Miracle-dz/UniFAR-FTUs huggingface dataset:
Torsional MEMS Scanner Based on LiNbO3 Thin Film

This article presents the micro-scanners based on 36Y-cut lithium niobate thin film (LiNbO3) to be used for projection display and optical attenuators. Through the analysis of different design parameters of micro-scanner structure, LiNbO3 micro-scanner has been proved to have lithographically defined scanning frequency with large scanning angles. A micro-scanner of high scanning angle has been designed, fabricated and characterized. The design has a total size of 15 × 270 µ m2, and driving efficiency of 13.21° at 135.5 kHz and input voltage of 10 V. Our devices show great potential for micro-scanners application where it has large potential of raster scanning upon further scaling in driving efficiency.
Cooperative Localization in Wireless Powered Communication Network

Large scale location management in wireless powered communication networks (WPCNs) can benefit the network performance, and it can also provide location based services for IoT applications without maintaining the batteries. However, it is difficult to attain accurate node positions only based on ranging information from anchors. Thus, cooperative localization is an effective way to increase the node positioning accuracy. In this paper, we mainly investigate the performance of cooperative localization in WPCNs, in which the nodes require energy from remote energy access point (E-AP). We firstly analyze the Cramer-Rao Lower Bound (CRLB) for the full connected´ network and the spatial recursive form for a single joint node respectively. Then we propose beamforming schemes to further optimize the cooperative localization performance, which are designed to achieve the minimum localization errors. The simulations demonstrate the highly accurate localization performance of our proposed schemes, which outperform the existing power allocation schemes.
A design strategy for phase synchronization in Precoding-enabled DVB-S2X user terminals

This paper address the design of a phase tracking block for the DVB-S2X user terminals in a satellite precoding system. The spectral characteristics of the phase noise introduced by the oscillator, the channel, and the thermal noise at the receiver are taken into account. Using the expected phase noise mask, the optimal parameters for a second-order PLL intended to track channel variations from the pilots are calculated. To validate the results a Simulink model was implemented considering the characteristics of the hardware prototype. The performance of the design was evaluated in terms of the accuracy and stability for the frame structure of superframe Format 2, as described in Annex E of DVB-S2X.
Towards Autonomous Defense of SDN Networks Using MuZero Based Intelligent Agents

The Software Defined Networking (SDN) paradigm enables the development of systems that centrally monitor and manage network traffic, providing support for the deployment of machine learning-based systems that automatically detect and mitigate network intrusions. This paper presents an intelligent system capable of deciding which countermeasures to take in order to mitigate an intrusion in a software defined network. The interaction between the intruder and the defender is posed as a Markov game and MuZero algorithm is used to train the model through self-play. Once trained, the model is integrated with an SDN controller, so that it is able to apply the countermeasures of the game in a real network. To measure the performance of the model, attackers and defenders with different training steps have been confronted and the scores obtained by each of them, the duration of the games and the ratio of games won have been collected. The results show that the defender is capable of deciding which measures minimize the impact of the intrusion, isolating the attacker and preventing it from compromising key machines in the network.
A Modified Reference Saturated Third Harmonic Injected Equal Loading PWM for VSC-Based Renewable Energy Systems

Mitigation of excessive heat due to high frequency switching of voltage source converter (VSC) is always an industrial concern for superconducting magnetic energy storage (SMES) or high temperature superconducting (HTS) material based power conditioning system with grid-tied renewable energy interface. The pulse width modulation (PWM) technique employed for the switching of the VSC has a significant impact on joules heating, switching and conduction power losses, total harmonic distortion (THD) profile of the VSC output, and conversion efficiency in the SMES/HTS based grid-tied power system. In this paper, a modified reference saturated third harmonic injected equal loading PWM technique is proposed for a VSC-based grid-tied photovoltaic (PV) system, which offers lower THD and reduced power conversion losses. The proposed PWM technique employed in the VSC can be used in a PV-based SMES/HTS integrated power system to improve the performance of the power conditioner. To prove the superiority of the proposed PWM technique, a simulation in MATLAB/Simulink was carried out and the simulation results were validated by laboratory tests.
Nonlinear Wind Energy Harvesting Based on Mechanical Synchronous Switch Harvesting on Inductor

We present for the first time a miniature flexible galloping piezoelectric energy harvester that combines a nonlinear aeroelastic energy harvesting structure with an energy extraction circuit employing mechanical synchronous switch harvesting on inductor (mechanical SSHI). Extra stoppers protect the harvester from overload at high wind speed, thus extending the highest working speed from 10 m/s to 13.5 m/s based on wind tunnel tests. The conductive contact on the bluff body and the conductive stoppers further forms a mechanical synchronous switch, used in the SSHI circuit for energy extraction. The maximum power enhancement reaches 221% compared to the design without SSHI, at a low wind speed of 3.6 m/s.
Matched Manifold Detection for Group-Invariant Registration and Classification of Images

Consider the set of possible observations turned out by geometric and radiometric transformations of an object. This set is generally a manifold in the ambient space of observations. It has been shown [1] that in those cases where the geometric deformations are affine and the radiometric deformations are monotonic, the radiometry invariant universal manifold embedding (RIUME) provides a mapping from the orbit of deformed observations to a single low dimensional linear subspace of Euclidean space. This linear subspace is invariant to the geometric and radiometric transformations and hence is a representative of the orbit. It thus naturally serves as an invariant statistic for solving problems of joint transformation estimation and detection or classification. In the unsupervised detection problem, subspaces evaluated from two observations are tested for the similarity of the observed object and their relative transformation is estimated from the RIUME matrix representation. In the classification set-up the RIUME subspace extracted from an experimental observation is tested against a set of subspaces representing the different object manifolds, in search for the nearest class. We show how to extract a set of mutually orthogonal subspaces, where each subspace represents a different object manifold. In the presence of observation noise, the observations do not lie strictly on the manifold and the resulting RIUME subspaces are noisy. We derive a method for estimating the mean subspace representation of a manifold of deformed observations. To optimize the performance of the matched manifold detector in the presence of observation noise, an analytic solution for choosing the RIUME nonlinear operators is derived, achieving the effect of simultaneous denoising of the object manifolds. The invariant representation of the object is the basis of a matched manifold detection and tracking framework for objects that undergo complex geometric and radiometric deformations. The experimental results on natural scenes demonstrate the generality and applicability of the RIUME framework for classification, detection, and dense registration.
Turing Meets Shannon: Algorithmic Constructability of Capacity-Achieving Codes

Proving a capacity result usually involves two parts: achievability and converse which establish matching lower and upper bounds on the capacity. For achievability, only the existence of good (capacity-achieving) codes is usually shown. Although the existence of such optimal codes is known, constructing such capacity-achieving codes has been open for a long time. Recently, significant progress has been made and optimal code constructions have been found including for example polar codes. A crucial observation is that all these constructions are done for a fixed and given channel and this paper addresses the question whether or not it is possible to find universal algorithms that can construct optimal codes for a whole class of channels. For this purpose, the concept of Turing machines is used which provides the fundamental performance limits of digital computers. It is shown that there exists no universal Turing machine that takes the channel from the class of interest as an input and outputs optimal codes. Finally, implications on channel-aware transmission schemes are discussed.
A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools’ precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring. On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the existence of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.
Haptic-Feedback Ring Enabled Human-Machine Interface (HMI) Aiming at Immersive Virtual Reality Experience

A haptic-feedback ring enabled glove-based human-machine interface (HMI) using a ring-shaped triboelectric nanogenerator (TENG) for finger bending sensing, and a lead zirconate titanate (PZT) thin film for effective haptic sense simulating, is developed for advanced interaction and collaborative operation in robotic manipulation and virtual/augmented reality (VR/AR) area. With the novel voltage integration method, the continuous finger motion can be captured with the minimalistic sensor design, which is difficult to be achieved by conventional contact-separation mode TENGs. With the haptic feedback function enriched by the PZT stimulator, the real sense of touch, compress and force can be successfully simulated, showing great potential in next-generation virtual entertainment and educational training applications.
Smart batteries: requirements of the automotive world

Electrification of passenger cars is one of the most effective approach to reduce noxious emissions in urban areas and, if the electricity is produced using renewable sources, to mitigate the global warming. Automotive battery packages are complex systems that have to comply with many constraints: high performances in terms of gravimetric and volumetric energy and power densities, long cycle and calendar lives, electrical safety, crashworthiness, recyclability. To respect all the above requirements, battery systems integrate a complex battery management system (BMS) connected to an extended network of sensors able to measure the electric potential and the temperature at multiple points in the battery package. New sensing technologies, based on different measurement principles, are emerging as promising approaches to improve the performances of the battery systems and to respond to the needs of the automotive manufacturers.
A new approach to ALMMo-0 Classifiers: A trade-off between accuracy and complexity

In this paper, a new approach to the usage of 0-order Autonomous Learning Multi-Model (ALMMo-0) classifiers is proposed. ALMMo-0 classifiers are fully automatic and do not rely on any hyper-parameters. The creation of clouds relies on normalizing data points by their norm, which may remove an important degree of freedom from the data itself. The proposed approach consists of adding the initial radius of the clouds as an hyper-parameter, which makes it possible to skip the normalization step. This approach requires the search for the ideal value of the hyper-parameter. This way, upon training a set of models with different values for the initial radius, the user is expected to be able to choose from several models which range from more accurate to less complex. This approach was tested on three benchmark problems and compared to the results obtained using the original approach. Furthermore, this approach was also tested on a real dataset (Acute Kidney Injury). The obtained results enhance the versatility provided by the proposed method, successfully allowing the user to choose the model that fits better the design demands regarding accuracy, training time, and complexity.
Whispering: Joint Service Offloading and Computation Reuse in Cloud-Edge Networks

Due to the proliferation of Internet of Things (IoT) and application/user demands that challenge communication and computation, edge computing has emerged as the paradigm to bring computing resources closer to users. In this paper, we present Whispering, an analytical model for the migration of services (service offloading) from the cloud to the edge, in order to minimize the completion time of computational tasks offloaded by user devices and improve the utilization of resources. We also empirically investigate the impact of reusing the results of previously executed tasks for the execution of newly received tasks (computation reuse) and propose an adaptive task offloading scheme between edge and cloud. Our evaluation results show that Whispering achieves up to 35% and 97% (when coupled with computation reuse) lower task completion times than cases where tasks are executed exclusively at the edge or the cloud.
New Exact Nematicon Solutions of Liquid Crystal Model With Different Types of Nonlinearities

The aim of this work is to explore and find new closed-form nematicon solutions for different nonlinearities which occur in nematic liquid crystals (NLC) along with proposing optical system application that utilizes NLC nonlinearities. In particular, Lie point symmetry method is employed to scrupulously inspect and acquire solutions for some interesting cases of nonlinearities which have not been fully examined in literature such as quadratic, generalized dual-power law, and eighth-order nonlinearities. A variety of different nematicon dynamics are observed, including bright solitons, dark solitons and periodic behaviors. The explicit solution form for each dynamical behavior is obtained and the solution dependence on model parameters is investigated. The proposed optical system enables the flexible realization of different types of NLC nonlinearities. To the best of our knowledge, this is the first time to attain explicit exact nontrivial solutions for the particular cases of generalized dual-power law and eighth-order nonlinearities.
Joint User Pairing and Resource Allocation for SWIPT-Enabled Cooperative D2D Communications

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.
Prospect of New AFM Probe Design Enabled by Stress Gradient

Fabrication recipes usually are designed to eliminate residual stress, while majority of electrostatic models considers fringing field to be negligible. The work presented here tries to make a benefit of these two to make a new design for an AFM probe. The curvature caused by residual stress was experimentally exported from profilometer into COMSOL to show the effect of curvature on the probe's static behavior (under DC voltage) at two different beam lengths. The results show that the curvature could turn the forcing on the probe from repulsive to attractive, which is analogues to results in existing literature.
An Experience-Based Direct Generation Approach to Automatic Image Cropping

Automatic Image Cropping is a challenging task with many practical downstream applications. The task is often divided into sub-problems - generating cropping candidates, finding the visually important regions, and determining aesthetics to select the most appealing candidate. Prior approaches model one or more of these sub-problems separately, and often combine them sequentially. We propose a novel convolutional neural network (CNN) based method to crop images directly, without explicitly modeling image aesthetics, evaluating multiple crop candidates, or detecting visually salient regions. Our model is trained on a large dataset of images cropped by experienced editors and can simultaneously predict bounding boxes for multiple fixed aspect ratios. We consider the aspect ratio of the cropped image to be a critical factor that influences aesthetics. Prior approaches for automatic image cropping, did not enforce the aspect ratio of the outputs, likely due to a lack of datasets for this task. We, therefore, benchmark our method on public datasets for two related tasks - first, aesthetic image cropping without regard to aspect ratio, and second, thumbnail generation that requires fixed aspect ratio outputs, but where aesthetics are not crucial. We show that our strategy is competitive with or performs better than existing methods in both these tasks. Furthermore, our one-stage model is easier to train and significantly faster than existing two-stage or end-to-end methods for inference. We present a qualitative evaluation study, and find that our model is able to generalize to diverse images from unseen datasets and often retains compositional properties of the original images after cropping. We also find that the model can generate crops with better aesthetics than the ground truth in the MIRThumb dataset for image thumbnail generation with no fine tuning. Our results demonstrate that explicitly modeling image aesthetics or visual attention regions is not necessarily required to build a competitive image cropping algorithm.
Rule Simplification Method Based on Covering Indexes for Fuzzy Classifiers

A large number of rules increases the complexity of fuzzy classifiers and reduces the linguistic interpretability of the classification. A tabular rule simplification method that extends the Quine-McCluskey algorithm of Boolean design to fuzzy logic is analyzed in detail in this paper. The method obtains a few compound rules from many initial atomic rules. The influence of membership functions as well as t-norms and s-norms operands, which can be even null if many atomic rules are used, becomes apparent in the classification regions (decision boundaries) induced by the compound rules. Since the compound rules can be ordered according to the covering indexes that measure the number of atomic rules covered, more or less generic classification rules and rules with particular indexes can be further identified, which could ease subsequent classification or decision-making.
Magnetically Excited Piezoelectric Energy Harvester for Micropower Supply and Wakeup Applications

A miniaturized, magnetically actuated MEMS energy harvester with a high power density is presented. The device can be excited contactless and at low frequencies thanks to an efficient frequency-up conversion. These characteristics render the harvester suitable for various applications like a compact micropower supply in the sense of classical energy harvesting, but also as a near-zero power wakeup device and for energy transfer in inaccessible locations. Power output up to 100 µW in resonance is reported. Pulse-like excitation in the frequency-up conversion scheme yields open-circuit voltages of up to 4 V. A near-zero power wakeup for stand-alone systems like sensor nodes is demonstrated with a wakeup time of 20 ms and 0.1 nW power consumption when deactivated.
Asynchronous Reception/Detection of 2 RFID Tags

Commercial radio frequency identification (RFID) readers have to resolve collisions between tags without sacrificing throughput. This work proposes a Viterbi joint sequence detector and a 2-symbol joint tag information detector that can resolve collision between two tags in the physical layer. In sharp contrast to prior art, the proposed closed-form signal model takes into account the asynchrony level between the two collided tag responses, due to the low-cost hardware of commercial RFID tags that follow industry’s Gen2 protocol. The asynchrony is considered as the time offset between the beginnings of the two tags’ RN16 responses and is modeled through a derived shaping matrix that depends on the delayed tag information. Performance evaluation of the proposed detectors shows improved performance compared to prior art, under different operating regimes. It is also found that for different time offset values, bit error rate (BER) does not present a monotonic behavior. Finally, it is shown that clustering techniques on the filtered received signal should explicitly take into account the time offset, since the latter can modify the number of observed clusters. Asynchronous detection has been overlooked in classic digital communications; in the era of batteryless, ultra-low cost tags, more work is clearly needed.