Rapid diagnosis plays a vital role in daily life and is effective in reducing treatment costs and increasing curability, especially in remote areas with limited availability of resources. Among the various common methods of rapid diagnosis, centrifugal microfluidics has many unique advantages, such as less sample consumption, more precise valve control for sequential loading of samples, and accurately separated module design in a microfluidic network to minimize cross-contamination. Therefore, in recent years, centrifugal microfluidics has been extensively researched, and it has been found to play important roles in biology, chemistry, and medicine. Here, we review the latest developments in centrifugal microfluidic platforms in immunoassays, biochemical analyses, and molecular diagnosis, in recent years. In immunoassays, we focus on the application of enzyme-linked immunosorbent assay (ELISA); in biochemical analysis, we introduce the application of plasma and blood cell separation; and in molecular diagnosis, we highlight the application of nucleic acid amplification tests. Additionally, we discuss the characteristics of the methods under each platform as well as the enhancement of the corresponding performance parameters, such as the limit of detection, separation efficiency, etc. Finally, we discuss the limitations associated with the existing applications and potential breakthroughs that can be achieved in this field in the future.

With the increasing scale of network, attacks against network emerge one after another, and security problems become increasingly prominent. Network intrusion detection system is a widely used and effective security means at present. In addition, with the development of machine learning technology, various intelligent intrusion detection algorithms also start to sprout. By flexibly combining these intelligent methods with intrusion detection technology, the comprehensive performance of intrusion detection can be improved, but the vulnerability of machine learning model in the adversarial environment can not be ignored. In this paper, we study the defense problem of network intrusion detection system against adversarial samples. More specifically, we design a defense algorithm for NIDS against adversarial samples by using bidirectional generative adversarial network. The generator learns the data distribution of normal samples during training, which is an implicit model reflecting the normal data distribution. After training, the adversarial sample detection module calculates the reconstruction error and the discriminator matching error of sample. Then, the adversarial samples are removed, which improves the robustness and accuracy of NIDS in the adversarial environment.

In recent years, point-of-care testing has played an important role in immunoassay, biochemical analysis, and molecular diagnosis, especially in low-resource settings. Among various point-of-care-testing platforms, microfluidic chips have many outstanding advantages. Microfluidic chip applies the technology of miniaturizing conventional laboratory which enables the whole biochemical process including reagent loading, reaction, separation, and detection on the microchip. As a result, microfluidic platform has become a hotspot of research in the fields of food safety, health care, and environmental monitoring in the past few decades. Here, the state-of-the-art application of microfluidics in immunoassay in the past decade will be reviewed. According to different driving forces of fluid, microfluidic platform is divided into two parts: passive manipulation and active manipulation. In passive manipulation, we focus on the capillary-driven microfluidics, while in active manipulation, we introduce pressure microfluidics, centrifugal microfluidics, electric microfluidics, optofluidics, magnetic microfluidics, and digital microfluidics. Additionally, within the introduction of each platform, innovation of the methods used and their corresponding performance improvement will be discussed. Ultimately, the shortcomings of different platforms and approaches for improvement will be proposed.

The presence of frequency response mismatches (FRMs) in time-interleaved (TI) systems can severely degrade system performance. In addition, it is difficult to represent FRMs in generalized TI systems using low-order polynomials or physical circuits, which complicates the design of calibration methods. In this study, first, we propose a digital time-interleaved (DTI) structure to establish a relationship between the TI system and a periodic time-varying filter. The DTI structure uses less resources in time-varying filter implementation than the method that utilizing filters and multipliers. We also develop a cascaded calibration model and its equivalent noncascaded form based on the DTI structure. The equivalent noncascaded form uses only one time-varying filter rather than cascades multiple time-varying filters, which is a significant advantage because it can improve the calibration performance without increasing hardware resources during implementation. Furthermore, a filtering process was included in the frequency domain, rather than in the time domain, to cope with the challenges of designing an arbitrary frequency response filter. Simulations based on a four-channel generalized frequency response TI system showed that the proposed method achieved more than 45-dB spur-free dynamic range (SFDR) improvements if measurements of frequency responses are unbiased. Also, more than 60-dB SFDR can be achieved even if a ±5% estimation bias in the measurement of frequency responses. Errors between calibration frames were also minimized by introducing an overlapping frame technique, which ensured that the algorithm could calibrate large data quantities with high precision. Calibration was also implemented in a real four-channel 10 gigabit sampling per second (GSPS) system. Results indicated that the spurious-free dynamic range increased from 47.27 to 62.21 dB at a 5-GHz bandwidth after calibration.

The band interleaved data acquisition system (BI-DAS) is an attractive structure to improve the bandwidth of the acquisition system. However, the non-ideal characteristic of the spectrum analysis filter in BI-DAS results in an overlapping frequency band between two adjacent frequency sub-bands. Phase misalignment (PM) between two sub-bands in the overlapping band may cause those signals canceled or partially canceled to each other when sub-bands’ signal are merged. In this paper, a compensation module with a digital all-pass filter (APF) is proposed for the PM of the overlapping bands in BI-DASs. Based on this compensation module, a hybrid Particle Swarm Optimization (PSO) algorithm, along with the Levenberg-Marquardt (LM) algorithm is proposed to design coefficients of the compensation module. The compensation module and corresponding method proposed in this paper are verified in a BI-DAS with 20Gsps sampling rate and 5.5GHz bandwidth. The experimental results show that the proposed compensation module can effectively compensate the PM between the sub-bands in the overlapping band. The proposed hybrid PSO-LM (HPSOLM) algorithm combines the flexibility and reliability inherent in the PSO with the fast convergence and precision of the LM algorithm. It can effectively design the compensation module with stable APF while consuming less time and obtaining better compensation results than the conventional PSO method.