Miracle-dz/UniFAR-FTUs huggingface dataset:
Noncontact displacement sensing with high bandwidth and subnanometer resolution based on squeeze film damping effect

Noncontact displacement measurement with high bandwidth and subnanometer resolution is critical for precision engineering applications. However, the existing displacement sensors with subnanometer resolution either require a special plate to be fixed on the object to be measured or are bulky and inconvenient to integrate with other instruments. The air film height between two plates affects the squeeze film damping coefficient, which has the potential for subnanometer displacement sensing. However, lacking comprehensive analysis, modelling and experimental research, the possibility of the use of the squeeze film damping effect for displacement sensing is still unknown. In this paper, the displacement sensing mechanism based on the squeeze film damping effect is thoroughly analyzed, and the related experiments are conducted. The air film height is converted into the vibration amplitude of a resonant microcantilever. Then, the cantilever vibration amplitude is measured by the optical lever method, and finally, the noncontact sample displacement measurement is realized. The squeeze film damping force on the plate is analyzed, and the model of the vibrated cantilever subject to squeeze film damping force is established. It is found that increasing the driving amplitude can improve the sensitivity of the displacement sensing and reduce the measurement noise. Experimental results show that the resolution of the surface displacement measurement is 0.5nm, the linear region is about 8μm, and the measurement bandwidth of the sensor is 700Hz.
Perfomance Evalution for the GFDM in the vehiculare channel

Since OFDM cannot meet the needs of 5G, scholars from various countries have developed many new multi-carrier transmission technologies to make up for or improve the short-comings of traditional OFDM. Generalized Frequency Division Multiplexing (GFDM) is a flexible multi-carrier transmission technology with many similarities with Orthogonal Frequency Division Multiplexing (OFDM). The main difference is that the carriers are not orthogonal to each other. As a result, GFDM provides better control of out-of-frequency emissions, which reduces the peak-to-average power ratio (PAPR). The simulation evaluation symbol error rate performance for the two-by-two antennas configuration GFDM system with zero-forcing equalization and different modulation schemes in the extended vehicular A (EVA) channel model. The simulation shows that the SER performance decreases as the modulation's order increases. 1
A spacious three-coil magnetic manipulation system

This paper describes the construction and performance of a spacious and easily portable three-coil magnetic manipulation system. A uniform rotating magnetic field with a maximum field strength of 15 mT in a volume of 10 mm × 10 mm × 10 mm working space has been achieved. The three-coil system has been primarily designed for maneuvering helical magnetic nanorobots inside complex biological fluids where a greater magnetic field is desirable to induce maneuverability. The coil design can aid us in our goal of maneuvering magnetic nanorobots inside living animals in the near future while simultaneously imaging them through an optical microscope.
3D intelligent fusion algorithm with data reconstruction ability

In this paper, a camera-lidar fusion method applied to dynamic scenes is proposed. This method can optimize the image characteristics, effectively integrate lidar data and camera data, solve the image distortion caused by external interference, the algorithm has good performance under low-light haze conditions, and the method also achieves a good balance between efficiency and accuracy.
An Improved Intrusion Detection Systems using BGAN

Intrusion Detection Systems (IDS) utilize machine learning to improve their performance in monitoring computer net-works' potential attacks. It is important to understand that the KDD Cup 99 dataset contains simulation data of computer network attack forms. However, because of the complexity of this data, ma-chine learning is required to determine the form of attacks. Artifi-cial Neural Network (ANN) and Random Forest (RF) techniques to classify forms of attacks against machine learning-based Intrusion Detection Systems. However, the performance of ANN and RF is still not optimal with Lower Quartile value below “0.5” for ANN and RF. As a result, we suggest Boundary Seeking Generative Ad-versarial Network (BGAN) to improve performance of ANN and RF in detecting attacks. Results indicate that BGAN can improve ANN and RF performance.
Based on CWGAN Deep Learning Architecture to Predict Chronic Wound Depth Image

In order to observed the healing of the patient's wound, doctors will detect the depth of the wound by inserting a cotton swab into the deepest part of the wound. This processing may cause discomfort for the patient. Therefore, we propose Chronic Wound Depth Generative Adversarial Network (CWGAN) to convert wounds images into wound depth maps which are segmented into four types: shallow, semi-medium, medium, and deep. The accuracy of CWGAN reaches 84.8%. According to the experimental results, this research method can accurately segment wounds of different depths in images and also reduce patients' pain.
Effects of Scene and Human Action Auditory Icons in Stereophonic Image Sonification System

In this paper, we develop the automatic image sonification system using auditory icons and their stereophonic presentation. We evaluated the effects of object sounds, scene sounds, and human action sounds on the sense of presence. The results showed that our system can generate a sound that is close to the ones that would be heard from the image. The results also showed that scene and human action sounds contribute to the improvement of the sense of presence. It is expected that our sonification system provides not only a more immersive experience for sighted people through images, but also an opportunity to share the experience with people with visual impairment through social networking services.
Joint optimization of server and content placement for in-network caching

In-network caching is a technology that enables intermediate routers to store contents. Clients can download the contents from the routers in addition to content servers. Considering optimal placement of servers and contents leads to improve responsiveness and save network resources. This paper discusses joint optimization of server and content placement problems for in-network caching. We formulate integer linear programming to simultaneously determine the location of content servers and content caching. Through numerical experiments, we examine the performance of the joint optimization.
A Doppler and B-Mode Hybrid Ultrasound Tracking Method for Microcatheter Navigation in Noisy Environments

Robot-assisted ultrasound imaging provides a promising microrobot tracking method for biomedical tasks. However, noise artifacts such as interfaces or acoustic shadows have a direct influence on localization accuracy. This paper introduces a novel ultrasound tracking method that uses an ultrasound Doppler image-based noise filter and a template matching algorithm to accurately localize the microcatheter in Brightness mode(B-mode) images. We also propose a robot-assisted ultrasound imaging system that can switch between in-plane and out-of-plane tracking and provides 3D localization of the microcatheter in noisy environments. A magnetic microcatheter design that can generate strong Doppler signals and perform drug delivery is also presented. The tracking performance of the proposed system is experimentally verified both in a tissue-mimicking agarose phantom and an ex vivo phantom made of chicken breast. The proposed method can robustly navigate the microcatheter to the desired position in noisy environments. A blood clot clearance experiment is also demonstrated to verify the effectiveness of the microcatheter.
Adaptive Quasi-Proportional-Resonant Observer-Based Rotor Position Estimation for Sensorless PMSM Drives

It is important to improve the accuracy of rotor position estimation for the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To mitigate the position harmonic error caused by the nonideal factors in high-frequency signal injection based methods, an adaptive quasi-proportional-resonant rotor position observer (AQPRO) for sensorless PMSM drives is proposed in this article. The AQPRO can track the estimation error and generate the compensation value, which can realize online suppression for position error. By analyzing the stability and the influence of parameters of the proposed position observer on the suppression performance, a proportional-resonant gain self-tuning strategy is investigated to improve the suppression effect of the position harmonic error for different loads and speeds. The influence of self-tuning regulator parameters is modeled and analyzed in detail. The proposed method does not require offline detection and has a strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.
Performance Evaluation of Fuzzy-PID and GA-PID Controllers on a 3-DOF Delta Robot Tracking Control

In order to optimize industrial production, the parallel three degrees of freedom (3-DOF) Delta robot has been studied and improved during the time. This study continues to present two methods to improve the quality of the PID controller using fuzzy logic and genetic algorithm, applying to 3-DOF Delta robot. The Fuzzy-PID controllers for the Delta robot are designed using errors and changes of errors between references and system responses and combined with initial parameters to find the optimal values of PID controllers. While the GA-PID controllers are optimally designed to satisfy IAE objective functions based on the starting values of PID controllers determined by the Ziegler-Nichols method. Simulation and comparison results indicate that the combination of fuzzy logic and genetic algorithm with traditional PID controllers are suitable for the improvement of Delta robot tracking control. The proposed controllers are also applied in experiments on the real Delta robot model. Experimental results also indicate that the Fuzzy-PID and GA-PID controllers can control the Delta robot for tracking several trajectories.
Data-Driven Feedforward Hysteresis Compensation with Genetic Algorithm for Atomic Force Microscope*

Nonlinear dynamics of piezo actuators such as hysteresis, distort the Atomic Force Microscopy (AFM) images as they adversely affect the accuracy of the nano-positioning setup. To compensate for the effects of hysteresis on lateral scanner actuators of AFM, a data-driven feedforward controller design algorithm is proposed. The pair of forward and backward images of a sample are used to extract a mapping between the trace and retrace motion of the actuator. A model corresponding to the input-output mapping of the actuator is defined with a set of unknown parameters. The values of these parameters, which shape the hysteresis curves of the actuator, are optimized through defining and solving an optimization problem. A genetic algorithm is utilized as a tool to look for the optimal values. The hysteresis mapping model is then implemented in the form of an inversion-based feedforward controller to correct the scan waveforms and get matching forward and backward images of the sample. The proposed sensor-less data-driven method is easy to implement as it does not depend on the instrument, the sample under study, or the imaging properties.
Distribution Analysis of Dental Plaque Based on Deep Learning

With the advancement of technology, many traditional industries are replaced by machine automation production lines. The reason for this is inevitably the problem of labor costs and in order to improve the quality and speed of products. The replacement of labor by machines is gradually applied in various fields, many new technologies appear. Among them, one of the hottest technologies in recent years is deep learning, especially in reducing labor cost and increasing detection speed and accuracy. Utilizing deep learning can greatly reduce detection time, and improve detection accuracy. The main purpose of this study is to use deep learning to detect dental plaque index, and to use different deep learning methods and algorithms for the posterior row of teeth and gums. Semantic segmentation is used to frame teeth for the posterior row of teeth and gums, and the algorithm is used to calculate the plaque index. The proposed method has been proven to be effective, but further evaluation by dentists will be necessary to prove it effectiveness.
Multiport Hybrid DC Circuit Breaker With Current Flow Control for MTDC Grids

The multiterminal dc (MTDC) grid plays an important role in large-scale power transmission and renewable energy integration owing to its excellent performance. However, there are still some problems existing in the MTDC grid, such as difficult fault current interruption and current flow control, which impedes its development. Currently, the mentioned two problems can be separately solved by hybrid dc circuit breaker (HCB) and current flow controller (CFC). However, when configured separately, they are expensive and have large footprints. Therefore, this article proposes a multiport hybrid dc circuit breaker (MHCB) with current flow control capability. The HCBs near the same dc bus are first integrated into a single MHCB and then novel CFCs are embedded into the MHCB to replace the load commutation switches. Compared with the existing MHCBs, the proposed topology shows advantages in the aspects of implementation cost, power loss, and operation speed. The operation principle is analyzed and the basis for parameter design is given in detail. The effectiveness of the proposed topology is verified by extensive simulations and experiments. Besides, the proposed MHCB is compared with the existing solutions.
Derivative formula and gradient estimate for SDEs driven by $\alpha$-stable processes

In this paper we prove a derivative formula of Bismut-Elworthy-Li's type as well as gradient estimate for stochastic differential equations driven by $\alpha$-stable noises, where $\alpha\in(0,2)$. As an application, the strong Feller property for stochastic partial differential equations driven by subordinated cylindrical Brownian motions is presented.
User Cooperative Mobility to Improve TCP-BBR Throughput in Ad-Hoc Networks

In this paper, we propose a user cooperative mobility to improve Transmission Control Protocol-Bottleneck Bandwidth and Round-trip propagation time (TCP-BBR) throughput. In a single-flow ad-hoc network with TCP-BBR traffic, the TCP-BBR does not work well due to the variation of Round-Trip Time (RTT). By considering the fact that TCP-BBR does not work well, the proposed Data suppression Acknowledge character (ACK) retention method stabilizes the RTT and improves the Wireless Local Area Network (WLAN) bandwidth at the same time. After giving an overview of the proposed method, we show the results of evaluating the throughput characteristics by NS3 simulation. The results show that the proposed method successfully improves the throughput by up to 232% compared to the throughput before moving, which is better than the conventional method, demonstrating the effectiveness of the proposed method.
LSTM-Based Ransomware Detection Using API Call Information

In this paper, we propose a ransomware detection method based on API IDs and call intervals. In the proposed method, to detect ransomware, when each API call occurs, we input both the API ID and the call interval from the previous call into an LSTM (Long Short Term Memory). By inputting the API IDs and call intervals into LSTM, we can learn the characteristics of the time series change of API calls in the ransomware. Through the experiments using an original dataset, we demonstrated that the accuracy of our proposed method was high and the characteristic learning of the call interval was useful for detecting ransomware.
An Automatic System for Manipulation and Rotation of Early Stage 2-Cell Mouse Embryos

Micromanipulation of biological cells such as embryos during preimplantation genetic diagnosis (PGD) requires delicate handling of cells. Specifically, accurate control of the embryo orientation is essential to gain a more favourable position for zona breaching and blastomere biopsy, ensuring embryo survival. Manual embryo reorientation is achieved by aspirating and releasing the embryo arbitrarily using a vacuum-equipped micropipette until the embryo reorients itself in a favourable manner. Unfortunately, this trial and error approach heavily relies on the operator’s skill, ultimately reducing biopsy success rates due to reduced precision in reorientation and ablation. In this study, an automatic image-based feedback orientation controller for early-stage embryos, using conventional IVF lab equipment, is proposed to automate the process of cell reorientation. Rotation of the embryo is achieved by rolling the embryo using a micropipette which grasps the embryo but allows slippage to permit the embryo to roll while in contact with a glass slide substrate. The substrate is mounted on a variable speed x,y stage which is controlled using one of two image-based rotation controllers investigated. The proposed control algorithms estimate the angular velocity of the embryo to determine the rotation angle, creating an error signal when differenced from the reference angle to drive a PID controller that changes the speed of the substrate. The first proposed controller uses the optical flow of the image feed to detect the cell rotation event and uses the kinematic model of the setup to determine the rotation angle. The second proposed controller uses the optical flow as a feedback signal in real-time to estimate the embryo rotation angle. Experimental results with both proposed controllers demonstrate accurate reorientation of the blastomeres, to lay the first steps towards a fully automated cell manipulation system.
Heteroepitaxial growth of Lithium Niobate Thin Films on sapphire substrates with different orientations by Pulsed-Laser Deposition

In this study, epitaxial LiNbO3 thin films were successfully grown on a - and c-Al2O3 substrates by Pulsed Laser Deposition (PLD). In order to control the film crystallinity and chemical composition, the influence of different growth parameters, such as substrate temperature, oxygen pressure, and target composition, were studied. The physical and chemical properties of the as-deposited LiNbO3 layers were characterized.
A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

In a cognitive radio network (CRN), a secondary user (SU) can utilize a channel that is unoccupied by any primary users (PU) in the licensed spectrum to increase spectrum usage efficiency. To provide rendezvous between any pair of SUs and avoid jamming attacks, many solutions applied a channel hopping mechanism. The channel occupancy probability is a factor that heavily affects the performance of a channel hopping protocol. However, most existing channel hopping solutions do not address this issue well. In this paper, we propose the Occupancy, Load and channel usage Probability Awareness Anti-jamming channel hopping protocol (OLAA P). Applying deep learning technology to predict the future PU occupancy probability for each channel, OLAA_P is capable of properly adjusting the usage ratio of each channel. Simulation results verify that OLAA_P outperforms the latest anti-jamming hopping protocol, Load Awareness Anti-jamming channel hopping protocol (LAA), in terms of average time to rendezvous.
Improving the performance of DEMUCS in speech enhancement with the perceptual metric loss

This study attempts to revise the loss function used in the well-known method, DEMUCS, to improve its speech enhancement performance. DEMUCS is a celebrated deep learning architecture initially designed for audio source separation, while it can also be applied to speech enhancement. In DEMUCS, the objective function is to minimize the loss function that contains the time-domain waveform loss and the multi-resolution short-time Fourier transform (STFT) loss. Here, we propose to directly adopt the perceptual metric-aware losses for speech enhancement, which involve perceptual speech quality (PESQ) and short-time objective intelligibility (STOI) to learn the DEMUCS network. The preliminary experimental results indicate that when jointly optimizing either of the two perceptual metrics (PESQ, STOI) and minimizing the original time- and spectral-domain losses, the speech enhancement performance of DEMUCS can be further promoted.
LUCINID BIVALVES FROM ANCIENT METHANE SEEPS Studies

Twelve species of lucinid bivalves are reported from late Jurassic to late Miocene methane-seep deposits worldwide. Among them, eight species and two genera are new. Amanocina n. gen. includes Nipponothracia yezoensis from the Cenomanian of Japan as type species, Cryptolucina kuhnpassetensis Kelly, 2000 from the Berriasian of Greenland, A. raukumara n. sp. from the Albian of New Zealand and A. colombiana n. sp. from the Oligocene of Colombia. Tehamatea n. gen. includes Lucina ovalis Stanton, 1895 and Lucina colusaensis Stanton, 1895 from the late Jurassic to early Cretaceous of California, T. vocontiana n. sp. from the Hauterivian of southern Europe and T. agirrezabalai n. sp. from the Albian of northern Spain. The new species are: Cubatea awanuiensis from the Albian and Cenomanian of New Zealand, Nymphalucina panochensis from the early Palaeocene of California, Elliptiolucina washingtonia from the late Oligocene of Washington State, USA, and Elongatolucina peckmanni from the Oligocene of Colombia. New combinations are provided for Nipponothracia lomitensis (Olsson, 1931) from the Oligocene of Peru and Elliptiolucina hetzeli (Martin, 1933) from the late Miocene of Indonesia. The anterior adductor muscle scar of Beauvoisina carinata is documented for the ﬁrst time. The lucinids found at Jurassic and Cretaceous deep-water methane seeps belong to the subfamily Myrteinae and within this to genera that are restricted to the seep environment ( Beauvoisina , Tehamatea , Amanocina and Cubatea ); shallow-water seeps were inhabited by the codakiine genus Nymphalucina , which is not seep-restricted. Amanocina , Cubatea and Nymphalucina survived into the Cenozoic. Genera that newly colonized deep-water seeps during the Cenozoic include members of both Myrteinae ( Elongatolucina , Nipponothracia and Elliptiolucina ) and Codakiinae ( Epilucina and Lucinoma ); the latter are clearly not seep-restricted.
Arbitrary degree distribution and high clustering in networks of locally interacting agents

Many real world networks, such as social networks, are primarily formed through local interactions between agents. Additionally, in contrast with common network models, social and biological networks exhibit a high degree of clustering. Here we construct a class of network growth models based on local interactions on a metric space, capable of producing arbitrary degree distributions as well as a naturally high degree of clustering akin to biological networks. As a specific example, we study the case of random- walking agents, though most results hold for any linear stochastic dynamics. Agents form bonds when they meet at designated locations we refer to as "rendezvous points. " The spatial distribution of the rendezvous points determines key characteristics of the network such as the degree distribution. For any arbitrary (monotonic) degree distribution, we are able to analytically solve for the required rendezvous point distribution.