Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-polarized Low-Profile Broadband Microstrip Antenna

A dual-polarized microstrip antenna based on a periodic structure is proposed with a dual aperture coupling excitation. A novel feeding mechanism is proposed for the dual-polarization design with a high port isolation. Simulation results show that it has a relative bandwidth of 50% with the |S11| less than -10 dB and the simulation result of port isolation is higher than 38 dB over the passband. Boresight radiation performance has been obtained with a maximum gain of 10 dBi, and the overall height is only 0.047 λ0, λ0 is wavelength for 4 GHz.
A New Bio-Inspired for Cooperative Data Transmission of IoT

The original queen honey bee migration (QHBM) was developed for independent action on solving efficient mobile routing in WSN. In this article, we enhanced the original QHBM using Binary testing injection on the cooperative node’s selection on IoT system. We also added a new cost function for making nodes’ coalition, implementing the threshold value for modified QHBM (mQHBM for short), and demonstrating the mQHBM-CMIMO in fair comparison with another previous algorithms. Our research portrayed that mQHBM can perform better than its competitors such as Fuzzy- BT, Neuro Fuzzy and PSO in terms of network lifetime and the end to end delay.
FCPP: an efficient and extensible Field Calculus framework

The Field Calculus is a promising language for the self-organisation of distributed devices, allowing to express on a high level of abstraction complex distributed algorithms with robust behaviour guarantees. This language has been argued to be fruitfully applicable to many different contexts: wireless sensor networks, internet of things, self-organising edge, fog or cloud computing scenarios, and simulations of such. However, existing implementations of this language rely on the Java Virtual Machine and have an high performance overhead, impairing their usability in contexts where performance is critical (cloud) or computational resources are tightly bounded (WSN/IoT). In this paper we present FCPP, a novel implementation of the Field Calculus as a C++ library. The library is built as a component-based system, in order to be easily extensible to fit different contexts. Furthermore, it leverages C++ template patterns to allow compile-time optimisation and minimal performance overhead, and enables fine-grained parallelism for scalability in self-organising cloud applications. A case study of an edge simulation shows the performance improvement compared to existing Field Calculus implementations, while preserving the same level of abstraction. This translates to a significant speed-up in the development process of distributed algorithms, paving the way towards application scenarios for which existing tools are unsuitable: microcontroller systems and self-organising cloud.
Closed-Form Output Response of Discrete-Time Linear Time-Invariant Systems Using Intermediate Auxiliary Functions [Lecture Notes]

Linear time-invariant (LTI) systems find ubiquitous applications in digital signal processing [1], image processing [2], communication [3], and array processing [4], to name a few. An LTI system can be characterized by its impulse response, which is defined as the output response to an impulse input. For an arbitrary input signal, the output signal of an LTI system is obtained by the convolution of the input signal with the impulse response. This property is closely related to transform-based methods in signal processing, such as discrete-time Fourier transforms, z-transforms, and discrete Fourier transforms. Interested readers are referred to [1] and the references therein.
Integrated moment-based LGMD and deep reinforcement learning for UAV obstacle avoidance

In this paper, a bio-inspired monocular vision perception method combined with a learning-based reaction local planner for obstacle avoidance of micro UAVs is presented. The system is more computationally efficient than other vision-based perception and navigation methods such as SLAM and optical flow because it does not need to calculate accurate distances. To improve the robustness of perception against illuminance change, the input image is remapped using image moment which is independent of illuminance variation. After perception, a local planner is trained using deep reinforcement learning for mapless navigation. The proposed perception and navigation methods are evaluated in some realistic simulation environments. The result shows that this light-weight monocular perception and navigation system works well in different complex environments without accurate depth information.
A Variable Stiffness Soft Continuum Robot Based on Pre-charged Air, Particle Jamming, and Origami

Soft continuum robots have many applications such as medical surgeries, service industries, rescue tasks, and underwater exploration. Flexibility and good accessibility of such robots are the key reasons for their popularity. However, the complexity of their structural design and control systems limit their broader applications. In this paper, a novel variable stiffness soft continuum robot based on pre-charged air, particle jamming, and origami is proposed. The robot is a bellow-like origami structure with internal chambers. A spine-like chamber is filled with particles, and three identical chambers surrounding the spine chamber are filled with pressurized air. When the origami structure is compressed, the particles are jammed by the compression force and the increased pressure of the three air chambers, thus increasing the overall stiffness of the robot. The robot expansion-contraction and bending are controlled by three tendons. An analytical model of the proposed stiffness variation mechanism has been developed. The effects of various parameters on the lateral and axial stiffness of the soft continuum robot have been investigated by experimental studies. A prototype robot has been fabricated to demonstrate grasping operations.
Designing Protograph-Based Quasi-Cyclic Spatially Coupled LDPC Codes With Large Girth

Spatially coupled (SC) low-density parity-check (LDPC) codes can achieve capacity approaching performance with low message recovery latency when using sliding window (SW) decoding. An SC-LDPC code constructed from a protograph can be generated by first coupling a chain of block protographs and then lifting the coupled protograph using permutation matrices. In this paper, we introduce a systematic design to eliminate 4-cycles in a coupled protograph. Further using a quasi-cyclic (QC) lifting, we introduce a procedure for constructing QC-SC-LDPC codes of girth at least eight. This can be interpreted as a multi-stage graph lifting process that yields a greater flexibility in designing QC-SC-LDPC codes with a large girth than previous approaches. Simulation results show the design leads to improved decoding performance, particularly in the error floor, compared to random constructions. Finally, we determine the minimum coupling width required to eliminate 4-cycles in a coupled protograph.
An Analysis on Wireless Edge Caching in In-Band Full-Duplex FR2-IAB Networks

This article develops a 3GPP inspired analytical framework for cache-enabled in-band full-duplex (IBFD) integrated access and backhaul (IAB) heterogeneous network in the FR2 (millimeter wave) band. In particular, by assuming wide-band FR2 channel model, we analyze the performance of cache-enabled IAB nodes that serve multiple users in access in IBFD mode through hybrid beamforming architectures. Since, IBFD transmission is considered, the IAB nodes are able to retrieve any non-cached files simultaneously through backhaul from the IAB donor nodes, which also use hybrid beamforming architectures. Utilizing stochastic geometry tools, we first derive the lower-bound for the average success probability (ASP) of file delivery for the considered system model, using which we evaluate two quintessential performance metrics: latency and average throughput of file delivery under a cache-enabled content delivery phase. Further, as a baseline for comparison, we also consider conventional half-duplex techniques, namely time division duplex and frequency division duplex transmission and evaluate their performance with respect to cache size, node density, content popularity, antenna numbers, blockage density, and residual self-interference. Numerical results demonstrate the precedency of using wireless edge caching as a cost-effective measure for improving the FR2-IAB network’s performance. More importantly, we provide several design insights for the implementation of wireless edge caching in IBFD FR2-IAB networks, with respect to required residual self interference cancellation, increasing antenna number, increasing target data rate and IAB node density.
Speed-Sensorless Control of Linear Induction Motor Based on the SSLKF-PLL Speed Estimation Scheme

In most motor drive applications, the speed sensor is of concern because it potentially deteriorates the system reliability and increases the system cost. To mitigate the adverse effects of speed sensors, various speed estimation schemes are employed to achieve speed-sensorless control of motor drives. With this, the concept of the steady-state linear Kalman filter-based phase-locked loop (SSLKF-PLL) is adopted in this article to enable speed estimation for speed-sensorless control of linear induction motor drives. However, the SSLKF-PLL scheme may be challenged by disturbances, e.g., harmonics and parameter variations in practice. To address this issue, the disturbance rejection schemes, including the prefilters and amplitude normalization unit, are developed for the SSLKF-PLL scheme in this article. Additionally, the dynamic performance analysis and parameter tuning of the SSLKF-PLL scheme are detailed by using the derived small-signal model. Experimental tests have validated the analysis and discussions of the SSLKF-PLL scheme.
A Flexible Multilayered Dry Electrode and Assembly to Single-Lead ECG Patch to Monitor Atrial Fibrillation in a Real-Life Scenario

The aim of this study is to propose a novel flexible dry electrode and assembly to a wearable single-lead ECG patch for continuously monitoring atrial fibrillation (AF) in a real-life scenario. Silver ink was screen-printed on a flexible polyurethane substrate to fabricate the multilayered dry electrode. A wearable recording patch with three dry electrodes was developed to collect single-lead ECG signal, particularly to provide a robust anti-artifact capability by employing a driven right leg electrode as reference. The characterization of the dry electrode based patch was quantified by combing the engineering measurement on raw signal quality and the clinical evaluation of automatical AF detection. A total of 50 patients (age 70 ± 11) were enrolled. Each patient wore one or two patches and a standard Holter simultaneously for a continuous 24 hours ECG recording in a real-life scenario. The averaged R wave peak amplitude, signal-to-noise ratio, and signal-to-artifact ratio were 0.75–1.47 mV, 3–6 dB, and 3–7, respectively. Compared with two different cardiologists’ interpretations of the ECGs from dry electrodes, an offline detection algorithm automatically interpreted AF with the overall accuracy 93.57% and 85.94% during the stationary and movement states, respectively. The obtained results demonstrated the ability of using a flexible screen-printed dry electrode and assembly to a wearable patch to effectively record ECG signal in patients even disturbed by body motions, which may establish its clinical validity and applicability.
Automated Attendance System using Few-Shot Learning Approach

We are living in a world where artificial intelligence is slowly reshaping the world. Artificial Intelligence systems reduce the error rate and can work very efficiently and with incredible speed and precision. Nowadays, Artificial Intelligence is being used to make the technology smarter so it can take decisions and minimize human efforts.An “Automatic attendance system using facial recognition” is one of the smart implementations of Artificial Intelligence. When we stand in front of the camera, it will take our photo and match it with the photos it has in its database. Once the photo is matched with a photo from the database, the system marks that person’s attendance as present in the table and stores it in the database so that we can view the attendance sheet as needed.In this paper we are mainly focusing on one of the major problems in making these systems i.e., requirement of large training data. As the machine learning and deep learning model require a large training data for its model to work with high accuracy, it become a tedious task to collect large no of photos per person. So, in the proposed model we are trying to minimize the training data up to 5 photos per person in a way that it doesn’t affect the accuracy of the whole system. We are using Convolutional neural network and pretrained FaceNet model and we are getting an accuracy of approx. 98% with 100 different faces.
A Tutorial and Review Discussion of Modulation, Control and Tuning of High-Performance DC-DC Converters Based on Small-Signal and Large-Signal Approaches

Many commercial controller implementations for dc-dc converters are based on pulse-width modulation (PWM) and small-signal analysis. Increasing switching frequencies, linked in part to wide bandgap devices, provide the opportunity to increase operating bandwidth and enhance performance. Fast processors and digital signal processing offer new computational techniques for power converter control. Conventional control techniques rarely make full use of operating capability. The objectives of this paper are to present an overview and link to literature on conventional modulation and control techniques for hard-switched dc-dc converters, identify performance limits associated with conventional small-signal-based design, discuss geometric control approaches, and compare strategies for control tuning. The discussion shows how current mode controls have alternative state feedback implementations, and describes unusual opportunities for large-signal control tuning. Considerations for minimum response time are described. Comparisons among tuning methods illustrate how geometric controls can achieve order of magnitude dynamic performance increases. The paper is intended as a baseline tutorial reference for future work on power converter control.
Recurrent MADDPG for Object Detection and Assignment in Combat Tasks

With the development of artificial intelligence, multiagent algorithms have been applied to many real-time strategy games. Making plans on the human being is gradually passing away, especially in combat scenarios. Cognitive electronic warfare (CEW) is a complex and challenging work due to the sensitivity of the data sources. There are few studies on CEW. In the past, wargame simulations depended on differential equations and war theory, which resulted in high time and human resource costs. In the future, as other artificial intelligence theories are developed, artificial intelligence will play a more critical role in wargames. The capabilities of multiagent modeling to describe complex systems and predict actions in dynamic environments are superior to those of traditional methods. In this paper, we use a 3D wargame engine from China Aerospace System Simulation Technology Co., Ltd. (Beijing) named All Domain Simulation (ACS), which supports land, sea, and air combat scenarios, to simulate combat. In the simulations, there are several unmanned air vehicles (UAVs) as attackers and several radar stations as defenders, and both have the ability to detect the others. In the game, several UAVs need to learn to detect targets and track targets separately, and we train the UAV’s behavior by well-designed reward shaping and multiagent reinforcement learning (MARL) with LSTM. We improved the RDPG algorithm and merged the MADDPG and RDPG algorithms. From the experimental results, we can see that the effectiveness and accuracy of the algorithm have been greatly improved.
Comparison of online algorithms for the tracking of multiple magnetic targets in a myokinetic control interface*

Magnetic tracking algorithms can be used to determine the position and orientation of magnetic markers or devices. These techniques are particularly interesting for biomedical applications such as teleoperated surgical robots or the control of upper limb prostheses. The performance of different algorithms used for magnetic tracking was compared in the past. However, in most cases, those algorithms were required to track a single magnet.Here we investigated the performance of three localization algorithms in tracking up to 9 magnets: two optimization-based (Levenberg-Marquardt algorithm, LMA, and Trust Region Reflective algorithm, TRRA) and one recursion-based (Unscented Kalman Filter, UKF). The tracking accuracy of the algorithms and their computation time were investigated through simulations.The accuracy of the three algorithms, when tracking up to six magnets, was similar, leading to estimation errors varying from 0.06 ± 0.02 mm to 2.26 ± 0.07 mm within a 100 mm × 54 mm × 100 mm workspace, at the highest sampling frequency. In all cases, computation times under 300 ms for the UKF and 45 ms for the LMA/TRRA were obtained. The TRRA showed the best tracking performance overall.These outcomes are of interest for a wide range of robotics applications that require remote tracking.
A Survey on Machine Learning Methodologies in Social Network Analysis

Social Network is an emerging area of research today. The amount of information carried by Online Social Networks in the form of text and images is of immense value for data mining and knowledge extraction. There are many approaches to social network analysis including machine learning. Machine learning algorithms work on a set of observable features extracted from user information. Application of machine learning in the field of online social network analysis includes spammer detection, user classification, link prediction, troll pages detection, friend suggestions, community or cluster identification, trend analysis, sentiment analysis of political blogging etc. This paper surveys on the existing work on a) fake profile detection b) personality trait recognition c) depression detection based on using machine learning algorithms in social network analysis and presents a comparative study of the different approaches.
Analytic Combined IMU Integration (ACI2) For Visual Inertial Navigation

Batch optimization based inertial measurement unit (IMU) and visual sensor fusion enables high rate localization for many robotic tasks. However, it remains a challenge to ensure that the batch optimization is computationally efficient while being consistent for high rate IMU measurements without marginalization. In this paper, we derive inspiration from maximum likelihood estimation with partial-fixed estimates to provide a unified approach for handing both IMU preintegration and time-offset calibration. We present a modularized analytic combined IMU integrator (ACI2) with elegant derivations for IMU integrations, bias Jabcobians and related covariances. To simplify our derivation, we also prove that the right Jacobians for Hamilton quaterions and SO(3) are equivalent. Finally, we present a time offset calibrator that operates by fixing the linearization point for a given time offset. This reduces re-integration of the IMU measurements and thus improve efficiency. The proposed ACI2 and time-offset calibration is verified by intensive Monte-Carlo simulations generated from real world datasets. A proof-of-concept real world experiment is also conducted to verify the proposed ACI2 estimator.
Frequency Reconfigurable Dual Notch UWB Antenna

In the paper a detailed demonstration of a dual-band reconfigurable band notched ultra-wide band (UWB) antenna is presented. This includes a simplified metallic radiating circular patch antenna integrated with defected ground structure (DGS) and a pair of L-slot structure and slit which produces the dual-band notch at C-Band and WLAN bands. The suggested antenna can alternately switches to the operating frequency through the four PIN diodes and the related dc bias networks. The designed antenna radiates over 3 to 11 GHz except for the notched bands with satisfactory VSWR (2:1). The measured results of fabricated prototype of the antenna compared with the simulated and it shows the satisfactory agreement in simulated and measured results.
Where and When: Event-Based Spatiotemporal Trajectory Prediction from the iCub’s Point-Of-View

Fast, non-linear trajectories have been shown to be more accurately visually measured, and hence predicted, when sampled spatially (that is when the target position changes) rather than temporally, i.e. at a fixed-rate as in traditional frame-based cameras. Event-cameras, with their asynchronous, low latency information stream, allow for spatial sampling with very high temporal resolution, improving the quality of the data and the accuracy of post-processing operations. This paper investigates the use of Long Short-Term Memory (LSTM) networks with event-cameras spatial sampling for trajectory prediction. We show the benefit of using an Encoder-Decoder architecture over parameterised models for regression on event-based human-to-robot handover trajectories. In particular, we exploit the temporal information associated to the events stream to predict not only the incoming spatial trajectory points, but also when these will occur in time. After having studied the proper LSTM input/output sequence length, the network performance are compared to other regression models. Then, prediction behavior and computational time are analysed for the proposed method. We carry out the experiment using an iCub robot equipped with event-cameras, addressing the problem from the robot perspective.
Analysis of Airgap Field Modulation Principle of Flux Guides

The flux guides are adopted in a number of electric machines, such as the synchronous reluctance machine (SynRM) and the brushless doubly fed reluctance machine (BDFRM). This article further develops a novel and unified theoretical approach for the category of machines with flux guides. Specifically, the magnetomotive force (MMF) modulation operator of variable flux guides is proposed based on the general airgap field modulation theory, to describe the modulation behavior on the source magnetizing MMF distribution in detail. Both machine topologies with flux guides, namely, the SynRM and BDFRM, are addressed in detail from the perspective of airgap field modulation mechanism. It is proven that, three kinds of harmonic components with pole pairs of <inline-formula><tex-math notation="LaTeX">$p_{f}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$(lN_{MB}\pm p_{f})$</tex-math></inline-formula> correspond to <inline-formula><tex-math notation="LaTeX">$p_{f}$</tex-math></inline-formula>pole-pair source magnetizing MMF and their relative amplitudes can be characterized by three magnetic field conversion factors. The magnetic field modulation effects of flux guides and simple salient poles are compared in terms of the torque production ability. In addition, electromagnetic performances such as airgap flux density, torque density, and inductance characteristics are correlated with their initial key topological parameters by certain analytical expressions and compared fairly between the SynRM and BDFRM. The agreement among the theoretical analysis and the two-dimensional (2-D) finite element analysis together with experimental test verifies the effectiveness of the proposed analysis.