Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dual-Output Switched Capacitor DC–DC Buck Converter Using Adaptive Time Multiplexing Technique in 65-nm CMOS

This paper presents an area- and power-efficient dual-output switched capacitor (DOSC) dc–dc buck converter for wearable biomedical devices. The DOSC converter has an input voltage range between 1.05 and 1.4 V and generates two simultaneous regulated output voltages of 1 and 0.55 V. The DOSC consists of two main blocks: a switched capacitor regulator and an adaptive time multiplexing (ATM) controller. The switched capacitor regulator generates a single regulated voltage using pulse frequency modulation based on a predetermined reference voltage. In addition, the ATM controller generates two simultaneous output voltages and eliminates the reverse current using pulse width modulation during the switching between the output voltages. Addressing the reverse current problem is important to reduce the output voltage droop and improve the performance. The proposed converter supports load currents of 10–<inline-formula> <tex-math notation="LaTeX">$350~\mu \text{A}$ </tex-math></inline-formula> and 1–<inline-formula> <tex-math notation="LaTeX">$10~\mu \text{A}$ </tex-math></inline-formula> at load voltages of 1 and 0.55 V, respectively. The DOSC circuit is fabricated in 65-nm CMOS, and it occupies an active area of 0.27 mm<sup>2</sup>. Measured results show that a peak efficiency of 78% is achieved at a load power of <inline-formula> <tex-math notation="LaTeX">$300~\mu \text{W}$ </tex-math></inline-formula>.
A Preliminary Study to Develop a Lightweight Biofeedback Device for Reading Activity

It's widely known that concentration can help to improve one's reading speed and efficiency. In previous researches, we found that concentration of the participants who were carrying out intellectual activities would be increased when we applied brainwave-based biofeedback to them, and the time they spent on current activities would also be prolonged. As the brainwave sensor is susceptible to interference and difficult to wear for a long time, we want to find a new method to get engagement levels by using the sensors which can be equipped to existing wearable equipment, and design a simpler biofeedback system excluding the measurement of brain waves for lightweight devices for reading. In experiment, each participant was requested to perform a 30-minute reading task 10 times. In the meantime, we measured their brain wave, heart rates and skin conductance. The visual analog scale (vas) was used to measure the degree of comprehension, the interest in the book, and the subjective engagement level at that time.
SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.
Distributed Event Triggered Control for Tracking Consensus of General Linear Multi Agent Systems Under Directed Graphs

In this paper, the leader following consensus problem for multi agent systems with general linear dynamics under directed graphs is investigated by means of event triggered strategy scheme. To ensure that the followers can track the leader asymptotically, an event triggered control strategy with state dependent threshold is proposed; where the communication does not occur continuously in both controller updates and triggering condition monitoring. Further, Analysis is provided to show that the proposed approach provides leader following consensus with guaranteed positive inter-event time intervals. Therefore, no Zeno behavior will be exhibited under this proposed strategy. Finally, a numerical simulation example is given to illustrate the effectiveness of the proposed event triggered strategy.
Language Evolution Modeling System

Our purpose is to design a mathematical model of language evolution among the language using community and develop the software system modeling the evolution process. The software main task is to design individual historical scenarios of particular languages, language groups and families, and show the quantitative and qualitative changes in the grammatical and lexical structure of language over time, depending on the social organization of a group of language learners and users.
A Wearable Multifunctional Pulse Monitor Using Thermosensation-Based Flexible Sensors

Objective: This study proposes a novel wearable pulse monitoring system, which can realize the synchronous measurements of pulse wave, skin, temperature, and pulse wave velocity (PWV). Methods: A flexible sensor based on thermosensation is used to detect pressure and temperature stimuli simultaneously. A total of two sensors are integrated to detect pulse transit along two specific points of the artery, e.g., Cun and Chi at a wrist, the data of which are subsequently used to figure out the PWV by using a tailor-designed algorithm conducted in a microprocessor. Calibration experiments and application cases are conducted to validate the effectiveness of the monitor. Results: The developed monitor detects the physiological signals of pulse wave, PWV, and skin temperature simultaneously. In addition, the monitor can measure the pulse changes before and after exercises and track skin temperature variations when warming and cooling. Moreover, the monitor can be also used to detect the local PWV at the wrist. Conclusion: The synchronous measurements of pulse wave, skin temperature, and PWV using a wearable monitor are feasible. Significance: The monitor is small, simple-structured, with multifunction, and thus provides a promising auxiliary approach for traditional Chinese medicine pulse diagnosis.
A Simplified Calculation Model for Micropower Impulse Radar Cross Section

Radar Cross Section (RCS) is a measure of how detectable an object is with a radar, thus, the resolution of the radar. Usually RCS is calculated either through complex mathematical modeling or direct measurement. This paper presents a simple-computation RCS model for Micropower Impulse radar (MIR) to predict RCS, which can be used as a first step to narrow down design selection and design requirement, before embarking on costly direct measurement or complex modeling at the very beginning of the design process. This RCS model is based on radiated power, effective aperture of the receiving port, and receiver's electronic circuitry. The model, in this study, presents an accuracy of 91%, verified by a commercially available MIR system.
Influence of ultrasonic waves on viscosity of edible natural esters based liquid insulation

In many of the high voltage apparatus, liquid insulation is used as the coolant and isolating medium between conducting parts. Petroleum based mineral oil is utilized as one of the liquid insulation in most of the applications such as transformers, oil bushing and etc. Recently many research works are engaged in the development of alternate liquid insulation with vegetable oil based natural esters. Major concern with natural esters is their higher viscosity which influences in the cooling characteristics of liquid insulation. With the aim of reducing the viscosity, the edible natural esters are treated with ultrasonic waves of 100 W ultrasonic power at a fixed frequency of 30 kHz. This work is carried out with edible natural esters such as sunflower oil (SFO), palm oil (PO) and sesame oil (SO). Further analysis on the impact of ultrasonic wave for different exposure conditions, the natural esters are exposed to ultrasonic waves for different time period of 15 minutes to 60 minutes with different temperatures of 30, 60 and 90°C in ultrasonic wave treatment setup. This process results in the reduction of viscosity of natural esters after ultrasonic treatment. The influence of ultrasonic waves on viscosity is studied by measuring the values of viscosity of treated natural esters after 24 and 720 hours for analyzing the retention of reduction in viscosity after treatment. This investigation reveals that the ultrasonic waves have impact on the reduction of viscosity of natural esters. Also the outcome of the ultrasonic treatment is derived as the mathematical model to estimate the viscosity values for different processing temperature and exposure time.
Optimal Torque Control for Large-Scale Wind Turbine Systems Based on Sliding Mode Control

The paper deals with the maximum power point tracking (MPPT) problem in large-scale wind turbine systems. The inertia of the large-scale wind turbine is very big and the optimal torque control (OTC) only considers the steady-state operating point of the system, leading to a long tracking time. For the existing sliding mode control (SMC) based on OTC, the MPPT efficiency is improved at the cost of the fluctuation of generator torque. In this paper, a sliding mode controller with variable parameter is proposed for improving MPPT efficiency. Compared with the existing control strategies, the performance of the proposed method is validated by using a FAST (Fatigue, Aerodynamic, Structure, and Turbulence) model based on the 5MW wind turbine. Simulation results clearly show that a more rapid speed response could be obtained through the proposed method, while the power capture efficiency is increased and the fluctuation of generation torque is decreased.
All-Metal Centipede-Like End-Fire Antenna

An all-metal end-fire leaky-wave antenna is proposed in this letter. The proposed antenna has a centipede-like structure, which consists of a feeding microstrip line and several periodical radiation elements. The all-metal antenna utilizes air medium to intrinsically realize the end-fire array factor. By periodically exciting radiation branch elements, the proposed antenna achieves end-fire radiation with vertical polarization. Compared with previous researches, the proposed antenna has higher end-fire gain and a compact all-metal structure, which contributes to fabrication convenience and low dielectric loss. A prototype of the proposed end-fire antenna is fabricated and tested. The measured results are consistent with full-wave simulation results. The length of the whole leaky-wave array is about 4.13 λ°, and the measured gain is 11.61 dBi in the end-fire direction at 5 GHz.
Number of Source Signal Estimation by the Mean Squared Eigenvalue Error

Detection of the number of source signals (NoSS) in the presence of additive noise is considered. We present a new approach denoted by the mean squared eigenvalue error (MSEE). The MSEE is the mean squared error between the desired noise-free eigenvalues and the available estimated eigenvalues. The approach investigates and analyzes the probabilistic distribution of the available eigenvalue estimates and revisits proper thresholding of these sorted values. The optimum NoSS is provided by minimizing the MSEE. A probabilistic worst-case technique is proposed to estimate the value of the MSEE by using only the available data. It is shown that the proposed method is consistent as the data length increases. It is also shown that the method is consistent as the signal-to-noise ratio (SNR) increases. Simulation results illustrate advantages of the MSEE over competing approaches and confirm effectiveness and robustness of the MSEE even in low-SNR or small sample size scenarios.
Understanding the Diffusion of the Blockchain Technology: A Patent-based Analysis using the tf-lag-idf for Term Novelty Evaluation

Blockchain technology seems to be an important driver in several application fields. It is already being used in the financial industry (where it has led to "Bitcoins" and other electronic currencies), but may also influence the logistics industry (as e.g. enabling unique identifier for components), the service industry (as a replacement for personal identifiers), and others. It is based on the replication of information at several distributed locations. To obtain a better understanding of how blockchain technology has diffused and will continue to diffuse, we carry out a patent analysis. In particular, after compiling a patent set concerning this technology, we classify the respective patents by means of a keyword list. This method helps us identify claims, which deal with business processes (in contrast to manufacturing processes). We extract novel concepts from these claims over the course of time on the basis of informetric measures (tf-lag-idf). The analysis reveals a sequence of broad application fields of blockchain technology, however, only few industry-specific ones. This indicates that managers should evaluate the hype, which presently surrounds block chain technology with great caution. So far, the expectations and potentials discussed in the current literature and media are hardly reflected by patent data. However, the strong increase over recent years should inspire managers from application fields that are not yet at the center of patenting activity to develop related inventions and apply for patents quickly.
Wideband Filtering Unbalanced-to-Balanced Independent Impedance-Transforming Power Divider With Arbitrary Power Ratio

In this paper, a novel wideband filtering unbalanced-to-balanced power divider is proposed. A generalized circuit model for arbitrary power ratio and independent impedance-transforming function is constructed based on the modified even-/odd-mode analysis. Thus, closed-form analytical equations and complete design procedures are provided. Furthermore, the introduced open-circuited stubs are loaded at the output ports to improve the frequency selectivity. Finally, two 2:1 unequal designed prototypes with the 10-dB relative bandwidth of 52% and 65% at the center frequency of 2 GHz are fabricated and measured. A good agreement between electromagnetic simulation and measured results is observed, which demonstrates the validity of the proposed design method and the circuit model.
An Improved Path Planning Algorithm for Unmanned Aerial Vehicle Based on RRT-Connect

For the problem of path planning in complex static environment, in order to reduce the cost of the UAV flight, this paper puts forward an improved algorithm combined with artificial potential field method and RRT-Connect algorithm. Artificial potential field method is oriented, and the RRT-Connect algorithm has the randomness and efficient planning efficiency. Therefore, the improved algorithm makes the planned path closer to the optimal path than the path of the single algorithm. The simulation results show that the proposed algorithm is shorter than the others, and the effectiveness of the proposed algorithm is verified.
Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension

Video-based facial expression recognition has received substantial attention over the past decade, while early expression detection (EED) is still a relatively new and challenging problem. The goal of EED is to identify an expression as quickly as possible after the expression starts and before it ends. This timely ability has many potential applications, ranging from human–computer interaction to security. The max-margin early event detector (MMED) is a well-known ranking model for early event detection. It can achieve competitive EED performance but suffers from several critical limitations: 1) MMED lacks flexibility in extracting useful information for segment comparison, which leads to poor performance in exploring the ranking relation between segment pairs; 2) the training process is slow due to the large number of constraints, and the memory requirement is also usually hard to satisfy; and 3) MMED is linear in nature, and hence may not be appropriate for data in a nonlinear feature space. To overcome these limitations, we propose an online multi-instance learning (MIL) framework for EED. In particular, the MIL technique is first introduced to generalize MMED, resulting in the proposed MIL-based EED (MIED), which is more general and flexible than MMED, since various instance construction and combination strategies can be adopted. To accelerate the training process, we reformulate MIED in the online setting and develop online multi-instance learning framework for EED (OMIED). To further exploit the nonlinear structure of the data distribution, we incorporate the kernel methods in OMIED, which results in the proposed online kernel multi-instance learning for early expression detection. Experiments on two popular and one challenging video-based expression data sets demonstrate both the efficiency and effectiveness of the proposed methods.
Devices and Sensors Applicable to 5G System Implementations

This paper describes some of the device technologies currently in development at Analog Devices, focusing on key areas for future integration within the 5G ecosystem. Three distinct device technologies are examined that will enhance and enable system implementation and are for use in dedicated applications. The first is a state-of-the-art RF MEMs switch, the second, an Energy Harvester for IoT applications and the third, an RF SAW filter.
Research on Face Recognition Based on Ensemble Learning

In order to improve the recognition performance of existing face recognition algorithms, the algorithm of face recognition based on ensemble learning was proposed. First of all, the face library image was down sampled and the number of the face library was changed form 1 to 5. Then the global features of PCA, the local features of LBP and the visual features were extracted and identified for each library, and 15 results of recognition were obtained. Nextly the final recognition result was obtained by using the majority voting algorithm in ensemble learning. The hold process could be processed in parallel in addition to the image down sampling and the majority voting integration. Lastly the algorithms before and after integration were compared and verified in ORL face database, and the results shown that the recognition rate based on the ensemble face recognition algorithm is 97.65% when the number of samples for each person is 5, which is 9.46%, 4.73% and 2.96% higher than that of PCA, LBP and GIST respectively. Under the single training set, the recognition rate reaches 81.5%, which is increased by 23.11% in maximum and by 10.88% in minimum comparing with the single algorithm before integration. It can be seen that the proposed face recognition algorithm is effective and feasible, and it has strong classification recognition effect.
Identification of Protein Complex Based on a Novel Multi-Objective Method

Systematic identification of protein complexes is an important application of data mining in bioscience. The existing computation methods of detecting protein complexes are usually based on the topological properties of protein-protein interaction(PPI) network data. However, limited by the inherent single structure of the PPI network, the mining of protein complexes may not be fully resolved. In this paper, we propose an original multi-objective optimization strategy based protein complex detection method, which is used Adaptive Multi-Objective Black Hole algorithm, called AMOBH. In the selection of objective function strategy, we integrate the topological structure of PPI network data with semantic similarity based on GO (Gene Ontology) annotation data. Experiments demonstrate that the method we adopt provide more convinced results and higher computational efficiency compared with the existing prediction measures.
A Method for Locating Reading Area of Digital Instrument Based on Color Characteristics

The goal of this work is to solve the positioning problems of the digital meter reading area under complicated background. This paper starts with the basic principles and methods of digital image processing, uses a special grayscale empirical algorithm to locate the LCD screen by analyzing the color characteristics of the LCD screen. Experiments on the actual pictures show that the proposed algorithm has good adaptability to most kinds of digital instruments with complicated dial information and effectively solves the positioning problems of the LCD screen, and positioning accuracy.
Plant Defensive β-Glucosidases Resist Digestion and Sustain Activity in the Gut of a Lepidopteran Herbivore

Two-component activated chemical defenses are a major part of many plants’ strategies to disrupt herbivory. The activation step is often the β-glucosidase-catalyzed removal of a glucose moiety from a pro-toxin, leading to an unstable and toxic aglycone. While some β-glucosidases have been well studied, several aspects of their roles in vivo, such as their precise sites of enzymatic activity during and after ingestion, and the importance of particular isoforms in plant defense are still not fully understood. Here, plant defensive β-glucosidases from maize, white mustard and almonds were shown to resist digestion by larvae of the generalist lepidopteran Spodoptera littoralis, and the majority of the ingested activities toward both general and plant pro-toxic substrates was recovered in the frass. Among other proteins potentially involved in defense, we identified specific plant β-glucosidases and a maize β-glucosidase aggregating factor in frass from plant-fed insects using proteomic methods. We therefore found that, while S. littoralis larvae efficiently degraded bulk food protein during digestion, β-glucosidases were among a small number of plant defensive proteins that resist insect digestive proteolysis. These enzymes remain intact in the gut lumen and frass and can therefore further catalyze the activation of plant defenses after ingestion, especially in pH-neutral regions of the digestive system. As most of the ingested enzymatic activity persists in the frass, and only particular β-glucosidases were detected via proteomic analyses, our data support the involvement of specific isoforms (maize ZmGlu1 and S. alba MA1 myrosinase) in defense in vivo.
Towards collaborative multi-device computing

Mobile devices (e.g., smartphone, wearable device) are now an essential part of human life as the most effective and convenient communication tools, providing users with rich experience of various services. Besides traditional web-based services, the research community and industry are exploring computation intensive capabilities of mobile devices to seamlessly augment users' cognitive abilities. However, the resource and communication shortages of a single mobile device make it insufficient in providing high quality of experience (QoE), especially in satisfying the real-time and interactive constraints.Recently, considerable research has addressed these problems, and the proposed systems take advantage of collaborative mobile computing which benefits mobile devices in terms of computing capability, battery life, latency, and data usage. In this survey, we pay our attention to collaborative multi-device computation, and discuss the QoE guarantee, user incentives and other challenges that exit in this area. We also study the networking technologies that can be utilized in multi-device collaboration. In the end of the survey, we point out some future research directions in real-time collaborative mobile systems.