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Objective: Infectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. Methods & Results: Avalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods. Conclusions: The proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion. Clinical and Translational Impact Statement— A rapid and multiplex diagnostic system proposed in this work can clinically help to control spread of COVID-19 and other infectious agents as it can provide timely diagnosis, isolation and treatment to patients. Using the system at remoted clinical sites can facilitate early clinical management and surveillance.

For a daily dental practice, the Panoramic (PANO) X-ray film is one of the most commonly used dental X-rays. One of its important advantages is the coverage of most anatomic structures and clinical findings in a single film. Important information about clinical treatment and diagnosis can be provided from the expert analysis of the PANO. Combined with the assistance of artificial intelligence, the application has great potential. The purpose of this study was to propose an automated detection system based on several modern convolutional neural networks (CNNs) for the classification of retained roots, endodontic treated teeth, and implants. In order to meet the standards of practical clinical application, the database used in this study is provided by dentists with more than three years of practical experience. The contributions of this work are given as follows: 1) proposed more advanced techniques for image segmentation and image position in dental radiographs; 2) a better image enhancement is proposed, which improves the accuracy of the five CNNs to more than 96%; and 3) combined with the fuzzy operation to achieve more powerful and accurate anomaly detection. The final result has an accuracy rate of up to 98.75%. It is about 20% higher than previous techniques. This research designed to identify and document each specific finding automatically could help dentists obtain an objective treatment evaluation and provide dentists more precious clinical time for dental operations and communication with patients. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researches discuss the GC molecular progression mechanisms from the perspective of the system level. In this study, we proposed a systems medicine design procedure to identify essential biomarkers and find corresponding drugs for GC. At first, we did big database mining to construct candidate protein-protein interaction network (PPIN) and candidate gene regulation network (GRN). Second, by leveraging the next-generation sequencing (NGS) data, we performed system modeling and applied system identification and model selection to obtain real genome-wide genetic and epigenetic networks (GWGENs). To make the real GWGENs easy to analyze, the principal network projection method was used to extract the core signaling pathways denoted by KEGG pathways. Subsequently, based on the identified biomarkers, we trained a deep neural network of drug-target interaction (DeepDTI) with supervised learning and filtered our candidate drugs considering drug regulation ability and drug sensitivity. With the proposed systematic strategy, we not only shed the light on the progression of GC but also suggested potential multiple-molecule drugs efficiently. [ABSTRACT FROM AUTHOR]

Efficient detection of pavement cracks can effectively prevent traffic accidents and reduce pavement maintenance costs. In order to overcome the complicated and uneconomical disadvantages of traditional crack detection methods, this paper introduces a pavement crack detection network based on deep learning, which can automatically detect pavement cracks and achieves excellent detection accuracy. And the network can easily use the sensors to collect data to facilitate industrial applications. In additional, considering that most cracks have slim feature, we apply the latest Transformer module in the network to improve the effect of cracks detection. Transformer has a strong ability to capture the long-range dependence of the cracks, which enables the network to learn the context information of the crack region. Furthermore, the network also utilizes some techniques to improve the ability of algorithm to detect various cracks. Our network is trained on pavement data sets containing India, the Czech Republic and Japan. It achieved F1 scores of 0.6739 and 0.6650 on two online test sets with fewer network parameters. [ABSTRACT FROM AUTHOR]

Karst water may break into coal tunnels through the rock fissures and cause severe coal mine disasters. The direct current resistivity (DCR) is sensitive to underground water and can be used to detect fissures during the coal mining process. However, the three-dimensional (3-D) measurement of the DCR is still a challenging task and has not been applied to the practical coal mining dynamic process. To bridge this research gap, this study proposes a new 3-D cross-borehole method by monitoring the DCR at multiple points to analyze the geoelectrical field evolution in the underground coal mining process. Based on the forward and inversion theoretical analysis, a DCR observation system is developed for a real unground coal mine to evaluate the cross-borehole points. The 3-D resistivity distributions at different positions in the coal mining process are calculated. The analysis result demonstrates obvious resistivity changes with the evolution of the Karst water zone during the coal mining, and the location and movement of the Karst water can be well estimated. As a result, the proposed 3-D cross-borehole method is very effective for monitoring the DCR and is able to accurately detect the underground water. [ABSTRACT FROM AUTHOR]

Multi-person tracking plays a vital role in intelligent video surveillance systems and has attracted researchers’ growing attention in recent years. This paper proposes a tracking-by-detection method to detect and track all existing persons in video sequences. The proposed method re-identifies detected persons in the latest video frame as observed persons in previous frames and thus generates their trajectories. Re-identification of the proposed approach uses a fusion of six distance metrics. Four metrics, i.e., position, scale, distance to estimated position, and tracklet continuity, are derived from two motion-based features, and two metrics, i.e., dominant colors and histogram of oriented gradients, are derived from corresponding appearance-based features. The proposed method performs tracking in two general steps per each frame. In the first step, all persons in the video frame are detected using the state-of-the-art YOLOv3 object detector. In the second step, the re-identification algorithm generates correspondences between detected persons in the latest video frame and observed persons in previous frames, using the distance matrix built up from compound distances between all detected–observed person pairs. Experimental results show that our simple yet effective approach achieves significant performance in multi-person tracking compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper describes a new type of electrowetting (EW)-driven digital microfluidic (DMF) platform incorporated with an acoustic bubble for improving the viscous droplet mixing performance on an open surface. First, it is experimentally verified that the working principle of the two different actuation methods: EW-driven droplet actuation on an open surface and acoustically driven bubble excitation in an aqueous medium, respectively. Then, it is investigated that the acoustic excitation of the bubble injected into the droplet. When an acoustically excited bubble inside the droplet generates microstreaming at the natural frequency of the bubble, the microstreaming inside the droplet is visualized using fluorescent particles and a high-speed camera. To examine the effect of the microstreaming inside the droplet on mixing performance, the mixture droplet consisting of safranin and glycerol (60 wt%) is used in this experiment. The mixing efficiency is measured through the mixing index calculated by image analysis. The mixture droplet with an acoustically excited bubble is completely mixed within 40 seconds owing to the microstreaming generated by the acoustically oscillating bubble at its natural frequency. On the other hand, the same mixture droplet with only a diffusion process remains unmixed for more than 5 mins. The transportation of a droplet ($10~\mu \text{l}$) containing a bubble ($800~\mu \text{m}$ in diameter) and the removal of the bubble from the droplet are demonstrated using an EW chip. Finally, as a proof of concept, the manipulation of droplets with the enhanced mixing performance on the proposed digital microfluidic platform is demonstrated by the combination of EW droplet actuation and acoustic bubble excitation. [2021-0057] [ABSTRACT FROM AUTHOR]

This paper explores the barriers which hamper the utilization and the routinization of product lifecycle management (PLM) practices in manufacturing organizations. The paper aims to confirm the barriers and their rank in order to improve the PLM institutionalization status in firms. Core barriers have been explored in an automotive firm by applying multicriteria decision-making techniques. Further, the significance of these barriers were corroborated with the executives in the broader PLM domain through a t-test. This study confirmes the “lack of advanced IT infrastructure,” “lack of integration and interoperability,” and “training on the PLM systems” as the top three barriers to PLM utilization and routinization in manufacturing firms in India. [ABSTRACT FROM AUTHOR]

Abnormal noise originating from within faulty products often irritates customers, which may lead to expensive warranty claims. Therefore, it is important to identify these faulty products proactively in the manufacturing process. However, noise detection in a loud shop-floor is not straightforward because inspection in an anechoic chamber is very costly, and some prerequisites for conventional noise reduction and source separation methods, such as stationary and independent signals and prior knowledge about the signal of interest, are sometimes not feasible in practice. Therefore, we developed an in-process noise inspection system that supports dual-channel acoustic data collection during the inspection process. By using two different groups of acoustic signals, abnormal sound separation and noise detection are made possible through three main steps: in-process background noise training, abnormal noise separation, and significance evaluation. The efficiency of the proposed procedure is demonstrated with two case studies: car door trim panels and dual-channel sound generator and collector. [ABSTRACT FROM AUTHOR]

This article proposes a fault-tolerant compensation control approach against nonlinearity, simultaneous additive, and multiplicative actuator faults in Markov jump systems. In this article, we first exploit the fuzzy logic system (FLS) to approximate the nonlinear functions, which have no available knowledge. Then, by utilizing the adaptive backstepping technique, a FLS-based adaptive fault-tolerant compensation controller is proposed, which can completely compensate for the adverse effects, arising from the additive actuator faults, the multiplicative actuator faults, and the mismatched nonlinearity simultaneously. The stability of the closed-loop system can be guaranteed by the proposed FLS-based adaptive controller with the adaptation laws. The novelty of this article lies in the fact that the additive and multiplicative actuator faults, and mismatched nonlinearity are considered simultaneously. Besides, the renown sliding mode control approach has limitations to deal with the FTC problem considered in this article because the considered nonlinearity is a mismatched one. The proposed control approach can cope with the challenging case. Finally, a practical wheeled mobile manipulator system is used to demonstrate the effectiveness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces a new nonlinear voltage-mode controller for a PWM dc-dc buck converter operating in continuous conduction mode. The sliding-mode control technique is used to derive a simplified equivalent control law based on the averaged dc-dc buck converter dynamics. As opposed to the reported design approaches, the proposed control scheme only requires the output voltage in the feedback loop without the need to the capacitor current. Thus, the analogue implementation cost is reduced and the drawback of adding a capacitor current sensor to the power converter is eliminated. The control equation is realized in a simple analogue circuit using few op-amps and resistors, which is suitable for industrial applications. The system modeling and control design procedure along with the derivation of the existence and stability conditions are presented. The control system performance is analyzed using MATLAB and SaberRD simulations. Furthermore, a simple analogue prototype of the simplified nonlinear voltage-mode controlled dc-dc buck converter is developed on a PCB. The simulation and experimental results show that the controller maintains a constant switching frequency, exhibits fast transient response, and provides good tracking performance under large disturbances. [ABSTRACT FROM AUTHOR]

We propose a new hot-electron programming method with a low drain-to-source voltage in a buried-diffusion (BD) bit-line SONOS memory array. In this method, channel electrons are preaccelerated in a cell preceding a program cell. For a small bit-line width, some energetic electrons will traverse an n+ liD region and enter a program cell with residual energy due to nonequilibrium transport. Our measurement result shows that this residual energy can significantly enhance hot-electron programming efficiency even at a Vds of 2.5 V. The concept of this method is verified by means of a Monte Carlo analysis. Our study shows that this method is more effective as a bit-line width reduces. [ABSTRACT FROM AUTHOR]

This brief presents an augmented Markovian system framework that can be applied to model non-Markovian quantum systems. In this augmented system model, ancillary systems are designed to play the role of internal modes of the non-Markovian environment converting white noise to colored noise. To capture the non-classical mutual influence between a quantum system and non-Markovian environments, direct interactions between a principal system and the ancillary system are introduced. Consequently, we showed that the augmented Markovian formulation can be used to theoretically model the environment for observed non-Markovian behavior in a recent experiment on quantum dots. [ABSTRACT FROM AUTHOR]

In this article, we investigate the problem of optimal orientation of the drilling tool which is subject to catastrophe signals in measurement while drilling (MWD). The proposed algorithm is characterized by the signal extraction and filtering of catastrophe signals. First, the reference frames and the orientation of the MWD surveying system, including a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer, are described. Subsequently, the orientation-dependent components, i.e., the Earth's gravity and geomagnetic vector, are decoupled from the measurement signals based on the Coriolis transfer equation. Finally, a new type of filter dealing with the catastrophe signals, i.e., Catastrophe filter, is designed. Performance has been evaluated by the simulations and actual drilling tests and also benchmarked against another most-favored method. The results indicate the proposed algorithm has achieved a higher level of accuracy in MWD. [ABSTRACT FROM AUTHOR]

This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. First, the continuous-time FSMJSs model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Second, the IT2 fuzzy semi-Markov mode-dependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using the Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with $H_{\infty }$ performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system that is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method. [ABSTRACT FROM AUTHOR]

The distributed state estimation problem is examined for a kind of nonlinear time-varying stochastic systems through sensor networks (SNs) with randomly switching topologies as well as redundant channels. The random switches of the topologies for SNs are governed by Markovian jumping parameters and the redundant channels are introduced to help improve the capability of the network communication. We are interested in designing distributed state estimators so that the estimation error dynamics is confirmed to reach a prescribed level of average ${H}_{\infty }$ performance in terms of a finite horizon. Through intensive stochastic analysis, we acquire some sufficient conditions that guarantee the existence of the expected state estimators whose gain parameters are obtained recursively by means of the solution to a series of matrix inequalities. A numerical simulation is carried out to illustrate the validity of the developed state estimation algorithm. [ABSTRACT FROM AUTHOR]

In this study, a simple and low-cost method is proposed to enhance angular color uniformity (ACU) of color-mixed light-emitting diodes (cm-LEDs) by introducing a diffuse coating layer. Optical models of the proposed cm-LEDs packaging modules were constructed by Monte Carlo ray-tracing simulation. The simulation results demonstrate the ACU enhancement of the cm-LEDs packaging modules with the application of the diffuse coating layer on the reflection cup. Owing to the strong light scattering and diffuse reflection effects of the diffuse coating layer, the different color light emitted from the different chip are sufficiently mixed. The diffuse coating layer was fabricated by spraying method. Its optical performance was demonstrated by experiments. The correlated color temperature (CCT) deviations of the cm-LEDs applying the diffuse coating layer have been reduced from 1174 K to 138 K at viewing angles ranging from −80° to +80°. A lowest luminous flux loss of 3.54% is obtained. The diffuse coating layer could also maintain the excellent ACU performance when the average CCTs changes from about 2000 K to 5200 K. Extremely small CCT deviations were obtained, which are less than 300 K at average CCTs ranging from 2000 K to 5200 K. The results demonstrate that the diffuse coating layer on reflection cup is a feasible and effective method to enhance the ACU of the cm-LEDs. [ABSTRACT FROM AUTHOR]

This paper considers the problem of asynchronous ${H}_{{\infty }}$ control for networked Markovian jump systems subject to probabilistic packet dropouts and communication delays in the measurement channel. A new dynamic output-feedback-based asynchronous controller is proposed wherein the dynamic output-feedback controller modes need not synchronize with the system modes. By utilizing results from stochastic Lyapunov–Krasovskii stability theory, sufficient conditions in terms of matrix inequalities are derived such that the closed-loop networked Markovian jump system is stochastically stable and achieves the prescribed ${H}_{{\infty }}$ performance. Using the Schur complement technique and under the assumption that the input matrix is full rank, the sufficient condition is reduced to a linear matrix inequality and the dynamic output-feedback-based asynchronous controller is synthesized. A detailed numerical example with simulation results are presented to evaluate the proposed controller design scheme. [ABSTRACT FROM AUTHOR]

The observation of a cell culture and its growth is a very important part of diagnostics in medical applications as well as biological applications. Especially medical applications benefit from a fast knowledge of the existence of bacteria growth. It can be used for the development of antibiotics as well as for the diagnostics of diseases. The recovered time can be vital for the success of a treatment. We present an approach and device to monitor on-chip bacteria growth based on the absorption measurement with a low limit of detection. A low limit of detection, high sensitivity and a small sample size define the capability of sensing the initial growth rate of small bacteria samples within a few hours. Because of the small height of microfluidic channels, a standard optical density method is not applicable. In our approach, the optical path is 20 times longer compared to an out-of-plane approach by performing an in-plane optical density measurement so that the sensitivity is improved and thereby the limit of detection is lowered. An LED (580 nm center wavelength) is used to propagate light through a sample in the measurement channel. The passing light intensity is measured at the outlet by a photo diode. The relation between the absorbed light and the bacteria concentration agrees well with the theory and a growth curve can be recorded. A particular focus is laid on a reproducible setup based on 3D-printed holders where external disturbances such as ambient light are minimized. Because of the increased sensitivity, the limit of detection is only ${1.5}\times {10}^{{6}}$ bac/ml. By applying the method of this contribution, additional standard laboratory operations can be integrated into chips. [ABSTRACT FROM AUTHOR]

In this work, multifunctional CuO nanowires (NWs) electrode-based extended-gate (EG) field-effect transistor (FET) has been explored for both pH and glucose sensing applications. The CuO nanowires (NWs) electrode-based EGFET gives good pH sensitivity (~48.34 mV/pH), high linearity (99.84%), good stability for pH level in between 2 and 12 for 12 hours with a drift rate of 2.5mV/h, and good reversibility in terms of low hysteresis loss of 2.5mV. The selectivity of this pH sensor towards hydrogen ion is significantly higher as compared to Na+, K+, Zn++ and Mg++ ions. In addition to the pH sensing, CuO NWs based electrode-based EGFET has also been explored for glucose-sensing for the first time without taking the help of neither any enzyme nor any organic receptor. The proposed glucose sensor gives a good sensitivity of 3.03 mV/mM with a high range of linearity (1mM-12mM), which covers up the glucose level of human blood ranging from 3.6 mM to 6.6 mM. This novel concept of CuO NWs based EG-FET glucose sensing is believed to be extended for sensing other saccharides such as fructose, sucrose, and mannose. [ABSTRACT FROM AUTHOR]

The spectral numerical mode-matching (SNMM) method is developed to simulate the 3-D layered multiregion structures. The SNMM method is a semi-analytical solver having the properties of dimensionality reduction to reduce the computational costs; it is especially useful for microwave and optical integrated circuits where fabrication is often done in a layered structure. Furthermore, at some layer interfaces, very thin surfaces such as good conductor surfaces and metasurfaces can be deposited to achieve desired properties such as high absorbance and/or anomalous reflection/refraction. In this article, the 3-D SNMM method is further extended from a single interface to multiple layers so that the electromagnetic propagation and scattering in the longitudinal direction is treated analytically through reflection and transmission matrices by using the eigenmode expansions in the transverse directions. Therefore, it effectively reduces the original 3-D problem into a series of 2-D eigenvalue problems for periodic structures. We apply this method to characterize metasurfaces and lithography models and show that the SNMM method is especially efficient when the longitudinal layer thicknesses are large compared with wavelength. Numerical experiments indicate that the SNMM method is highly efficient and accurate for the metasurfaces and the lithography models. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust finite-horizon filter design problem for a class of two-dimensional (2-D) time-varying systems with norm-bounded parameter uncertainties and incomplete measurements. The incomplete measurements cover randomly occurring sensor delays and missing measurements that are presented in a unified form by resorting to a stochastic Kronecker delta function. The occurrences of the sensor delays and missing measurements are governed by stochastic variables with known probability distributions. The main aim of the addressed problem is to design a recursive filter with appropriate gain parameters that ensure the local minimum of certain upper bound on the estimation error variance at each time instant. With the aid of the inductive approach and the 2-D Riccati-like difference equations, one of the first few attempts is made to tackle the robust filter design problem for 2-D uncertain systems with random sensor delays over a finite horizon. Sufficient conditions are provided for the existence of an upper bound on the estimation error variance, an algorithm is then developed to derive such an upper bound, and finally the desired filter is designed to minimize the obtained upper bound. The filter design procedure is of a recursive form that facilitates the online calculation. A numerical simulation is carried out to show the effectiveness of the developed filtering scheme. [ABSTRACT FROM AUTHOR]

In this paper, a sequence switching control (SSC) scheme based on an auxiliary parallel inductor is proposed to improve the transient performance of buck converters. The proposed SSC scheme introduces a small controlled inductor in parallel with the output inductor, so as to increase the inductor-current slew rate when it is activated during a load transient. Furthermore, an “n+ 1” sequence switching strategy is proposed to control the auxiliary parallel inductor. The proposed scheme divides the transient event inton+ 1 periods. In the formernperiods, the same small output voltage deviations are designed, while in the last period, a smaller deviation is done for a smooth transition from the transient to the steady state. The switching sequence is derived applying the capacitor–charge balance principle for each ofn+ 1 periods. For a given buck converter in the experiment, the settling time and the output voltage deviation of the proposed SSC scheme enhance more than 50% and 67%, respectively, over those of the time-optimal control for a 3-A load step. [ABSTRACT FROM AUTHOR]

This paper presents a highly sensitive and flexible tactile sensor based on 3D microstructure graphene sponges. The graphene sponges are fabricated in a dip-coating process that stacks the graphene layers onto the polyimide scaffolds in a homogeneous graphene solution with graphene oxide serving as the dispersant. The tactile sensor has $3 \times 3$ sensing units and is fabricated through photoetching, magnetron sputtering, and screen-printing processes. The graphene sponge has relatively low Young’s modulus of 16 kPa and is capable of enduring large strains over 60%. Furthermore, the sensor unit with the graphene sponge has a high sensitivity of 0.046 kPa $^{-1}$ at 0.3 ~ 10 kPa, a lower sensitivity of 0.007 kPa $^{-1}$ at 10 ~ 40 kPa, and shows good reproducibility and dynamic response. The increase in the contact areas of the graphene cell walls inside the graphene sponge and the overlapping areas in the laminated graphene flakes synergistically contribute to the high piezoresistive sensitivity. Body wearing experiments to detect wrist bending and muscle vibration demonstrate that the tactile sensor has potential for monitoring body motion and other biomedical applications. [2018–0189] [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a bidirectional distributed amplifier (BDDA) in a 0.18- $\boldsymbol \mu \text{m}$ CMOS process. The performance of the BDDA is theoretically analyzed, and the optimum number of gain stages ($n_{\text {opt}}$), maximum achievable power gain ($G_{P}$), and circuit bandwidth are formulated. In addition, a new formula for proper choice of the number of DA stages (i.e., $n$) is offered where dc-power consumption of the circuit ($P_{\text {dc}}$) is also considered. This formula optimizes $G_{P}/P_{\text {dc}}$ , and it is preferred over the conventional $n_{\text {opt}}$ formula. To validate the theoretical analyses, a 2–12-GHz BDDA with high output 1-dB compression point of +16 dBm and small-signal gain of 10 dB is fabricated. The BDDA chip occupies 1.89-mm2 die area, and its average measured noise figure and $P_{\text {dc}}$ are 6.8 dB and 0.38 W in the high-power mode and 6.5 dB and 0.13 W in the low-power mode, respectively. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault detection problem for a class of networked fuzzy systems with multiplicative noises on both system states and measurement outputs. In view of the limited communication capacity, a periodical communication protocol (i.e., the round-robin protocol) is adopted to undertake the transmission task between the sensors and the fault detection filter, which leads to periodical delays in the overall system. A Takagi–Sugeno fuzzy-model-based fault detection filter is constructed to produce the residual signal and an auxiliary error system is established to facilitate the stability analysis of the error dynamics. With the aid of Lyapunov stability theory, sufficient conditions are obtained that ensure the exponentially mean-square stability of the error dynamics with prescribed $H_{\infty }$ performance constraint. The desired fault detection filter is designed by solving a convex optimization problem via the semidefinite programme method. A finite-time evaluation function and an adjustable threshold are introduced in order to detect the possible faults effectively. The effectiveness of the proposed fault detection scheme is validated by a numerical simulation example. [ABSTRACT FROM AUTHOR]

The distributed filtering problem is addressed in this paper for the discrete-time Takagi–Sugeno (T–S) fuzzy systems with incomplete measurements. The system under consideration includes various network-induced uncertainties, e.g., sensor saturation, quantization error, communication delay, and packet dropouts. Specifically, all these uncertainties are assumed to occur in a stochastic way. In addition, the measurement scheduling issue is also addressed such that only a portion of measurements are broadcasting due to the communication constraints. The main focus is on the design of distributed filters based on the information received locally and from the neighborhood such that the desired estimation performance is guaranteed in terms of the decay rate and disturbance attenuation level. Based on the Lyapunov stability theory, the existence condition for such filters is first proposed and the filter gain parameters are then determined by solving an optimization problem. A simulation example is finally presented to illustrate the effectiveness of the new filtering techniques. [ABSTRACT FROM AUTHOR]

The development of a high static gain dc–dc converter with the integration of the voltage multiplier cell and the coupled inductors operating with soft switching is presented in this paper. The use of coupled inductors and voltage multiplier techniques allow the operation with low switch voltage and low conduction losses. The switch turn-on commutation occurs with zero current switching due to the presence of the coupled-inductor leakage inductance. The leakage inductance also reduces the reverse recovery current of all diodes. The zero voltage switching at the power switch turn-off is obtained with the inclusion of only a diode and a snubber capacitor per phase, without the use of auxiliary switches. The soft-switching range is independent of the load and is maintained even with a light load. The theoretical analysis, design procedure, and experimental results are presented in this paper. A two-phase boost converter with one voltage multiplier and coupling inductors was built with an input voltage $V_{{\text{in}}}= { \text{24 V}}, output voltage Vo= { \text{432 V}}, and output power Po= { \text{400 W}}. The efficiency obtained is equal to 94.55% at the nominal power. [ABSTRACT FROM PUBLISHER]

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index. [ABSTRACT FROM AUTHOR]

The high-technology industry is capital-, technology-, and knowledge-intensive and considerably emphasizes the speed of innovations. Continual updates and upgrades to technology are required for maintaining competitiveness. Research and development (R&D) personnel are core personnel in high-technology industries, but they are insufficient in number. Reducing R&D personnel turnover has become a key topic in the human resource management of innovative enterprises. Numerous studies have verified that low job satisfaction weakens employee morale and organizational commitment, leading to a high turnover rate. To examine the assessment criteria for the job satisfaction of R&D personnel in high-technology industries, fuzzy theory and decision making trial and evaluation laboratory (DEMATEL) were combined into a novel fuzzy–DEMATEL model. First, fuzzy theory was used to examine job satisfaction criteria and fuzzy semantic analysis. Second, the fuzzy–DEMATEL model was used to calculate causal relationships and the degree of influence among all of the criteria. Finally, a model for assessing the job satisfaction of R&D personnel in high-technology industries was developed. The findings of this paper are theoretically innovative and practically applicable to the high-technology industry. [ABSTRACT FROM AUTHOR]

This paper presents a parameter estimation-based time-varying sliding mode controller for the multimotor driving servo systems with the unknown parameters and actuator saturation, such that the finite-time coupling control of load output tracking and motors synchronization is achieved. First, the parameter estimation scheme employs the filtered representation of the load system dynamics to obtain the parameter estimation error, which is utilized to design the adaptive parameter law with dynamic gain to attain the finite-time parameter estimation with the prescribed transient and steady-state performances. Then, the generalized coupling error (GCE) is designed based on the estimation results to transform the complicated coupling control problem into the GCE convergence, which would extremely simplify the controller design. By incorporating the fuzzy echo state network (FESN), a time-varying sliding mode controller is presented to successfully eliminate the reaching phase and guarantee the finite-time GCE convergence with $L_2$ norm performance. For the FESN compensation, a novel learning law is derived based on the norm of FESN weight vector to reduce the computational cost where only a scalar weight needs to be updated online. Finally, the comparative experiments illustrate the benefits and reliability of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

We propose a simple and effective even-power fast algorithm to obtain scalable fringe precision in self-mixing interference. In this letter, the principle of the even-power fast algorithm is introduced in detail. The validity of the proposed algorithm is demonstrated by means of simulated signals and confirmed by experimental measurements at different vibration amplitudes. Without adding any additional optical elements in the optical path, theoretical analysis shows that the resolution of the fringe could be enhanced to ${{\lambda }}/{2^{{\text{n}} + 3}} \text(n\,= \,0,\,1,\,2,\,3\ldots). Experimental results show a fringe precision of \lambda /32 is achieved at a sampling rate of 50 KS/s. The influence factors are also discussed when the algorithm is applied in actual measurements. [ABSTRACT FROM PUBLISHER]

This paper presents a novel detection method for weak sinusoidal signal embedded in strong noise. Two synchronization systems are constructed to implement this method, in which one is used to estimate the frequency and phase angle, the other is used to estimate the amplitude. The detection process is achieved in three steps. First, two synchronization systems are established based on chaotic synchronization theorem and adaptive algorithm. The test signal is injected into their driving ends. Second, in the first synchronization system, the frequency of the sinusoidal signal is obtained by the MUSIC algorithm. Then, the reference sinusoidal signal with this frequency can be generated and correlated with the test signal. The phase difference between them is obtained. Finally, in the second synchronization system, the amplitude of the reference signal is close to the actual amplitude of the sinusoidal signal in the process of synchronization. This detection system is achieved by the Chua's circuit, and its detection characteristics are verified by simulation and experimentation. The results show that it is feasible to obtain the parameters of the sinusoidal signal. It retains excellent detection performance, simplifies parameters setting, avoids the solution of the running state, and is easy to achieve. [ABSTRACT FROM PUBLISHER]

In general, the design cost of a fuzzy controller for nonlinear partial differential systems (PDSs) is very expensive. In this paper, a new approach for robust fuzzy H\infty stabilization design is developed for a class of $N$-dimensional nonlinear parabolic PDSs. Further, two low design-cost robust fuzzy controllers, called the robust fuzzy area-controller and point-controller, as well as a normal design-cost robust fuzzy full-controller are proposed for this problem. The difference between the three control designs lies in their controller placement in the spatial domain. First, we present the $N$-dimensional parabolic Takagi–Sugeno (T–S) fuzzy PDS based on the knowledge-based fuzzy system technique. Next, these three robust fuzzy controllers are constructed via solving diffusion matrix inequality (DMI) problems. With the proposed simple but general method using the Poincaré inequality, the linear matrix inequality problems are provided to replace DMI problems for the robust fuzzy H\infty stabilization designs for computational simplicity. Further, the comparison of these three robust fuzzy controllers are demonstrated to enable a designer to select a low-cost option. Finally, a simulation example is provided to illustrate the design procedure and verify the performance of the proposed designs. [ABSTRACT FROM PUBLISHER]

Modern genetics research has resulted in significant advances in cell in vitro microinjection systems. Such systems provide biological and medical practitioners with high volume cell throughput and statistically relevant data. This paper provides the reader with a comprehensive review of the major research technologies used in automated cell microinjection and of their individual subsystems. Microinjection subsystems reviewed include machine vision, nonvision sensors and user interface (input), cell modeling, piercing mechanisms and injection control loop (control), cell holder and manipulator, and microinjection (output). The interdisciplinary technologies reviewed for microinjection sensing, automation, and control include microfluidic actuation, optical field actuation (optical trapping and optical guidance), electrical field actuation (electrorotation, electrowetting, and dielectrophoresis), and ultrasonic vibration. The survey concludes that research into automated microinjection systems will focus on reducing the scale of microinjection systems and developing appropriate controllers. [ABSTRACT FROM PUBLISHER]

Polarimetric inverse synthetic aperture radar (pol-ISAR) imaging exploits extra scattering information about a target compared with single pol-ISAR. Target description and classification can be improved by incorporating pol-ISAR images. One of the most critical problems in ISAR imaging is accurate motion compensation, specifically for noncooperative targets. Due to the imperfection of coarse motion compensation, phase-adjustment methods are further developed to eliminate the residual phase errors. Unlike taking the contrast or entropy as the measurements in the existing pol-ISAR phase-adjustment algorithms, this paper exploits the sparsity of the scattering centers to correct the phase errors and simultaneously proposes an unambiguous image formation with the sparse aperture signal. The superiority of this algorithm is that the global information of full pol-ISAR images can be properly incorporated to compensate the phase errors and suppress the noise effectively. Experimental results indicate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In large polymer electrolyte membrane (PEM) fuel cell stacks, monitoring and control of the local changes in membrane humidity inside the cathode channel is critical. In this study, a control-oriented dynamic model capable of describing the spatial distribution of voltage and relative humidity (RH) in a large fuel cell stack is developed and experimentally validated. The model tracks energy and mass flow inside the cathode, anode, and coolant channels, as well as the fuel cell stack body. Validation tests show that the model agrees well with the experimental data. The new modeling framework developed in this study can be used to predict the localized effects of humidity on the performance of a fuel cell stack. Also, given its accurate prediction of RH in the stack, this model can be used as an observer to predict local humidity variations that are, otherwise, not available. This capability would allow PEM fuel cells to avoid membrane damage due to low operating humidities as well as efficiency losses due to catalyst layer flooding. [ABSTRACT FROM PUBLISHER]