Chunhui Ren's research while affiliated with University of Electronic Science and Technology of China and other places

In this correspondence, we address the issue of direction-of-arrival (DOA) estimation for coprime multiple-input multiple-output (MIMO) radar employing mixed-resolution quantization. First, we derive the received signal model of coprime MIMO radar under mixed-resolution quantization, and utilize mixed-resolution measurements to construct an augmented signal vector via virtual array interpolation. Subsequently, we reconstruct a low-rank augmented covariance matrix without the effect of quantization error by solving an atomic norm minimization problem. Finally, we extract the DOAs of targets by applying MUSIC method on the reconstructed low-rank matrix. Unlike the existing techniques, our method not only recovers the missing correlation information, but also reduces the influence of noise and quantization error. Simulation results illustrate the advantage of the proposed approach over the existing techniques when handling mixed-resolution measurements.

Platelet detection and counting play a greatly significant role in medical field, especially in routine blood tests which can be used to judge blood status and diagnose related diseases. Therefore, platelet detection is valuable for diagnosing related blood diseases such as liver-related diseases. Blood analyzers and visual microscope counting were widely used for platelet detection, but the experimental procedure took nearly 20 minutes and can only be performed by a professional doctor. In recent years, technological breakthroughs in artificial intelligence have made it possible to detect red blood cells through deep learning methods. However, due to the inaccessibility of platelet datasets and the small size of platelets, deep learning-based platelet detection studies are almost nonexistent. In this paper, we carried out experiments for platelet detection based on commonly used object detection models, such as Single Shot Multibox Detector (SSD), RetinaNet, Faster_rcnn, and You Only Look Once_v3 (YOLO_v3). Compared with the other three models, YOLO_v3 can detect platelets more effectively. And we proposed three ideas for improvement based on YOLO_v3. Our study demonstrated that YOLO_v3 can be adopted for platelet detection accurately and in real time. We also implemented YOLO_v3 with multiscale fusion, YOLO_v3 with anchor box clustering, and YOLO_v3 with match parameter on our self-created dataset and, respectively, achieved 1.8% higher average precision (AP), 2.38% higher AP, and 2.05% higher AP than YOLO_v3. The comprehensive experiments revealed that YOLO_v3 with the improved ideas performs better in platelet detection than YOLO_v3.

In this letter, an effective method for estimating direction-of-arrival (DOA) using the nested array via mixed-resolution (MR) quantization is developed. First, we derive the output signal model of the nested array under MR quantization, and utilize MR quantized data to construct a quantized covariance matrix. Subsequently, we recover an unquantized augmented covariance matrix by formulating a rank-minimization problem in the coarray domain. Finally, we perform DOA estimation using the recovered covariance matrix. The proposed method can achieve excellent performance close to that of the existing methods using high-resolution data. Simulation results demonstrate the validity of our approach.

Microfluidic technology has shown great application prospects in biochemical analysis, drug delivery, disease diagnosis, and other fields. Magnetophoresis is an effective means to manipulate particles in liquid media by using magnetic field force. This combination forms a simple, effective, high-throughput, and low-cost bioparticle analysis technology. In this paper, we explain the basic principles of magnetophoresis technology and analyze the two key parts of magnetophoresis equipment, liquid medium and magnetic field source. Then, we classify current mainstream magnetophoresis techniques, review the research and applications of magnetophoresis in particle mixing, focusing and separation in recent years, and analyze the key parameters affect the performance of magnetophoresis. Finally, in view of the limitations of current magnetophoresis technology, we offer an outlook on possible future development directions.

In cognitive radio context, the coding parameters are unknown at the receiver. The design of an intelligent receiver is essentially to identify these parameters from the received data blindly. In this paper, we are interested in the blind identification of binary linear block codes from received noisy data. In order to recognize the code length, the concept of the normalized column weight vector is defined and cosine similarity is used to measure the difference between linear block codes and random codes. Then, the correct code length could be obtained by finding the local minimum of cosine similarity. The proposed code length recognition method needs no prior knowledge about the codes, which results in completely blind identification. To reconstruct the parity check matrix, the concept of association rules mining is introduced to the problem of blind identification of channel codes for the first time. Furthermore, five criteria are proposed to reduce the redundant rules mined by the association rules mining algorithm and to recognize the parity check vectors effectively. Simulations show that the proposed two methods have excellent performance even in a high error rate transport environment. The performance comparisons with existing methods validate the advantages of our two proposed methods.

Point-of-care testing (POCT) systems have been greatly developed in recent years. Among them, lateral flow immunochromatography (LFIA) based on magnetic nanoparticles (MNPs) is widely used in various fields due to the advantages of small background noise and good biocompatibility. This paper designed an ultra-sensitive giant magnetic resistance system for the quantitative detection of methamphetamine (MET). The system uses giant magnetic resistances to detect the distribution of magnetic field intensity of MNPs captured by the test (T) and control (C) lines on the LFIA. A special external interference cancellation (EIC) method and a weak-signal waveform reconstruction method were used to improve the accuracy of the detection. Finally, the T/C ratio was calculated to realize the quantitative detection of MET. The result exhibited a good linear performance with a detection limit of 0.1 ng/mL. The system can also be used in other fields such as disease detection, food analysis, and environmental testing.

An up-conversion luminescent material converts low-frequency excitation light into high-frequency emission light through photons and has the advantages of long fluorescence lifetime, narrow emission peak and low toxicity; thus, this material has many unique applications in the detection and identification of biomolecules. In this study, an ultrasensitive up-conversion luminescent system for the quantitative detection of morphine was developed. The principle of this system is based on infrared light as an excitation light source to convert light with lower energy into excitation light with higher energy. The up-conversion luminescent material is used as a label and through the processing and analysis of the excitation light intensity, the quantitative detection of morphine concentration is achieved. At the same time, the excitation light can avoid the interference and scattering phenomenon of the autofluorescence of the biological sample, which improves the system's detection sensitivity. An algorithm for light intensity processing is added to process image data, reduce the interference caused by noise during image acquisition and improve the accuracy of morphine detection. The T/C value is calculated to achieve the quantitative detection of morphine with a detection limit of 0.1 ng mg⁻¹ and detection time within 0.5 min. The up-conversion luminescent system has the advantages of quantitative detection, convenience, portability, short detection time and low price. Thus, the system can be used for the detection of other biomolecules or for other applications such as food analysis, environmental detection, national security, etc.

Since December 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused millions of deaths and seriously threatened the safety of human life; indeed, this situation is worsening and many people are infected with the new coronavirus every day. Therefore, it is very important to understand patients’ degree of infection and infection history through antibody testing. Such information is useful also for the government and hospitals to formulate reasonable prevention policies and treatment plans. In this paper, we develop a lateral flow immunoassay (LFIA) method based on superparamagnetic nanoparticles (SMNPs) and a giant magnetoresistance (GMR) sensing system for the simultaneously quantitative detection of anti-SARS-CoV-2 immunoglobulin M (IgM) and G (IgG). A simple and time-effective co-precipitation method was utilized to prepare the SMNPs, which have good dispersibility and magnetic property, with an average diameter of 68 nm. The Internet of Medical Things-supported GMR could transmit medical data to a smartphone through the Bluetooth protocol, making patient information available for medical staff. The proposed GMR system, based on SMNP-supported LFIA, has an outstanding advantage in cost-effectiveness and time-efficiency, and is easy to operate. We believe that the suggested GMR based LFIA system will be very useful for medical staff to analyze and to preserve as a record of infection in COVID-19 patients.

Biosensing platforms for detecting and quantifying biomarkers have played an important role in the past decade. Among them, platforms based on magnetoresistance (MR) sensing technology are attractive. The resistance value of the material changes with the externally applied magnetic field is the core mechanism of MR sensing technology. A typical MR-based sensor has the characteristics of cost-effective, simple operation, high compactness, and high sensitivity. Moreover, using magnetic nanoparticles (MNPs) as labels, MR-based sensors have the ability to overcome the high background noise of complex samples, so they are particularly suitable for point-of-care testing (POCT). However, the problem still exists. How to obtain high-throughput, that is, multiple detections of biomarkers in MR-based sensors, thereby improving detection efficiency and reducing the burden on patients is an important issue in future work. This paper reviews three MR-based detection technologies for the detection of biomarkers, i.e., anisotropic magnetoresistance (AMR), giant magnetoresistance (GMR), and tunneling magnetoresistance (TMR). Based on these three common technologies, different typical applications that include biomedical diagnosis, food safety, and environmental monitoring are presented. Furthermore, the existing MR-based detection method is better expanded to make it more in line with present detection needs by combining different advanced technologies including microfluidics, Microelectromechanical systems (MEMS), and Immunochromatographic test strips (ICTS). And then, a brief discussion of current challenges and perspectives of MR-based sensors are pointed out.

This paper studies adaptive detection of distributed targets in interference and noise. The signal and interference are assumed to lie in two known subspaces which are partially related; this denotes the cases where the signal and interference cannot be completely separated in spatial, temporal and frequency domains. Since impartible, the signal and interference are recast as one with the aid of singular value decomposition (SVD); then, the generalized likelihood ratio test (GLRT) and the two-step GLRT are derived in both homogeneous and partially homogeneous environments. The four new detectors are the generalizations of the existing GLRT-based ones; they have the constant false alarm rate (CFAR) properties and the capabilities of interference rejection. The effectiveness of the new detectors is demonstrated via numerical experiments, also in comparison with previous detectors of similar kind.