Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Wideband High-Quality Factor Delay- Tunable Fully Differential All-Pass Filters

In this article, all possible wideband fully differential second-order voltage-mode active all-pass filters (APFs) based on a common-source differential amplifier are systematically derived using two-port network modeling techniques and symbolic math CAD tools. The proposed APF structure is based on the generic two-transistor differential-pair and all possible combinations of the surrounding impedances. In particular, 26 transfer functions and a total of 180 APF circuits, all of which can have a pole quality factor (<inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>) value greater than unity, are found to be possible. Hence, the proposed topology can be used to produce APFs with dispersive delay as well as flat delay for true time delay elements. The operation of some APFs is verified with simulations and a selected circuit with tunable peak delay is designed and fabricated in a 65-nm CMOS process. The implemented filter achieved a maximum peak delay of 88.4 ps at 31 GHz, a constant delay of 11.5 ps over the frequency band 5–20 GHz, and a delay <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor (<inline-formula> <tex-math notation="LaTeX">$Q_{D}$ </tex-math></inline-formula>) value of 3.74, while consuming 1.74 mW from a 1-V supply voltage. The fabricated circuit occupies an area of <inline-formula> <tex-math notation="LaTeX">$0.11\times 0.348$ </tex-math></inline-formula> mm<sup>2</sup> and experimentally demonstrates an APF with the highest <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value of any published second-order APF known to the authors.
High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence

Machine learning systems are vulnerable to adversarial attack. By applying to the input object a small, carefully-designed perturbation, a classifier can be tricked into making an incorrect prediction. This phenomenon has drawn wide interest, with many attempts made to explain it. However, a complete understanding is yet to emerge. In this paper we adopt a slightly different perspective, still relevant to classification. We consider retrieval, where the output is a set of objects most similar to a user-supplied query object, corresponding to the set of $k$ -nearest neighbors. We investigate the effect of adversarial perturbation on the ranking of objects with respect to a query. Through theoretical analysis, supported by experiments, we demonstrate that as the intrinsic dimensionality of the data domain rises, the amount of perturbation required to subvert neighborhood rankings diminishes, and the vulnerability to adversarial attack rises. We examine two modes of perturbation of the query: either ‘closer’ to the target point, or ‘farther’ from it. We also consider two perspectives: ‘query-centric’, examining the effect of perturbation on the query’s own neighborhood ranking, and ‘target-centric’, considering the ranking of the query point in the target’s neighborhood set. All four cases correspond to practical scenarios involving classification and retrieval.
Anchor Design Affects Dominant Energy Loss Mechanism in a Lamé Mode MEM Resonator

We present a Lamé mode resonator whose limiting damping mechanism depends on its anchor geometry. The device is anchor-limited when the anchors are stiffer and is Akhiezer-limited with more compliant anchors. This result is determined by observing the temperature dependence of the quality factor (Q) for devices with different lateral dimensions and different anchor designs. The total measured Q increases by an order of magnitude with the more compliant anchors and reaches a room temperature fxQ product of $\boldsymbol {2.2\times 10^{13}}$ . We studied the relationship between the device design and the anchor design and the measured Q(T) results to identify the contributions from different dissipation mechanisms. This investigation provides insight into how anchor design affects anchor damping. [2020-0195]
Detailed review on embedded MMU and their performance analysis on test benches

The demand for lightweight embedded frameworks is rapidly expanding with the upcoming changes on the Internet-of-Things (IoT) era. Ultra-low power (ULP) processors have guided the development of the implanted lightweight frameworks until this point. In any case, as the IoT time is rapidly advancing, current ULP processors are expected to arrive at a simple breakpoint without the board unit (MMU) being able to run different programs in the embedded MMU-less systems. This review paper provides a comprehensive review on various techniques for analyzing the performance of the embedded MMU units. The study briefs about various performance parameters of MMU such as Memory deallocation and Speed versus fragmentation. It was observed from existing literary works that significant attention was given to the successful execution of the out-scale applications that command in the processing of the datacenter and the effect of equipment support in MMU was ignored. For the meantime, the MMU execution on a real computer is thoroughly reviewed using a variety of scale-out systems using execution counters.
Adaptive and Robust Network Routing Based on Deep Reinforcement Learning with Lyapunov Optimization

The most recent development of the Internet of Things brings massive timely-sensitive and yet bursty data flows. The adaptive network control has been explored using deep reinforcement learning, but it is not sufficient for extremely bursty network traffic flows, especially when the network traffic pattern may change over time. We model the routing control in an environment with time-variant link delays as a Lyapunov optimization problem. We identify that there is a tradeoff between optimization performance and modeling accuracy when the propagation delays are included. We propose a novel deep reinforcement learning-based adaptive network routing method to tackle the issues mentioned above. A Lyapunov optimization technique is used to reduce the upper bound of the Lyapunov drift, which leads to improved queuing stability in networked systems. Experiment results show that the proposed method can learn a routing control policy and adapt to the changing environment. The proposed method outperforms the baseline backpressure method in multiple settings, and converges faster than existing methods. Moreover, the deep reinforcement learning module can effectively learn a better estimation of the longterm Lyapunov drift and penalty functions, and thus it provides superior results in terms of the backlog size, end-to-end latency, age of information, and throughput. Extensive experiments also show that the proposed model performs well under various topologies, and thus the proposed model can be used in general cases. Also the user can adjust the preference parameter at ant time without the need to retrain the neural networks.
Mobile Internet Throughput Prediction using Machine Learning Techniques

Data streaming and internet consumption are some of the most common activities in this 21st century. Streaming can be deployed in many different ways, it can be music, movie and even video game streaming. All these activities rely on the user’s internet throughput. While a broadband or wired connection remains ideal, many people use mobile data to access the services. When the users access internet using mobile data, it may not always have the same internet conditions or speed and thus the content they intend to access should remain dynamic and adaptable to their network changes. The paper explores the techniques that can be used to predict a user’s internet throughput by utilizing various basic parameters, which are easily accessible. This would help the server to predict a user’s throughput beforehand and serve data or content at a bit rate, which suits the throughput and leads to lower buffering/excessive loading and ultimately achieving a better quality of experience (QoE).
Resonant DC/DC converter with Class Φ2inverter and Class DE rectifier based on GaN HEMT

A resonant DC/DC converter with Class Φ2 inverter and Class DE rectifier based on GaN HEMT is designed and validated. A PCB plane transformer is designed to realize the isolation of the primary side and the secondary side of the converter. The output voltage is regulated by hysteresis ON/OFF control. The operation principle and design procedure are introduced in detail.
Modular Multilevel Converter with Distributed Galvanic Isolation: A Decentralized Voltage Balancing Algorithm with Smart Gate Drivers

This work introduces a novel concept of Distributed Galvanic Insulation (DGI) for multilevel converters. By daisy chaining the submodule gate drivers, the requirement in terms of isolation voltage is highly decreased. The use of Smart gate drivers, which implement advanced communication features, allows communication signals to flow along the chain. Applied to MMC, the DGI forbids the use of classical Voltage Balancing Algorithms. A new algorithm, directly integrated on the gate drivers, is proposed for DGI-MMC and is validated by simulation.
Delay Study by Parasitic Capacitance Caused by Inverter Layout

The propagation time can be obtained by using the rising edge of the CLOCK signal reaching the receiver. To do this, it is necessary to synchronize the start time by synchronizing the impulse generator used in the transmitter and the Digital-to-Time Converter(DTC) part used in the receiver. In the receiver, the reference clock enters the delay line of the DTC. At this time, a template signal is generated by using a signal generated through each delay line. In addition, time information can be obtained by using the signal generated by the impulse generator of the receiver transmitted from the transmitter and whether the template signal is correlated with the correlator. Therefore, the DTC's delay line affects the resolution of time information. Therefore, in this paper, we explain the difference and solution of the delay between the simulation in Schematic and the post-layout in the delay line of DTC.
Fiber-Optic Ultrasonic Sensing via Quasi-Continuous Quadrature Frequency Modulation

In this work, we investigated the applicability and effectiveness of ultrasound detection using laser frequency modulated phase shifting interferometry. A quasi-continuous quadrature frequency modulation scheme is proposed to improve the time resolution and dynamic range. Based on a modulated grating Y-branch (MG-Y) laser, we achieve fast and discrete frequency switching with flat intensity output. Selective data sampling is used to eliminate the instability caused by the abrupt changes in drive currents, so as to obtain an effective frequency modulation rate of 10 MHz. Through numerical simulation, the demodulation errors induced by time delay are analyzed. The 5B-step phase shifting algorithm (PSA) shows a better performance. Simulation and experimental results demonstrate that this technique can be effectively used to demodulate extrinsic Fabry–Perot interferometric (EFPI) ultrasonic sensors. With programmable frequency modulation, essential data for signal recovery can be obtained in nanoseconds, which provides a novel perspective for high-speed and high-performance fiber-optic sensing.
LIDAR-based Collision-Free Space Estimation Approach

As autonomous technologies flourish within the vehicle industry, an increasing number of academic autonomous competitions are appearing. One of them is the Shell Eco-marathon Autonomous Urban Concept competition (SEM AUC) which seeks to provide hands-on experience for the academic community to design, build and test their own driverless vehicles within a realistic infrastructure. The team at our university participates in this competition and our concept is to rely on simple and robust algorithms. This paper presents a simple collision-free space estimation algorithm for the LiDAR sensor.
Process Simulation Using Physical Joining Models for Learning Robot-based Assembly Tasks

The ambitious requirements for the economical production of personalized products are one of the key drivers in the development of flexible automation solutions. Novel solution approaches for programming robots using advanced Machine Learning methods show promising results to increase flexibility. These so called intelligent automation solutions can adapt to changes in the product or manufacturing process with only minor human investment. However, the significant amounts of data needed to train the robots still limits the approach. One possible way is to generate this data is utilizing a physics simulation environment. Specialized joining models can be used to further increase the accuracy of the simulation and enrich the generated data as well as reduce the gap between the simulation and the real world. The presented work focussed on the extension of state of the art simulation environments with dedicated joining models for the assembly of electrical components in the electrical cabinet assembly. This extension is intended to make a significant contribution to Simulation-based training for intelligent robot-based automation solutions.
Automatic Students Attendance Marking System Using Image Processing And Machine Learning

To track student attendance, colleges and schools use traditional methods involving manual marking on sheets. These methods are timeconsuming and tedious for teachers. The proposed system automatically records the student’s attendance during lecture hours using facial recognition technology with image processing instead of the traditional manual methods. The proposed system works in a controlled environment, in which students, face images are captured and then upon recognition, their attendance is automatically marked in an Excel sheet. Student’s face is detected using the Viola-Jones technique, whereas Linear Discriminate Analysis (LDA) along with KNN and SVM is used for face recognition. Experimentation shows that it provides better accuracy than the existing PCA and other techniques.
Pose-independent Facial Action units Recognition with Attention Enhanced Residual Mapping

Facial action units (AU) recognition is an essential issue of affective computing, which is important to modern human-computer interaction and virtual reality. Recent advances in deep learning have shown great achievements in facial action unit recognition. However, the conventional approaches are sensitive to the pose of head. To tackle this limitation, we propose a pose-independent AU recognition approach based on attention enhanced deep residual mapping. In the deep feature space, the non-frontal face is mapped to frontal face through attention enhanced residual addition to improve the performance of non-frontal AU recognition. The network consist of three parts: the base network, the residual mapping module and the channel attention enhanced module. The base network is the fine-tuned VGG-Face which are trained with frontal faces. Then, the residual mapping and channel-wise attention mechanism are proposed and introduced into the deep feature space to learn the AU consistent features of faces in different poses. The non-frontal facial features are combined with the residual to map it to frontal face. The channel-wise attention mechanism enables the network to understand which features are more important for the facial pose mapping process. We have demonstrated the effectiveness of our approach on FERA2017 dataset. The experiment results have shown that our approach has improved the face recognition performance.
Secrecy Outage Analysis for Satellite-Terrestrial Downlink Transmissions

This letter investigates the secrecy outage performance of satellite-to-terrestrial downlink transmissions consisting of one legitimate receiver (<inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>) and one eavesdropper (<inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>). To reflect the practical application scenarios, it is assumed that <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula> are randomly distributed in the footprint of the satellite. Furthermore, in the considered downlink scenario, <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula> are equipped with multiple antennas, and maximal ratio combining (MRC) technique is adopted at both of them to seek the maximum receiving diversity gain. To reveal the impacts of the randomness of the positions of <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>, and MRC scheme on the secrecy outage performance of the considered satellite-terrestrial system, the exact and asymptotic closed-form analytical expressions for secrecy outage probability are derived by using stochastic geometry method. Finally, simulation results are given to confirm the accuracy of our proposed analytical models.
New Grid-Connected Multilevel Boost Converter Topology with Inherent Capacitors Voltage Balancing Using Model Predictive Controller

This paper presents a new grid-connected multilevel boost converter topology. The proposed multilevel boost converter includes several switched-capacitor units along with a developed H-bridge. The capability of voltage boosting, inherent voltage balancing of capacitors, reduction of power electronic elements, and voltage on switches are the merits of proposed topology compared to other topologies. All required mathematical analysis related to the suggested topology is presented. The proposed topology is verified by experimental results for grid-connected applications using model predictive controller.
An Osteological Analysis of Holarctic Tree Frogs, Family Hylidae

Holarctic hylids are currently grouped into 4 genera: Hyla, Acris, Pseudacris, and Limnaeodus. A complete osteological analysis of 32 species of holarctic hylids was performed to discover if systematic skeletal differences supported recognition of these taxonomic groups. Detailed examinations of their skeletons revealed that the species differed in 20 osteological characters. Simple matching similarity coefficients were calculated for all of the species and a phenogram was constructed using the weighted pair group method of averages. The osteological analysis suggests that the 32 species form 5 clusters: 1) 7 species of Pseudacris; 2) two species of Acris; 3) Limnaeodus ocularis; 4) Hyla crucifer; and, 5) 21 species of Hyla. Both Acris and Limnaeodus are distinctive osteologically, and both genera appear to be valid. Osteologically, Hyla crucifer appears to be a connecting link between Pseudacris and other Hyla, and literature reports of hydridization studies also suggest that H. crucifer is equally related to both Pseudacris and other holarctic Hyla. Present data, then, suggest that the recognition of Pseudacris as a separate genus is unwarranted, and the species so designated probably represent species within the genus Hyla.
Kernel Low-Rank Entropic Component Analysis for Hyperspectral Image Classification

Principal component analysis (PCA) and its variations are still the primary tool for feature extraction (FE) in the remote sensing community. This is unfortunate, as there has been a strong argument against using PCA for this purpose due to its inherent linear properties and uninformative principal components. Therefore, several critical issues still should be considered in the hyperspectral image classification when using PCA, among which: the large number of spectral channels and a small number of training samples; the nonlinearities of hyperspectral data; the small-sample issue. In order to alleviate these problems, this article employs a new information-theoretic FE method, the so-called kernel entropic component analysis (KECA), which can not only extract more nonlinear information but also can adapt to the limited-sample environment. A theorem of the pivoted Cholesky decomposition is also introduced to improve the efficiency of the KECA. The optimized version can more rapidly implement spectral-spatial features extraction, particularly for large-scale HSIs, while effectively maintaining the clustering performances of KECA. Experiments implemented on several real HSIs verify the effectiveness of the new method armed with a support vector machine classifier, in comparison with other PCA-based and state-of-the-art HSI classification algorithms. The code will also be made publicly available.
Reactive Mission Planning for UAV based crane rail inspection in an automated Container Terminal

At the automated Container Terminal Altenwerder (CTA) Hamburg, Rail-mounted gantry cranes (RMGC) transfer the containers between the incoming ships and the customer container trucks. Due to the special geomorphological conditions of the soil at CTA and heavy load of the crane movement, the rail tracks move in horizontal and vertical plane. Currently, these rail tracks are bi-annually inspected using traditional surveying methods during which the container storage blocks need to be stopped to allow the human surveyors to operate, leading to significant loss of operational capacity. In the AeroInspekt project we aim to inspect the rails using UAV based autonomous aerial photogrammetry without affecting the crane operations. To avoid the bad effect on the photogrammetry process from the images with crane occluding the rails, several intelligent drone mission planning strategies were designed and tested in a simulation environment in order to minimize the flying time. The optimized approach was implemented in the control station app on an Android tablet and tested in practice.
Assessing Intentional Use of AR in Cultural Heritage Learning

In cultural heritage development, augmented reality (AR) technology plays an essential role, seamlessly realizing immersions by merging virtual objects with real images. It not only combines the virtual and the real, but most importantly, increases overall tourists' interest in culture and history. However, limited research was carried out to explore the determinants of adoption towards AR by potential consumers and to what extent do these factors affect. In this paper, a new research horizon is adopted so as to explain the complex relationship of these influencing factors by using quantitative research. Valid data were collected from participants using a national AR tourism platform in China which was developed on Smart-phones. The main results demonstrate that five variables: Performance Expectancy, Social Influence, Facilitating Conditions, Price Value, and Habit significantly influence the individuals' Behavioral Intention to visit the destination. Personal Innovativeness (PI) moderates Habit (HB) on Behavioral Intention (BI). This novel Augmented Reality (AR) graphic travelling system, which provides not only real environment viewing module, but also live voice guide, stimulates cultural heritage learning.