The CrMYB33 transcription factor positively coordinate the regulation of both carotenoid accumulation and chlorophyll degradation in the peel of citrus fruit.

Citrus, cultivated extensively across the globe, possesses considerable economic importance and nutritional value. With the degradation of chlorophyll and accumulation of carotenoids, mature citrus fruits develop an orange-yellow peel, enhancing fruit value and consumer preference. MYB transcription factors (TFs) exert a significant role in diverse plant developmental processes and investigating their involvement in fruit coloration is crucial for developing new cultivars. This work aimed to characterize a citrus TF, CrMYB33, whose expression was found to be positively correlated with carotenoid biosynthesis during fruit ripening. The interference of CrMYB33 expression in citrus fruit resulted in inhibition of carotenoid accumulation, down-regulation of carotenoid biosynthetic genes, and a slower rate of chlorophyll degradation. Conversely, overexpression of CrMYB33 in tomato (Solanum lycopersicum) enhanced chlorophyll degradation and carotenoid biosynthesis, resulting in a deeper red coloration of the fruits. Furthermore, the transcription of associated genes was upregulated in CrMYB33-overexpressing tomato fruits. Additional assays reveal that CrMYB33 exhibits direct links and activation of the promoters of lycopene ß-cyclase 2 (CrLCYb2), and ß-carotene hydroxylases 2 (CrBCH2), both crucial genes in the carotenoid biosynthetic pathway. Additionally, it was found to inhibit chlorophyllase (CrCLH), a gene essential in chlorophyll degradation. These findings provide insight into the observed changes in LCYb2, BCH2, and CLH expression in the transgenic lines under investigation. In conclusion, our study revealed that CrMYB33 modulates carotenoid accumulation and chlorophyll degradation in citrus fruits through transcriptionally activating genes involved in metabolic pathways.

Analysis of the differences in physicochemical properties, volatile compounds, and microbial community structure of pit mud in different time spaces.

Pit mud (PM) is among the key factors determining the quality of Nongxiangxing baijiu, a Chinese liquor. Microorganisms present inside PM are crucial for the unique taste and flavor of this liquor. In this study, headspace solid-phase microextraction was used in combination with gas chromatography and high-throughput sequencing to determine the volatile compounds and microbial community structure of 10- and 40-year PM samples from different spaces. The basic physicochemical properties of the PM were also determined. LEfSe and RDA were used to systematically study the PM in different time spaces. The physicochemical properties and ester content of the 40-year PM were higher than those of the 10-year PM, but the spatial distribution of the two years PM samples exhibited no consistency, except in terms of pH, available phosphorus content, and ester content. In all samples, 29 phyla, 276 families, and 540 genera of bacteria, including four dominant phyla and 20 dominant genera, as well as eight phyla, 24 families, and 34 genera of archaea, including four dominant phyla and seven dominant genera, were identified. The LEfSe analysis yielded 18 differential bacteria and five differential archaea. According to the RDA, the physicochemical properties and ethyl caproate, ethyl octanoate, hexanoic acid, and octanoic acid positively correlated with the differential microorganisms of the 40-year PM, whereas negatively correlated with the differential microorganisms of the 10-year PM. Thus, we inferred that Caproiciproducens, norank_f__Caloramatoraceae, and Methanobrevibacter play a dominant and indispensable role in the PM. This study systematically unveils the differences that affect the quality of PM in different time spaces and offers a theoretical basis for improving the declining PM, promoting PM aging, maintaining cellars, and cultivating an artificial PM at a later stage.

Research on a method of high precision frequency measurement based on coordinate rotation digital computer algorithm and Kalman filter.

Frequency measurement is one of the key techniques in high-precision data acquisition technology of broadband signals. Generally, frequency measurement not only needs to deal with a large amount of data processing but also requires a high precision, but these two aspects are sometimes difficult to reconcile. Some algorithms are overly dependent on the accuracy of the to-be-measured data, which might not be the desired option for real projects since it is almost impossible to get ideal error-free data. This article adopts a frequency measurement method based on the coordinate rotation digital computer algorithm, differential algorithm, and Kalman filter. The use of these algorithms for the frequency measurement process would not only simplify the calculation but also reduce the effect of the measurement error. This method can measure all signals that satisfy the sampling theorem and can also measure multi-channel parallel signals. The experimental results of data simulation and actual measurement on the hardware platform show that the accurate frequency measurement algorithm has a strong data processing ability, stable measurement, and steady improvement in the accuracy of measurement results, which can meet the needs of most instruments for accurate frequency measurement. The measurement error could be reduced to the percentile by the Kalman filter and could be reduced to below the thousandth by the combining the algorithms.

Convolutional Neural Network for Accurate Analysis of Methamphetamine With Upconversion Lateral Flow Biosensor.

Methamphetamine is a powerful stimulant drug, the abuse of which threatens human health and social stability. Rapid and accurate quantification of methamphetamine is essential to inhibit the abuse and prevalence of methamphetamine effectively. In this paper, we present a portable fluorescence reader with upconverting nanoparticle-labeled lateral flow immunoassay (LFIA) for rapid and accurate quantification of methamphetamine. Based on specific binding of a methamphetamine antigen to an antibody in the LFIA, the fluorescence reader is designed to capture and record the fluorescence intensities T and C of the test and control lines, respectively, and the T/C ratio is calculated to determine the concentration of methamphetamine. The linear range for methamphetamine is 0.1-100 ng/mL. Because the sensor is often susceptible to noise interference, using only the T/C ratio to distinguish weakly positive and negative samples of methamphetamine renders the results inaccurate. To solve this problem, we applied a convolutional neural network (CNN) to learn image features of different methamphetamine concentrations (0, 0.01, 0.05, 0.1, and 0.5 ng/mL) for accurate detection of weakly positive and negative samples. The results show that the proposed method can effectively detect weakly positive and negative samples of methamphetamine with an accuracy of up to 92%. The CNN provides a novel scheme for accurate analysis of weakly positive and negative samples in upconverting nanoparticle-labeled LFIA.

Transient abnormal signal acquisition system based on approximate entropy and sample entropy.

In the field of time domain measurement, with increasing complexity of measured signals, the periodic stationarity of signals is destroyed and the transient non-stationarity starts to stand out, specifically manifested as frequent presence of transient abnormal signals, such as burrs, harmonics, noises, and modulating waves in the periodic signals. By applying the entropy estimation of signals to the field of time domain measurement, this paper designs a transient abnormal signal acquisition system based on approximate entropy (ApEn) and sample entropy (SampEn). In the process of data acquisition, the ApEn and SampEn of sampled data are computed in real time and the complexities of measured signals are differentiated, thus realizing abnormal signal detection. The experimental results demonstrate that SampEn generally has a higher sensitivity and wider application than ApEn in the detection process of transient abnormal signals. The study can provide a new method for the design of a time-domain measuring instrument with abnormal signal detection ability.

Functional Characterization of a Flavone Synthase That Participates in a Kumquat Flavone Metabolon.

Flavones predominantly accumulate as O- and C-glycosides in kumquat plants. Two catalytic mechanisms of flavone synthase II (FNSII) support the biosynthesis of glycosyl flavones, one involving flavanone 2-hydroxylase (which generates 2-hydroxyflavanones for C-glycosylation) and another involving the direct catalysis of flavanones to flavones for O-glycosylation. However, FNSII has not yet been characterized in kumquats. In this study, we identified two kumquat FNSII genes (FcFNSII-1 and FcFNSII-2), based on transcriptome and bioinformatics analysis. Data from in vivo and in vitro assays showed that FcFNSII-2 directly synthesized apigenin and acacetin from naringenin and isosakuranetin, respectively, whereas FcFNSII-1 showed no detectable catalytic activities with flavanones. In agreement, transient overexpression of FcFNSII-2 in kumquat peels significantly enhanced the transcription of structural genes of the flavonoid-biosynthesis pathway and the accumulation of several O-glycosyl flavones. Moreover, studying the subcellular localizations of FcFNSII-1 and FcFNSII-2 demonstrated that N-terminal membrane-spanning domains were necessary to ensure endoplasmic reticulum localization and anchoring. Protein-protein interaction analyses, using the split-ubiquitin yeast two-hybrid system and bimolecular fluorescence-complementation assays, revealed that FcFNSII-2 interacted with chalcone synthase 1, chalcone synthase 2, and chalcone isomerase-like proteins. The results provide strong evidence that FcFNSII-2 serves as a nucleation site for an O-glycosyl flavone metabolon that channels flavanones for O-glycosyl flavone biosynthesis in kumquat fruits. They have implications for guiding genetic engineering efforts aimed at enhancing the composition of bioactive flavonoids in kumquat fruits.

5G-Enabled intelligent construction of a chest pain center with up-conversion lateral flow immunoassay.

Acute myocardial infarction (AMI) has become a worldwide health problem because of its rapid onset and high mortality. Cardiac troponin I (cTnI) is the gold standard for diagnosis of AMI, and its rapid and accurate detection is critical for early diagnosis and management of AMI. Using a lateral flow immunoassay with upconverting nanoparticles as fluorescent probes, we developed an up-conversion fluorescence reader capable of rapidly quantifying the cTnI concentration in serum based upon the fluorescence intensity of the test and control lines on the test strip. Reliable detection of cTnI in the range 0.1-50 ng mL-1 could be achieved in 15 min, with a lower detection limit of 0.1 ng mL-1. The reader was also adapted for use on a 5th generation (5G) mobile network enabled intelligent chest pain center. Through Bluetooth wireless communication, the results achieved using the reader on an ambulance heading to a central hospital could be transmitted to a 5G smartphone and uploaded for real-time edge computing and cloud storage. An application in the 5G smartphone allows users to upload their medical information to establish dedicated electronic health records and doctors to monitor patients' health status and provide remote medical services. Combined with mobile internet and big data, the 5G-enabled intelligent chest pain center with up-conversion lateral flow immunoassay may predict the onset of AMI and save valuable time for patients suffering an AMI.

A sensitive and quantitative prognosis of C-reactive protein at picogram level using mesoporous silica encapsulated core-shell up-conversion nanoparticle based lateral flow strip assay.

C- reactive protein (CRP) is a sensitive indicator for infectious or inflammatory diseases in human which can reflect the body's inflammation latency and early pathophysiological changes. The most common detection method of serum CRP is ELISA that has been proved to be expensive and time-consuming, restricting its use in point-of-care application. In this paper, we demonstrated a lateral flow system for CRP quantification by using mesoporous silica (mSiO2) coated up-converting nanoparticles (UCNPs) (denoted as UCNPs@mSiO2) as fluorescent labels. The up-converting core can emit strong green fluorescence signals under NIR excitation light (980 nm) with excellent photostability, high signal-to-noise ratio and low background fluorescence. By wrapping ultrathin mSiO2 outside, the core-shell structured UCNPs@mSiO2 exhibits good dispersity and stability meanwhile maintains strong fluorescence emission. Besides, the mSiO2 shell provides further functionalities for antibody linkage. By using a portable fluorescence sensor, we reached a CRP detection limit of 0.05 ng/mL and a linear range from 0.1 ng/mL-50 ng/mL, and the detection time was no more than 8 min. The lateral flow test strips exhibit great stability in CRP quantification (CV%<5) and have a life time of more than 1 week at ambient temperature. Furthermore, the proposed system can work with a cloud-enabled smartphone through Bluetooth for Internet of Medical Things application. This CRP detection method proves to be rapid and easy-operated, which has great potential in early inflammatory disease perception in the point-of-care tests and future's 5G-enabled remote healthcare management.

Aptamer-based lateral flow assay on-site biosensors.

The lateral flow assay (LFA) is a widely used paper-based on-site biosensor that can detect target analytes and obtain test results in several minutes. Generally, antibodies are utilized as the biorecognition molecules in the LFA. However, antibodies selected using an in vivo process not only may risk killing the animal hosts and causing errors between different batches but also their range is restricted by the refrigerated conditions used to store them. To avoid these limitations, aptamers screened by an in vitro process have been studied as biorecognition molecules in LFAs. Based on the sandwich or competitive format, the aptamer-based LFA can accomplish on-site detection of target analytes. Since aptamers have a distinctive ability to undergo conformational changes, the adsorption-desorption format has also been exploited to detect target analytes in aptamer-based LFAs. This paper reviews developments in aptamer-based LFAs in the last three years for the detection of target analytes. Three formats of aptamer-based LFAs, i.e., sandwich, competitive, and adsorption-desorption, are described in detail. Based on these formats, signal amplification strategies and multiplexed detection are discussed in order to provide an overview of aptamer-based LFAs for on-site detection of target analytes. In addition, the potential commercialization and future perspectives of aptamer-based LFAs for rapid detection of SARS-CoV-2 are given to support the COVID-19 pandemic.

5G-enabled ultra-sensitive fluorescence sensor for proactive prognosis of COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading around the globe since December 2019. There is an urgent need to develop sensitive and online methods for on-site diagnosing and monitoring of suspected COVID-19 patients. With the huge development of Internet of Things (IoT), the impact of Internet of Medical Things (IoMT) provides an impressive solution to this problem. In this paper, we proposed a 5G-enabled fluorescence sensor for quantitative detection of spike protein and nucleocapsid protein of SARS-CoV-2 by using mesoporous silica encapsulated up-conversion nanoparticles (UCNPs@mSiO2) labeled lateral flow immunoassay (LFIA). The sensor can detect spike protein (SP) with a detection of limit (LOD) 1.6 ng/mL and nucleocapsid protein (NP) with an LOD of 2.2 ng/mL. The feasibility of the sensor in clinical use was further demonstrated by utilizing virus culture as real clinical samples. Moreover, the proposed fluorescence sensor is IoMT enabled, which is accessible to edge hardware devices (personal computers, 5G smartphones, IPTV, etc.) through Bluetooth. Medical data can be transmitted to the fog layer of the network and 5G cloud server with ultra-low latency and high reliably for edge computing and big data analysis. Furthermore, a COVID-19 monitoring module working with the proposed the system is developed on a smartphone application (App), which endows patients and their families to record their medical data and daily conditions remotely, releasing the burdens of going to central hospitals. We believe that the proposed system will be highly practical in the future treatment and prevention of COVID-19 and other mass infectious diseases.

A novel algorithm for implementing time-frequency transform with low computation.

In this paper, a novel algorithm called two-dimensional sliding fast Fourier transform (2D SFFT) algorithm is proposed. This algorithm organizes one-dimensional data in two dimensions and calculates the spectrum of current data by using the existing spectrum and new collected data. The algorithm formula and accurate simulation results show the following: first, the computation required by the proposed 2D SFFT algorithm is lower than that required by the traditional sliding discrete Fourier transform algorithm when the sliding rate is larger than or equal to 4/M, where M is the sequence length. Moreover, the computation required by the proposed 2D SFFT algorithm is lower than that required by the fast Fourier transform (FFT) algorithm when the sliding rate is less than or equal to 6.25%. Finally, the error between the spectrum calculated by the 2D SFFT and FFT algorithms is less than 10-10. The 2D SFFT algorithm is used to increase the power of the ultra-short pulse, which is initially invisible in the frequency-domain window of the mixed-domain oscilloscope. Therefore, the 100% probability of intercept of the mixed-domain oscilloscope is lower.

IoT-Enabled Fluorescence Sensor for Quantitative KET Detection and Anti-Drug Situational Awareness.

Recently, drug abuse has become a worldwide concern. Among varieties of drugs, KET is found to be favorite in drug addicts, especially teenagers, for recreational purposes. KET is a kind of analgesic and anesthetic drug which can induce hallucinogenic and dissociative effects after high-dose abuse. Hence, it is critical to develop a rapid and sensitive detection method for strict drug control. In this study, we proposed a cloud-enabled smartphone based fluorescence sensor for quantitative detection of KET from human hair sample. The lateral flow immunoassay (LFIA) was used as the detecting strategy where UCNPs were introduced as fluorescent labels. The sensor was capable of identifying the up-converted fluorescence and calculating the signal intensities on TL and CL to obtain a T/C value, which was corresponding to the KET concentration. The sensor transmitted the test data to the cloud-enabled smartphone through Type-C interface, and the data were further uploaded to the edge of the network for cloud-edge computing and storage. The entire detection took only 5 minutes with high stability and reliability. The detection limit of KET was 1 ng/mL and a quantitative detection range from 1 to 150 ng/mL. Furthermore, based on the huge development of Internet of Things (IoT), an App was developed on the smartphone for anti-drug situational awareness. Based on this system, it was convenient for Police Department to perform on-site KET detection. Moreover, it was critical for prediction of the development trend of future events, benefiting much to constructing a harmonious society.

Electrochemical vitamin sensors: A critical review.

This paper reviews the recent development of electrochemical sensors for the detection of vitamins over the past three years. Vitamins present in natural foodstuffs are a group of organic compounds that are indispensable to maintain human health. While they are only present in minute amounts, they still play a significant role in healthy metabolism. The deficiency of vitamins in the human body often leads to numerous diseases. Because the human body cannot synthesize most vitamins, it is necessary to obtain them from dietary sources. For these reasons, the detection of vitamins has gained widespread attention in recent years. As it is well known, almost all vitamins are electrochemically active. Based on the electrochemical oxidation or reduction reaction of the vitamins in an electrolyte, electrochemical sensors can obtain the concentration of the vitamins by measurement of the current at the working electrode. Electrochemical sensors for detecting water-soluble vitamins, such as vitamin B1, vitamin B2, vitamin B6, vitamin B9, vitamin B12, and vitamin C are discussed in detail. A comprehensive overview of electrochemical sensors for detecting fat-soluble vitamins, such as vitamin A, vitamin D, vitamin E, and vitamin K, is also provided. Furthermore, the strategies employed and the performance of the electrochemical sensors for detecting vitamins are described. Finally, current challenges and future prospects of electrochemical sensors for the detection of vitamins are discussed.

Multiplexed detection of biomarkers in lateral-flow immunoassays.

Multiplexed detection of biomarkers, i.e., simultaneous detection of multiple biomarkers in a single assay, is a process of great advantages including enhanced diagnostic precision, improved diagnostic efficiency, reduced diagnostic cost, and alleviated pain of patients. A typical lateral-flow immunoassay (LFIA) is a widely used paper-based immunochromatographic test strip designed to detect a target biomarker through two common formats: sandwich assay and competitive assay. In order to obtain qualitative or quantitative results, a probe with unique optical or magnetic properties is usually employed to characterize the concentration of the target biomarker. The typical LFIA is suitable for point-of-care testing due to its simplicity, portability, cost-effectiveness, and rapid detection of a target biomarker. However, detection of a single biomarker in the typical LFIA is not favorable for high throughput analysis. Therefore, multiplexed detection of biomarkers in LFIAs has been extensively studied in recent years for high throughput analysis. To accomplish multiplexed detection of biomarkers in LFIAs, the most frequently used structure is a test strip with multiple test lines (TLs), where each TL can detect a specific biomarker. An alternative structure, i.e., a multi-channel structure with multiple test strips, where each test strip has one TL for detecting a specific biomarker, is employed for multiplexed detection of biomarkers. Sometimes, a single test strip with only one TL containing different receptors, where each detection receptor corresponds to a specific biomarker, is another structure applied for multiplexed detection of biomarkers. This paper reviews three common structures for multiplexed detection of biomarkers in LFIAs, i.e., a test strip with multiple TLs, a multi-channel structure with multiple test strips, and a test strip with a single TL. Based on the three common structures, different signal detection strategies that include colorimetric detection, fluorescence detection, surface-enhanced Raman scattering detection, and magnetic detection, along with performance and perspectives are discussed.

A smartphone-based biomedical sensory system.

Disease diagnostics, food safety monitoring and environmental quality monitoring are the key means to safeguard human health. However, conventional detection devices for health care are costly, bulky and complex, restricting their applications in resource-limited areas of the world. With the rapid development of biosensors and the popularization of smartphones, smartphone-based sensing systems have emerged as novel detection devices that combine the sensitivity of biosensors and diverse functions of smartphones to provide a rapid, low-cost and convenient detection method. In these systems, a smartphone is used as a microscope to observe and count cells, as a camera to record fluorescence images, as an analytical platform to analyze experimental data, and as an effective tool to connect detection devices and online doctors. These systems are widely used for cell analysis, biochemical analysis, immunoassays, and molecular diagnosis, which are applied in the fields of disease diagnostics, food safety monitoring and environmental quality monitoring. Therefore, we discuss four types of smartphone-based sensing systems in this review paper, specifically in terms of the structure, performance and efficiency of these systems. Finally, we give some suggestions for improvement and future prospective trends.

The review of Lab-on-PCB for biomedical application.

Prevention of infectious diseases, diagnosis of diseases, and determination of treatment options all rely on biosensors to detect and analyze biomarkers, which are usually divided into four parts: cell analysis, biochemical analysis, immunoassay, and molecular diagnosis. However, traditional biosensing devices are expensive, bulky, and require a lot of time to detect, which also limited its application in resource-limited areas. In recent years, Lab-on-PCB, which combines biosensing technology and PCB technology, has been widely used in biomedical applications due to its high integration, personalized design, and easy mass production. Among these Lab-on-PCB sensing devices, the PCB circuit plays an important role. It can be directly used as a resistance sensor to count cells, and also used as a control device to automatically control the detection device. Flexible PCBs can be used to make wearable medical biosensors. In addition, due to the high degree of integration of the PCB circuit, Lab-on-PCB can perform multiple inspections on the same platform, which reduces the inspection time equivalently. Therefore, in this review paper, we discuss the application of Lab-on-PCB in four analysis methods of cell analysis, biochemical analysis, immunoassay, and molecular diagnosis, and give some suggestions for improvement and future development trends at the end.

The design of a wide bandwidth time marker generator.

The Time Marker Generator (TMG) is an important instrument that is used to calibrate the time base of an oscilloscope. If a direct digital frequency synthesizer is used to generate the necessary waveforms, there is serious distortion in the generated square wave when the ratio of the sampling frequency to the output frequency is non-integer. In addition, the look-up table (LUT) in the direct digital waveform synthesizer needs a large storage capacity to generate a narrow triangular wave at a low output frequency. This paper proposes a design that will generate the time marker whose samples are synthesized by real-time calculation instead of storing them in a LUT. We also propose an M waveform data synthesizer with a parallel structure (M-WDSPS) to reduce the operating clock frequency of the field programmable gate array (FPGA). We built a 4-WDSPS TMG based on this design and reduced the required clock frequency from 1 GHz to 250 MHz, thus making the implementation possible in an FPGA. Our proposed TMG design can provide square wave, pulse wave, narrow triangular wave, and linear triangular wave with different amplitudes in two operating modes: normal mode and highlight mode. The TMG we constructed can provide the time marker with an output frequency range from 10 mHz to 111 MHz, a rising/falling edge time lower than 1 ns, regardless of whether the output frequency is low or high, and a frequency stability lower than 0.1 ppm.

Precisely synchronous and cascadable multi-channel arbitrary waveform generator.

The output bandwidth and the capability to generate multiple analog outputs with accurately adjustable relative phase are important specifications of arbitrary waveform generator (AWG). To increase the output bandwidth, AWG with a multi-memory paralleled direct digital synthesizer structure (DDS) was proposed to break through operating speed limitations of memory and field programmable gate array. But this structure does complicate synchronization of the analog outputs. This paper proposes a structure for synchronization of the outputs of multi-channel high speed AWG that generates arbitrary waveforms using a multi-memory paralleled DDS. Careful distribution of the clock and trigger signals enables elimination of the random initial phase caused by the frequency divider. Based on this structure, a four-channel 600 mega samples per second AWG is designed. An embedded clock synchronization calibration module is designed to eliminate the random phase difference caused by a frequency divider inside a digital-to-analog converter. The AWG provides a 240 MHz bandwidth, 16 mega-samples storage depth, inter-channel initial skew accuracy less than 150 ps, and 0.0001° phase resolution, which can be used to generate two pairs of I/Q signals or a pair of differential I/Q signals for the quadrature modulator. Additionally, more AWGs can be cascaded to obtain more output channels with an output timing skew between adjacent channels of less than 1.6 ns.

Seamless measurement technology of transient signals based on approximate entropy.

The acquisition of waveforms and the analysis of transient characteristics of signals are the fundamental tasks for time-domain measurement, while the reduction of the measuring gap till seamless measurement is extremely important to the acquisition, measurement, and analysis of transient signals. This paper, aimed at the seamless time-domain measurement of non-stationary transient signals, proposes an approximate entropy-based characteristic signal extraction algorithm on the basis of information entropy theories. The algorithm quantitatively describes the complexity (amount of information) of sampled signals using the approximate entropy value, self-adaptively captures characteristic signals under the control of the approximate entropy in real time, extracts the critical or useful information, and removes redundant or useless information so as to reduce the time consumption of processing data and displaying waveforms and realize the seamless time-domain measurement of transient signals finally. Experimental results show that the study could provide a new method for the design of electronic measuring instrument with seamless measurement capability.

Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM.

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.