinical serum samples and received a good correlation with large electrochemical luminescence (ECL) equipment, which would offer a new platform for early cancer diagnostics, especially in those resource-limit areas

Investigation on accuracy of numerical simulation of aerodynamic noise of single-stage axial fan

The prediction accuracy of turbomachinery aerodynamic noise, particularly in relation to broadband noise with uncertain factors, has long been a challenging issue. Previous studies have not fully comprehended the factors influencing its prediction accuracy, lacking an objective and comprehensive evaluation method. An improved approach combining orthogonal experiment design and principal component analysis is employed to address these limitations. The evaluation method expands the noise metrics and provides a comprehensive assessment of the accuracy of numerical simulation for aerodynamic noise. The evaluation method is utilized to optimize and quantitatively analyze the impact of the refinement size of the core area on noise prediction for single-stage axial fans. Subsequently, the three metrics, namely, Z1, Z2, and broadband noise Z3, are integrated using PCA to form a new integrated optimal metric Ztotal. The influence of different refinement sizes, particularly on Ztotal, is quantitatively examined. The findings reveal that the mesh size of the stator wake (D area) exhibits the most significant influence on noise prediction accuracy, with a calculated weight of 81.3% on noise accuracy. Furthermore, a comprehensive investigation is conducted on the influence of turbulence models and the wall Y+ value on aerodynamic noise. Detached-eddy simulation and large eddy simulation demonstrate effective capabilities in simulating both upstream and downstream turbulent flow characteristics of the stator, enabling accurate prediction of broadband noise. This study presents a set of numerical simulation schemes that achieve precise prediction of turbomachinery aerodynamic noise

Automated Raman based cell sorting with 3D microfluidics

Raman activated cell sorting has emerged as a label-free technology that can link phenotypic function with genotypic properties of cells. However, its broad implementation is limited by challenges associated with throughput and the complexity of biological systems. Here, we describe a three-dimensional hydrodynamic focusing microfluidic system for a fully automated, continuous Raman activated cell sorting (3D-RACS). The system consists of a 3D printed detection chamber (1 mm3) that is integrated with a PDMS based sorting unit, optical sensors and an in-line collection module. It has the ability to precisely position cells in the detection chamber for Raman measurements, effectively eliminating spectroscopic interference from the device materials. This enables the sorting of a range of cell sizes (from 1 μm bacteria to 10's μm mammalian cells) with stable operation over >8 hours and high throughput. As a proof-of-concept demonstration, Raman-activated sorting of mixtures of Chlorella vulgaris and E. coli has demonstrated a purity level of 92.0% at a throughput of 310 cells per min. The platform employed in this demonstration features a simple “Raman window” detection system, enabling it to be built on a standard, inverted microscope. Together with its facile and robust operation, it provides a versatile tool for function-based flow cytometry and sorting applications in the fields of microbiology, biotechnology, life science and diagnostics