Miracle-dz/UniFAR-FTUs huggingface dataset:
Pitch DBN Based Audio Search with Fog Computing Architecture

Fog computing is an excellent framework with low energy consumption and high effectiveness during data processing, which usually consists of cloud layer, fog layer and terminals. As one of the major information expression and acquirement methods, audio, whose usage for machine automatic search has an essential research significance. However, there are few works about audio search based on fog computing currently. Therefore, in this paper, we firstly analyze the potential of fog computing framework for audio search, then audio feature extraction and pattern matching Deep belief network is utilized for high-representative acoustics feature DBN_F extraction as well as pitch parameters, afterwards the two kinds of features are combined to be a hybrid one with good inimitability and high resolution. The experiments have proven that the audio search accuracy for all the performances are all above 90% using the fog computing framework.
Multi-Level Power Converters using Coupled Inductors and Parallel Connected 2-Level Inverters

Multi-level voltages can be produced for medium and low voltage motor drive applications using parallel-connected standard 2-level coupled inductors inverters with smaller voltage steps to reduce the machine winding stresses. Motor drives can be made small and compact with a significant reduction in the size of drive-related magnetics by generating pwm frequencies number of times higher than the inverter switching frequency, keeping the switching losses low. Coupled inductor modeling techniques have been described that account for the very low output impedance and high impedance between the parallel inverter legs. PWM control techniques and coupled inductor topologies are described that produce high-quality pwm voltages with high pwm frequency to reduce size of output filters. The resultant converter topology is described for a 3-phase system that is modular in design and has a very low output impedance. The latter feature produces very low fundamental voltage drops and is especially suited for generating high fundamental frequencies and lowering losses in high-speed machines. Simulated results have been experimentally validated.
5G RAN: Functional Split Orchestration Optimization

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.
AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification

Event cameras, with low power consumption, high temporal resolution, and high dynamic range, have been used increasingly in computer vision. These superior characteristics enable event cameras to perform low-energy and high-response object classification tasks in challenging scenarios. Nevertheless, specific encoding methods for event-based classification are required owing to the unconventional output of event cameras. Existing event-based encoding methods have focused on extracting semantic and motion information in event signals. However, two main problems exist in these methods: $(i)$ the motion information of event signals leads to mispredictions by the classifiers. $(ii)$ effective evaluation methods to validate the motion robustness of event-based classification models have yet to be proposed. In this work, we introduce an adaptive motion-agnostic encoder for event streams to address the first problem. The proposed encoder would allow us to extract indistinguishable semantic information from an object in different motion circumstances. In addition, we propose a novel motion inconsistency evaluation method to assess the motion robustness of the classification models. We apply our method to several benchmark datasets and evaluate it using motion consistency and inconsistency testing methods. Classification performance shows that our proposed encoder outperforms state-of-the-art methods by a large margin.
A 97% Peak Efficiency and 308 A/in3 Current Density 48-to-4 V Two-Stage Resonant Switched-Capacitor Converter for Data Center Applications

Resonant switched-capacitor (SC) converters have shown great potential in achieving high efficiency and high current/power density, due to their better passives and switch utilization compared to the conventional switched inductor and SC converters. The series-parallel (SP) topology has excellent passive component utilizations, since all flying capacitors sustain the same lowest voltage (i.e. the output voltage) with the minimum voltage derating factors. In this work, a 48 V-to- 4V two-stage hybrid SC converter, cascading a 2-to-1 resonant SC converter (highvoltage stage, using 40 V switches) and a 6-to-1 hybrid series-parallel SC converter (high-current stage, using 25 V switches), is designed, built and tested. The hardware prototype achieves 97% peak system efficiency (including gate driver losses) through the use of zero voltage switching (ZVS) control techniques for both stages. A peak current density of 308 A/in3 or a peak power density of 1154 W/in3 is achieved.
Investigation and Optimization for Planar Coupled Inductor dual-phase interleaved GaN-based Totem-Pole PFC

This paper presents the comparison among different coupled inductors for the GaN-based critical mode (CRM) Totem-Pole PFC. The flux linkage and flux density are deduced both for the positive coupled inductor and the negative coupled inductor. The interleaving layout winding also has been researched for dual-phase coupled inductors. It also investigates both the winding loss and core loss of different coupled inductors. The result shows that the negative coupled planar inductor employing interleaving winding layout can achieve the smallest power loss. Furthermore, a simplified digital control strategy that can make the control design easy to implement for CRM Totem Pole PFC has been proposed and finally it was evaluated by a 1kW MHz dual-phase interleaved Totem-Pole PFC converter prototype.
Phytic Acid Chemistry: Influence on Phytin-Phosphorus Availability and Phytase Efficacy

Abstract Poultry diets are primarily composed of seed-based ingredients and contain a high proportion of their P in the phytic acid molecule, making this P poorly available. Phytic acid (IP6) is a highly reactive acidic compound that readily binds mineral cations, and in this complexed form is called phytin. The chemical characteristics of IP6 influence exogenous (diet) and intestinal phytase efficacy and the availability of both phytin-P (PP) and any minerals bound to IP6. Research has shown that minimizing IP6-mineral complexes in the digesta of poultry can lead to increased PP availability in the absence of dietary phytase, as well as increased efficacy of dietary phytases. Understanding these binding interactions and how they influence the efficacy of different phytases can provide a valuable tool in choosing when to use phytase and what phytase to use under different situations. This understanding can provide the foundation for developing new methods, as well as minimizing the cost and maximizing efficacy of current methods, to reduce the amount of P excreted by poultry.
Predicting malicious hosts by blacklisted IPv4 address density estimation

Internet resource blacklisting is a well-known and effective technique for mitigation of Internet threats. Blacklisting of Internet Protocol (IP) addresses and domains is present as a de-facto component in a variety of systems such as threat intelligence services, intrusion detection systems (IDS), intrusion prevention systems (IPS). Nevertheless, it suffers from an obvious flaw – inability to mitigate threats from zero-day malicious Internet resources. To address this issue numerous predictive blacklisting approaches were developed in recent years. This study presents a novel predictive blacklisting approach, based on blacklisted IP probability density function estimation with Gaussian Mixture Model (GMM). We evaluate the developed approach on two tasks: estimation of model accuracy in predicting future blacklisted IPs and intersection of generated blacklist with real blacklist database. Experiments provided on the dataset of total 270200 unique blacklisted IPs collected from the list of open blacklist providers. We compare our solution both with recent predictive blacklisting solutions and with rule-based approaches. Experimental results show that proposed GMM-based approach outperforms the recent predictive blacklisting approaches in terms of new threats mitigation: it has 2-4 times smaller "predicted" blacklist, high F1-score of 0,916 (which means low level of false detections). As a result, our approach provides a better solution for realworld threat prevention scenarios. Another contribution of this paper is the evaluation of overall applicability of proximity-based methods for blacklisted IP prediction tasks.
True Detect: Deep Learning-Based Device-Free Activity Recognition Using WiFi

Activity recognition has taken an important role in smart devices and has a wide range of applications. Traditional approaches to activity recognition either require the target to carry electronic sensors or require dedicated devices such as software-defined radios, which may not be feasible in many situations. This paper provides an overview of device-free activity recognition techniques and proposes a convolutional neural network-based approach for classifying different human activities by leveraging the channel state information (CSI) obtained using off-the-shelf hardware. The design and implementation of the deep learning framework, to classify activities in a live scenario, are also discussed. The results show that, in controlled environments, a high activity recognition accuracy can be achieved using CSI and that the framework can also be implemented in live scenarios.
Hybrid 2/3L Inverter with Unequal PV Array Voltages

In Photovoltaic (PV) systems with a two-level (2L) voltage source inverter (VSI), the occurrence of partial shades and/or the mismatching of PV arrays results in poor utilization of available power. In order to overcome this issue, a new configuration known as hybrid 2/3 level (2/3L) inverter is presented in this paper. The topology is formed by adding two half bridge modules to 2L VSI. It addresses one of the common phenomena which is the unequal voltage sharing of the PV arrays addresses by a new switching sequence. This new switching sequence allows the independent operation of two PV arrays to yield maximum output. The performance of proposed hybrid 2/3L inverter configuration is studied and compared with the conventional switching sequence for varied input voltages. The effect of unequal voltage sharing in PV arrays does not reflect in the ac side waveforms when the proposed switching sequence is used and it is evident from the simulation and hardware results.
Upper-Bounded Price of Anarchy in D2D Communications Over 5G Edge-Computing Mobile Wireless Networks

Device to Device (D2D) communications provide a means of boosting communication capacity. As there is a gap between the D2D requests and the available spectrum resource, it is critical to design an allocation rule for user equipment and payment. We design two mechanisms from a linear programming perspective and the maximum in range perspective, respectively. Both mechanisms can achieve the upper-bounded approximation ratios in terms of price of anarchy for D2D communications over 5G edge-computing mobile networks. These two mechanisms have found various applications in other disciplines as well. We conduct numerical studies and compare our work with a greedy method. We show that the proposed algorithm outperforms the greedy method.
A Practical Intrusion Visualization Analyzer based on Self-organizing Map

In the era of big data, devices and services are interconnected through the network. For the purpose of ensuring the security and reliability of network communication and resisting the threat of intrusions, Network Intrusion Detection Systems (NIDS) have become an infrastructure in the Internet ecosystem. It is challenging to develop an effective NIDS which can provide visualization ability due to the high-dimensional characteristic of network traffic. In our work, a practical NIDS based on the Self-Organizing Map (SOM) neuron network is presented. The proposed method can arrange high-dimensional data in a 2D topological graph in an orderly manner, which enables an easy-to-understand insight of the distribution of normal or abnormal data. With the training, similar data is grouped together, based on which intrusions can be identified. In addition, a hybrid preprocessing model that combines two different feature selection methods is adopted to accelerate the detection process. In order to evaluate the performance of the proposed framework in terms of several different metrics, experiments are performed over a benchmark dataset UNSWNB15 which is widely used in intrusion detection research. Experiment results show that the proposed approach provides a user-friendly visualization and enhances the detection of intrusions.
Discontinuous Bi-tri Logic SPWM for Current Source Converter with Optimized Zero-state Replacement

This paper proposed an optimized zero-state replacement (ZSR) method for discontinuous Bi-tri logic SPWM (BTSPWM) to reduce the switching frequency and common-mode voltage (CMV). The BTSPWM can take advantages of the well-developed modulation strategies adopted in voltage source converters (VSCs) and translates into current source converter (CSC) system through logic translation. Generally, all-off gating signals can be generated through the logic translation, which should be replaced with the redundant zero-states to avoid open-circuit. However, most of the ZSR strategies are focused on switching time minimization while not considering the CMV excited by the replaced zero-states. To address the CMV issue, an optimized ZSR method for discontinuous BTSPWM was proposed to suppress the CMV while not influence the pulse width modulation (PWM) features. Not only the CMV maximum value but also the third-order component can be effectively reduced. Moreover, the proposed method can be easily applied to parallel CSC system due to the inherent modularity. The validity of proposed method is verified on both single and parallel CSC system by experiment results.
A "Predictive" Synchronous Rectifier Control for High-Frequency Series Resonant Converters

A predictive synchronous rectifier (SR) sensing and control method is proposed in this paper using a Rogowski coil with a passive integrator and an adaptive SR turn-off scheme in order to optimize series resonant (SRes) converter efficiency. Unlike other SR sensing methods used in SRes converters, the SR sensing method proposed here has high bandwidth, can be operated at high operational frequency and doesn’t have SR voltage level limitations. Therefore, this method is suitable for optimizing the efficiency of a high-frequency, high-power-density SRes converter. This paper discusses the operation principles and experimental results of the method on a 6.6-kW capacitor-inductor-inductor-inductor-capacitor (CLLLC) series resonant dual active bridge converter.
Carbon transfer between plants and its control in networks of arbuscular mycorrhizas

1. Two studies using the stable-isotope 13C have shown that large amounts of carbon can move between plants linked by arbuscular mycorrhizal fungi. Quantities comparable to the carbon cost of the symbiosis for an individual plant may be transferred. 2. We measured C transfer between linked plants of the grass Cynodon dactylon (C4, δ13C ≈– 14‰) and the herb Plantago lanceolata (C3, δ13C ~ – 28‰). To test the hypothesis that the carbon transferred between plants remained in fungal structures at all times, plants were grown for two harvests; at the first harvest they were clipped to ground level, so that shoot re-growth required the transport of carbon from the roots. We also tested the influence of the direction of growth of the fungus, to determine whether C was transported out of or into a newly colonized root, and of growing plants in elevated CO2, to increase the availability of carbon compounds in the roots. 3. Large amounts of C were transferred between linked plants, more so into Plantago than into Cynodon roots. Transfer occurred whether root systems were separated by a 20 μm mesh, that excluded roots but not hyphae, or a 0·45 μm mesh, intended to act as a barrier to hyphae as well. We believe that the high root densities achieved in the experiment allowed hyphae to cross the finer mesh between the two dense root mats. 4. Clipping the plants did not result in any movement of C from roots to shoots, thus confirming the prediction that all C transferred remains in fungal structures. 5. The direction of growth of the fungus did not affect the direction of transfer, nor did the CO2 concentration in which the plants were grown. 6. The amount of C transferred was a positive correlate of the frequency of vesicles in the roots but a negative correlate of the frequency of hyphae. If C were moving into developing colonization units, thus effectively giving the plant a ‘free’ symbiosis, the correlation with internal hyphae should be positive. The positive correlation with vesicles suggests that C is moving into fungal storage structures. 7. We propose a mycocentric view of the phenomenon of interplant C transfer, in which the fungal colonies within roots are seen as parts of an extended mycelium between which the fungus moves resources depending on the dynamics of its own growth. We do not believe that the transfer has an impact on plant C budgets or fitness, but that it may be a major element in the understanding of fungal C budgets.
Demo: A Network Slicing Solution for Flexible Resource Allocation in SDN-Based WLANs

WLANs need to support an increasing number of applications each characterised by different requirements in terms of bitrate, latency, and reliability. Network slicing is an emerging trend that enables such a vision by creating virtual networks, i.e., the slices, that are tailored to specific requirements of the services. In this paper, we demonstrate a programmable SDN-based solution that enables customisable end-to-end slice definition encompassing both the wired and the wireless segments of an Enterprise WLAN. We release the entire implementation under a permissive license for academic use.
A Soft-Switched Bridgeless AC/DC Converter for Electric Vehicles

This paper presents a new bridgeless AC/DC converter for electric vehicle (EV) applications. This converter acts as the power factor correction (PFC) stage with boost capability for EV charging systems. The proposed circuitry offers soft-switching of the power switches for a wide range of operating conditions as well as the ripple attenuation of the input current. The attenuated input ripple allows significantly reduced sizes for the differential mode EMI filter. The soft-switching and the ripple attenuation are performed through a ripple steering technique that is implemented by modifying the input filter of the AC/DC converter. Simulation and experimental results verify the feasibility of the proposed converter and demonstrate its superior performance.
Work in progress Engaging professional competencies through gamification

This article showcases a gamification implementation design to examine the contribution to professional competencies of college students who are close to complete internships. This paper analyses through an exploratory approach, the preliminary results of the implementation of a leaderboard that used avatars or nicknames (students’ election between both options) when students earn a badge at the same time they prepared for their full-time internship search.
Series-Capacitor Buck Converter with Soft Turn-On

The Series-Capacitor Buck (SCB) converter lowers the switching loss, doubles the duty ratio, and equalizes the current between two phases. A Resonant Series-Capacitor Buck (RSCB) converter is realized by adding a parallel resonant tank next to the series-capacitor Cs. All switches turn on into zerovoltage (ZVOn) and the low-side switches turn off from zerocurrent (ZCOff). The voltage gain is still linearly proportional to the off-time of the low-side switches. The resonant tank generates additional loss and increases the voltage stress of the low-side switches. A 2-MHz prototype with a full load efficiency of 97.3%, 48 V at the input, and 7 V, 20 A at the output, was built to verify the design.
Advanced Modulation Scheme with Loss Balancing Effect Under Low-Modulation Operation for FC-T2C Converter

Multilevel converters are widely applied in the field of high-power machine drives. The flying capacitor-T-type converter (FC-T2C), which is derived by combining the T-type converter and flying capacitor converter, is suitable for high current operation. Under the traditional phase-shifted PWM (PSPWM) scheme, uneven distribution of power device temperature occurs under low modulation coefficient, and it will affect the reliability and durability of the converter. In this paper, an advanced phase-deposition PWM (PDPWM) is proposed to realize loss balancing among power devices by introducing new redundant switching states. In addition, the flying capacitor and dc-link capacitor balancing control for the advanced PDPWM is presented and applied. The proposed scheme is verified by the simulation and experiment.
Distributed Coordination of Heterogeneous Multi-Agent Systems with Output Feedback Control

This paper investigates a heterogeneous multi-agent system consensus control problem under the leader-follower formation. The state dimensions of multi-agent systems (MASs) are diverse, which makes it difficult to analyze and design the controller for the multi-agent formation consistency problem. Therefore, an output feedback control scheme is designed for MAS consensus to ensure the output convergence for each follower. First, a discrete-time model of the leader-follower system is established. Then, an output feedback controller is provided with formation topology to ensure the formation consensus of the heterogeneous MAS. Finally, simulation results are shown to verify the designed control strategies.
Impedance Tuning Control and Synchronization Technique for Semi-Bridgeless Active Rectifier of IPT System in EV Applications

This paper proposes an impedance tuning control for a semi-bridgeless active rectifier (SBAR) of inductive power transfer (IPT) systems in electric vehicle (EV) applications. The proposed control can compensate for impedance mismatch which is caused by the change in parking position through adjusting turn-on point and duty ratio of SBAR. The operating principle is explained by the analysis of frequency characteristics and equivalent impedances. In addition, a rising edge detection technique of SBAR switch voltage is proposed to synchronize SBAR with the primary-side IPT system using minimal primary-side information, even if a variable frequency control is applied. Analysis results and validity of the proposed impedance tuning control are experimentally verified with a 3.3 kW laboratory prototype of the IPT system with SBAR.