Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel microfluidic system using a reservoir and flow control system for single-cell release, migration, separation, and characterization

The microfluidic chambers have become the focus of many scientists and researchers because it ensures efficient control for many functions such as cell migration and separation. In addition, the microfluidic is interested in studying the behavior of fluids within microchannels. It is also interested in the technology of the manufacturing of these micro-channels. Single cell migration and separation are the key step in several applications such as lab-on-chip (measurement the electrical properties (i.e. Impedance, and capacitance) of a single-cell), biochemistry, and cell biology. In this study, a novel technique for single-cell migration and separation using a reservoir, flow control and dielectrophoresis (DEP) is purposed. Also, sensors for the capacitance measuring is implemented for cell characterization applications. The design of the proposed microfluidic chamber is simulated and tested using COMSOL Multiphysics 5.4. The results prove the ability of the proposed system for single-cell migration and separation using a reservoir and flow control system at a lower flow rate than previous work through a single sample.
Differentiate Everything with a Reversible Programming Language

This paper considers the source-to-source automatic differentiation (AD) in a reversible language. We start by reviewing the limitations of traditional AD frameworks. To solve the issues in these frameworks, we developed a reversible eDSL NiLang in Julia that can differentiate a general program while being compatible with Julia's ecosystem. It empowers users the flexibility to tradeoff time, space, and energy so that one can use it to obtain gradients and Hessians ranging for elementary mathematical functions, sparse matrix operations, and linear algebra that are widely used in scientific programming. We demonstrate that a source-to-source AD framework can achieve the state-of-the-art performance by showing and benchmarking several examples. Finally, we will discuss the challenges that we face towards rigorous reversible programming, mainly from the instruction and hardware perspective.
Acoustic-Domain Self-Interference Cancellation for Full-Duplex Underwater Acoustic Communication Systems

In full-duplex (FD) underwater acoustic communication (FD-UWAC) systems, the self-interference (SI) will affect the communication performance. Till now, there is no solution for active cancellation of the wide-band SI in the acoustic domain. In this paper, we propose such a solution with two transducers, a primary transducer and a secondary transducer. The acoustic signal emitted by the secondary transducer is generated to cancel the SI signal received at the hydrophone from the primary transducer. The performance of the proposed scheme is investigated by simulation. We use the Waymark UWA simulator that allows the virtual signal transmission in various acoustic environments. The simulation results demonstrate that the proposed scheme can provide an effective acoustic SI cancellation for FD-UWAC systems, in terms of the mean square error and bit error ratio.
Fault-Tolerant D-STATCOM based Matrix Converter

Practical experiences have demonstrated that power semiconductor devices are vulnerable to failures, which challenges the reliability of power converters. One of the effective approaches to enhance the reliability of the power converters is to use fault-tolerant topologies and control techniques. In this paper, a fault-tolerant static synchronous compensator (D-STATCOM) based matrix converter (MC) system for low voltage distribution network is presented. The MC system performance under normal operation and an open-circuit fault are presented. A fault detection strategy is has been implemented based on the comparison of the predicted and measured output voltages. The fault-tolerant converter is able to detect the faults quickly and reconfigure the converter to ensure continuous operation of the D-STATCOM system with good performance. Simulation results are shown to demonstrate the effectiveness of the proposed fault-tolerant D-STATCOM.
Structure and rheology of suspensions of spherical strain-hardening capsules

Abstract We investigate the rheology of strain-hardening spherical capsules, from the dilute to the concentrated regime under a confined shear flow using three-dimensional numerical simulations. We consider the effect of capillary number, volume fraction and membrane inextensibility on the particle deformation and on the effective suspension viscosity and normal stress differences of the suspension. The suspension displays a shear-thinning behaviour that is a characteristic of soft particles such as emulsion droplets, vesicles, strain-softening capsules and red blood cells. We find that the membrane inextensibility plays a significant role on the rheology and can almost suppress the shear-thinning. For concentrated suspensions a non-monotonic dependence of the normal stress differences on the membrane inextensibility is observed, reflecting a similar behaviour in the particle shape. The effective suspension viscosity, instead, grows and eventually saturates, for very large inextensibilities, approaching the solid particle limit. In essence, our results reveal that strain-hardening capsules share rheological features with both soft and solid particles depending on the ratio of the area dilatation to shear elastic modulus. Furthermore, the suspension viscosity exhibits a universal behaviour for the parameter space defined by the capillary number and the membrane inextensibility, when introducing the particle geometrical changes at the steady state in the definition of the volume fraction.
Metantenna: When Metasurface Meets Antenna Again

Metasurfaces, composed of 2-D planar arrays of sub-wavelength metallic or dielectric scatterers, have provided unprecedented freedoms in manipulating electromagnetic (EM) waves upon interfaces. The development of metasurface has always been closely related to antennas. On the one hand, metasurface was developed from reflect arrays/transmit arrays that are used as reflectors/lens of antennas, and most fundamental theories of metasurfaces are directly borrowed from antenna array theories; on the other hand, the development of antennas was flourished and expedited by progresses in metasurfaces. Many emerging antenna configurations have been constructed based on unique functional metasurfaces. In this article, we will review briefly the development roadmap of both metasurfaces and metasurface-based antennas, including antenna-inspired metasurfaces, metasurface-assisted antennas, and metasurface antennas. In particular, the recent fusion of metasurface and antenna as metantenna will bring significant impacts on methodologies of functional metasurface, antenna design, and radio-frequency device miniaturization.
Interference Suppression Based on Single-channel Blind Source Separation in Weather Radar

The interference suppression is an eternal issue in radar system. In this paper, we first proposed a novel single-channel blind source separation (SBSS) algorithm which overcomes the limitation of traditional BSS method, and then the novel SBSS algorithm was performed to weather radar in interference suppression. The proposed SBSS method is inspired by the recently proposed variational mode decomposition (VMD) and the theory of sparse component analysis (SCA). The VMD algorithm was implemented to decompose the single-channel signal into two-channel signals and the improved SCA method was utilized to signal separation with under-determined situation. The numerical results of SBSS demonstrate that the source signals are preeminently retrieved from a single observed mixture. The algorithm is successfully applied to Doppler weather radar with interference of bird. Simulation results showed that the meteorology echo signal and interference signal are obviously separated. In addition, the SBSS algorithm also can be applied to multipath separation, signal denoising and some other situation.
Searching a Suitable Keypoint Detection Network for Robotic Assembly

Traditional automated assembly systems require a large amount of labor to design a well-structured workplace to reduce uncertainties, these systems have difficulty in generalization because their visual detection parts are usually programmed by experts to detect specific visual patterns. Although recent advances in end-to-end deep learning have alleviated people’s burden to a certain extent by allowing them to generalize a system without reprogramming, the process of tuning the neural network hyperparameters still requires a lot of time. To address this problem, we present a system that can detect key features for assembly tasks using the deep learning technique. Besides, our system can search the neural network hyperparameters automatically thus saving users a lot of time and labor to deploy a robotic assembly system. As an example, we will show how to deploy our system on a general purpose robot and use it to assemble an IKEA stool.
Outage Constrained Robust Secrecy Energy Efficiency Maximization for EH Cognitive Radio Networks

In this letter, we investigate the outage-constrained robust secure design in a multiple-input single-output (MISO) energy harvesting (EH) cognitive radio network (CRN), where the malicious energy receivers (ERs) may wiretap the desired information and hence can be treated as potential eavesdroppers (Eves). In particular, considering a non-linear energy harvesting (EH) model, our objective is to design the transmit covariance matrix to maximize the secrecy energy efficiency (SEE) under the given outage probability and transmit power constraints, while satisfying the EH and quality-of-service (QoS) requirements. To tackle the original non-convex problem, we resort to semi-definite relaxation (SDR) and Bernstein-type inequality (BTI)-based approximations to reformulate it into a tractable form. Then, the original problem becomes decomposable and can be efficiently solved by handling a two-stage optimization problem. At last, numerical results are provided to demonstrate the effectiveness and superior performance of the proposed design in comparison with the existing schemes.
Cooperative privacy provisioning for energy harvesting based cognitive multi-relay networks

In order to provide privacy provisioning for the secondary information, we propose an energy harvesting based secure transmission scheme for the cognitive multi-relay networks. In the proposed scheme, two secondary relays harvest energy to power the secondary transmitter and assist the secondary secure transmission without interfere the secondary transmission. Specifically, the proposed secure transmission policy is implemented into two phases. In the first phase, the secondary transmitter transmits the secrecy information and jamming signal through the power split method. After harvesting energy from a fraction of received radio-frequency signals, one secondary relay adopts the amplify-and-forward relay protocol to assist the secondary secure transmission and the other secondary relay just forwards the new designed jamming signal to protect the secondary privacy information and degrade the jamming interference at the secondary receiver. For the proposed scheme, we first analyze the average secrecy rate, the secondary secrecy outage probability, and the ergodic secrecy rate, and derive their closed-form expressions. Following the above results, we optimally allocate the transmission power such that the secrecy rate is maximized under the secrecy outage probability constraint. For the optimization problem, an AI based simulated annealing algorithm is proposed to allocate the transmit power. Numerical results are presented to validate the performance analytical results and show the performance superiority of the proposed scheme in terms of the average secrecy rate.
End-to-end autonomous driving based on the convolution neural network model

In the control algorithm of autopilot system, the Deep Learning method plays a vital role. Since the convolutional neural network (CNN) model used in automatic driving has a huge amount of parameters and the training results are prone to overfitting, an excellent model is necessary. In this paper, an end-to-end control method was proposed to apply a convolutional neural network with a new network structure to control the steering angle and speed of the vehicle and reach the goal of automatic vehicle driving. The experimental results show that it not only greatly reduces the number of parameters, but also keeps the error rate of the experimental results at the low level.
Versatile Dual-Receiver 94-GHz FMCW Radar System With High Output Power and 26-GHz Tuning Range for High Distance Applications

Airborne applications demand exceptional overall radar system performance and eminently high output power for high range target detection. The frequency modulated continuous wave (FMCW) radar system presented in this article is capable of achieving this task due to its high output power at 94-GHz center frequency with over 26-GHz tuning range. Nevertheless, the radar still provides a small form factor and low power consumption of 4.25 W at 5 V single Universal Serial Bus (USB) supply. The key system component is a Silicon Germanium (SiGe) bipolar complementary metal-oxide-semiconductor (BiCMOS) monolithic microwave integrated circuit (MMIC) that contains a 94-GHz voltage-controlled oscillator (VCO), and a 27-GHz VCO for dual-loop phase-locked loop (PLL) stabilization, a power amplifier (PA), and two receive mixers. It generates frequency ramps between 83- and 109-GHz with a maximum output power of 19.7 dBm at its output after the bond wires on the printed circuit board (PCB) and 14.8-dBm output power at the radar’s transmit (TX)-waveguide WR-10-flange. The sensor was also tested in a temperature range from −40 °C to +70 °C with menial deviation. Thus, the system offers high system dynamic range and far distance target detection range. Following a detailed system description, we finally present the FMCW range and Doppler measurements performed with the presented radar sensor as well as the application on unmanned aerial vehicles (UAVs) for flight altitude control and as airborne collision avoidance system (ACAS).
A Deep Learning Approach for Chinese Tourism Field Attribute Extraction

Attribute extraction is a very significant and fundamental task of Natural Language Processing (NLP). It aims to extract missing attribute knowledge for entities from unstructured contexts. Attribute knowledge is an important part of the knowledge graph. Chinese tourism field attribute extraction can help people to construct knowledge graph in Chinese tourism field. In this paper, we crawl attractions information for different travel websites as annotating raw data. We formalize the problem as a sequence labeling task and propose a novel deep sequence labeling model, called BERT-ResCNNs-BLSTM-CRF. First, we fine-tune the pre-trained BERT model to get character embedding. We employ multiple convolutional layers to capture local features from character embedding. In order to tackle the vanishing gradient problem in deep convolutional networks, we use deep residual learning. Then the local features are concatenated with the character embedding vector to feed into Bidirectional long short-term network (BLSTM) to obtain local information of each sentence. Finally, we utilize the conditional random field (CRF) to predict a label sequence for an input sentence. As far as we know, we are the first to propose the joint model for sequence labeling task. Experimental results demonstrate the effectiveness of our method compared with several state-of-art baselines.
Magnetic Milli-Robot Swarm Platform: A Safety Barrier Certificate Enabled, Low-Cost Test Bed

Swarms of micro- and milli-sized robots have the potential to advance biological micro-manipulation, micro- assembly and manufacturing, and provide an ideal platform for studying large swarm behaviors and control. Due to their small size and low cost, tens to hundreds of micro/milli robots can function in parallel to perform a task that otherwise would be too cumbersome or costly for a larger macroscopic robot. Here, we demonstrate a scalable system and modular circuit architecture for controlling and coordinating the motion of <inline-formula><tex-math notation="LaTeX">$ >$</tex-math></inline-formula>10's of magnetic micro/milli robots. By modifying the concepts of safety barrier certificates to our magnetic robot hardware, we achieve minimally invasive, collision-free, 2D position control (x, y) of up to N = 16 robots in a low-cost tabletop (<inline-formula><tex-math notation="LaTeX">$\text{288 mm} \times \text{288 mm}$</tex-math></inline-formula>) magnetic milli-robot platform with up to 288 degrees of freedom. We show that the introduction of random dithering can achieve a <inline-formula><tex-math notation="LaTeX">$100\%$</tex-math></inline-formula> success rate (i.e., no deadlocking), enabling the system to serve as a platform for the study of various swarm-like behaviors or multi-agent robotic coordination.
Towards Intelligent IoT Networks: Reinforcement Learning for Reliable Backscatter Communications

Backscatter communication is becoming the focal point of research for low-powered Internet of things (IoT). However, the intelligence aspect of the backscattering devices is not well-defined. Since future IoT networks are going to be a formidable platform of intelligent sensing devices operating in a self-organizing manner, it is necessary to incorporate learning capabilities in backscatter devices. Motivated by this objective, this paper aims to employ reinforcement learning for improving the performance of backscatter networks. In particular, a multicluster backscatter communication model is developed for shortrange information sharing. This is followed by a power allocation algorithm using Q-learning technique for minimizing the interference in the network. The results of the algorithm are compared with the conventional equal power allocation technique. It is shown that while the equal power allocation approaches capacity ceiling, the proposed algorithm continues to perform better as the number of backscatter devices increase.
Traffic Light Detection for Self-Driving Vehicles Based on Deep Learning

Research on traffic light detection and semantics is important in the field of intelligent vehicles. Better detection and clearer semantics can help prevent traffic accidents by self-driving vehicles at busy intersections and thus improve driving safety. However, complex traffic scenes increase the difficulty of detection and recognition algorithm. This paper presents a method to detect and recognize traffic lights based on Faster RCNN. Our work includes an annotated collection of traffic scene image data, which fills a gap in public traffic light data sets. An optimal feature extraction network is selected through experimental comparisons. Effectiveness of the method has been verified on a self-driving vehicle platform.
Efficiently Generating Test Data to Kill Stubborn Mutants by Dynamically Reducing the Search Domain

Mutation testing is a fault-oriented software testing technique, and a test suite generated based on the criterion of mutation testing generally has a high capability in detecting faults. A mutant that is hard killed is called a stubborn one. The traditional methods of test data generation often fail to generate test data that kill stubborn mutants. To improve the efficiency of killing stubborn mutants, in this article, we propose a method of generating test data by dynamically reducing the search domain under the criterion of strong mutation testing. To fulfill this task, we first present a method of measuring the stubbornness of a mutant based on the reachability condition of a mutated statement. Then, we formulate the problem of generating test data to kill the mutant as an optimization one with a unique constraint. Finally, we generate test data using a coevolutionary genetic algorithm. Given the fact that the domain of test data that kills a stubborn mutant is generally small, we adopt a method of dynamically reducing the search domain to improve the efficiency of the algorithm. We apply the proposed method to test eight benchmark and industrial programs. The experimental results demonstrate that the proposed method has capabilities in seeking stubborn mutants and efficiently generating test data to kill stubborn mutants.
Kernel Prediction Network for Detail-Preserving High Dynamic Range Imaging

Generating a high dynamic range (HDR) image from multiple exposure images is challenging in the presence of significant motions, which usually causes ghosting artifacts. To alleviate this problem, previous methods explicitly align the input images before merging the controlled exposure images. Although recent works try to learn the HDR imaging process using a convolutional neural network (CNN), they still suffer from ghosting or blurring artifacts and missing details in extremely under/overexposed areas. In this paper, we propose an end-to-end framework for detail-preserving HDR imaging of dynamic scenes. Our method employs a kernel prediction network and produces per-pixel kernels to fully utilize every pixel and its neighborhood in input images for the successful alignment. After applying the kernels to the input images, we generate a final HDR image using a simple merging network. The proposed framework is an end-to-end trainable method without any preprocessing, which not only avoids ghosting or blurring artifacts but also hallucinates fine details effectively. We demonstrate that our method provides comparable results to the state-of-the-art methods regarding qualitative and quantitative evaluations.
Time-frequency Analysis of a Flexible RF Sensor for GPR Applications

The detection of utilities in complex underground using Ground Penetrating Radar (GPR) is still challenging due to electromagnetic attenuation and scattering effects. Radio-Frequency (RF) tagging of the buried pipes is a new approach to deal with these problems. In this paper, a flexible and passive tag is designed and tested in sand. Two indexes, i.e., the resonant frequency and amplitude, of the tag are extracted from the time-frequency spectrum based on Variational Mode Decomposition (VMD) with high frequency resolution to study the performance of the tag under different bending angles. The results show that large bending angles result in quick degradation of the tag’s resonance performance, which mainly displays in two aspects, the big frequency shift and the weaken strength of the response signals. Such results demonstrate that the flexible conformal tag is able to enhance the detection, however it is of significance to design the tag which allows large bending angles while the performance degrades slowly.
A Data Structure-Based Approximate Belief Propagation Decoder for Polar Codes

Polar code, as the first code that can probably achieve the capacity of B-DMCs, has received great attention. Belief propagation (BP) decoding algorithm, a paralleled de-coding approach for polar codes, suffers from high hardware complexity. In this paper, we devoted ourselves to proposing a data structure-based approximate BP decoder for polar code. Multiple simulations have been done. The simulation results show that by reforming the data structure of the received channel message and introducing the approximate computing schemes, significant hardware reduction has been made compared to its conventional counterpart. The hardware architecture and corresponding implementation results are also given in this paper.