Miracle-dz/UniFAR-FTUs huggingface dataset:
Evalution of ADC to Near Infrared Charging for Bio-sensing and Healthcare Applications

The proposed successive approximation register (SAR) analog-to-digital converter (ADC) for integrates healthcare sensor (HC). The healthcare sensor (HS) including heart rate monitor (HRM) and pulse oximeter (OX) is presented. Optimal DAC exchanging energy, a hybrid resistor capacitor DAC is promoted. The optimal design used charging near infrared laser driven (NIRLD) with thin Fresnel and OPVs light with VCSEL. The evaluation of accuracy and latency in line with chest-belt comparison of optical sensor between benchmarking. The optical sensor might be a promising OPVs light power supplier and low consumption, asynchronous control logic to fall in the ADC is used. The kickback noise can improve by comparator of pre-amplifier from dynamic latch design.
Operating Modes based Review of Single-Stage Buck-Boost Inverters

There is a need of buck-boost inverter (BBI) wherever the magnitude of source voltage is low as compared to the peak of ac output. Solar micro-inverter is most common application which requires BBI because of the low output PV voltage. Other major applications are uninterruptible power supply (UPS) and small wind turbine systems. Single stage BBIs are well known configurations which generally find demand in low power applications because of their claimed better efficiency profile as compared to two stage BBIs. In literature, there are many interesting BBIs reported which perform buck-boosting of input voltage by optimally merging multiple elementary circuits. This paper reviews single stage single phase BBIs based on the operating mode circuits involved for generating output voltage. Operation of BBIs, their efficiency profile and the ease in controller design are the main aspects that are discussed in this paper.
Absolute Indoor Positioning-aided Laser-based Particle Filter Localization with a Refinement Stage

Robot localization in indoor environments is crucial for achieving flexible automated navigation. In this paper, we propose a novel multi-stage localization approach for mobile robots which combines a commercial beacon-based absolute indoor positioning system with laser scan data. This configuration of sensors can be deployed in a non-intrusive way in most robotic platforms without the use of proprioceptive sensors (e.g. wheel encoders) which often introduce non-negligible maintenance and downtime costs. The data fusion is performed by a particle filter aided by a refinement stage. The proposed approach was evaluated in two different indoor scenarios with a mobile platform equipped with a mobile Marvelmind beacon and a Hokuyo UTM-30LX scanning laser rangefinder. The proposed localization framework, purposely without proprioceptive data, is compared with an AMCL approach having as inputs odometry, calculated from wheel encoders' data, and laser scan data. Preliminary results show that the proposed approach can provide an accurate localization estimate and that using the refinement stage improves localization.
Construction of Finite Field Based QC-LDPC Codes from Isomorphism Perspective

There are many algebraic tools, e.g., finite fields and combinatorial designs, for constructing good quasi-cyclic LDPC (QC-LDPC) codes. In particular, QC-LDPC codes designed based on two arbitrary subsets of a given finite field perform very well and some well-known algebraic constructions of QC-LDPC codes have been verified as special cases of this method. How to choose these two subsets to further improve the performance of finite field based QC-LDPC codes is of interest. In this paper, we study such codes and analyze their structural properties from isomorphism perspective, and generalize some rules to efficiently determine the non-isomorphic finite field based codes. By comparing the cycle distributions of non-isomorphic codes, we can easily construct the finite field based QC-LDPC codes with larger girth and fewer short cycles. These codes generally perform very well and can be used as the ingredients of some further processings, e.g., masking. Numerical results show that the constructed codes have better performance with the iterative decoding algorithms.
Multiphase PMSM and PMaSynRM Flux Map Model with Space Harmonics and Multiple Plane Cross Harmonic Saturation

Multiphase Synchronous Machines vary in rotor construction and winding distribution leading to non-sinusoidal inductances along the rotor periphery. Moreover, saturation and cross-saturation effects make the precise modeling a complex task. This paper proposes a general model of multi-phase magnet-excited synchronous machines considering multi-dimensional space modeling and revealing cross-harmonic saturation. The models can predict multiphase motor behavior in any transient state, including startup. They are based on flux maps obtained from static 2D Finite-Element (FE) analysis. FE validations have been performed to confirm authenticity of the dynamic models of multiphase PMaSynRMs. Very close to FE precision is guaranteed while computation time is incomparably lower.
A Novel Three Leg Inverter for High Power Hybrid Inductive and Capacitive Wireless Power Transfer System

A novel topology for hybrid wireless power transfer (HPT), which uses both capacitive and inductive action, for electric vehicle charging is proposed in this paper. A three leg inverter topology is used for HPT. Two legs of the inverter, switched at 85 kHz, are used for inductive wireless power transfer (IPT) and the third leg, switched at 1 MHz, is used for capacitive wireless power transfer (CPT). Mixed switching frequency operation of the converter, is the novelty in proposed topology. Series-series compensation is used for IPT and dual LC compensation is used in CPT. The effectiveness of the proposed topology is simulated for a power level of 1 kW. IPT contributes to twice the power transfer than that of CPT.
Autonomous navigation in unknown environments using robust SLAM

Autonomous navigation in unknown environment is a big challenge. In this paper, we combine the SLAM (simultaneous localization and mapping) with the path planning method. We first modify the classical SLAM with sliding mode technique, such that it is robust in the unknown environment. Then we analyze the algorithm using the “known space” and “free space” conditions, and propose the polar histogram path planning based on these conditions. We use Monte Carlo method to evaluate the performance of our algorithms. Simulation results show that our autonomous navigation algorithms are better than the others in unknown environment.
Discovering Urban Functional Regions with Call Detail Records and Points of Interest: A Case Study of Guangzhou City

Urban spatial structure is becoming increasingly complex with the development of cities and there are usually regions of different functions in a city. Understanding the distribution of these functional regions is significant for urban management and planning. The rapid growth of mobile phone users and telecommunication data motivates us to utilize a topic-modeling-based method using call detail records (CDRs) and points of interest (POIs) for urban functional region discovering. In this paper, we describe a data-driven framework based on latent topic model. Specially, we propose a method to obtain the human mobility patterns between different areas by extracting their trajectories from CDRs, which is important temporal-spatial information to discover the functions of a region. We evaluated our method using large-scale and real-world datasets including a POI dataset of Guangzhou and a CDR dataset provided by a certain telecom operator. The results verify that the framework is effective and the proposed method can be a new tool for computational urban science.
A New Single-Phase Single Source Nine Level Boost Inverter Topology

This paper proposed a new single-phase single stage single source nine-level boost inverter topology with self-voltage balancing along with the reduce switch count. The proposed topology is aimed to design for the renewable energy sources interfacing to the loads, which require higher voltage magnitudes. The proposed topology gives twice voltage booting feature without using any dc-dc converter. The topology proposed in this paper uses a single dc voltage source with two capacitors to generate a nine-level output voltage waveform. Nearest level control modulation technique has been used for the generation of gate pulse for the different switches used in the proposed structure. Different simulation results have been provided using PLECS software, and these simulation results have been verified using the experimental results obtained from a laboratory prototype.
All about face. An experiment in face emotion recognition in interactive dramatic performance

In this paper we present an experiment on the use of face expression recognition in interactive performance, conduced by using a platform for interactive drama that augments the performer-audience interaction with an intelligent prompt. Step after step, the emotional response of the audience is automatically detected through a face expression recognition service and the performer decides whether to encourage the audience's response or not. We describe the data collected during the performance, discussing the potential of the approach and the redesign issues emerged from the experience gained.
Hierarchical Topology Formation in Large-Scale IEEE 802.15.4 Wireless Sensor Networks

Cluster-tree topologies are commonly employed in large-scale Wireless Sensor Networks applications where nodes are randomly deployed. This topology presents several challenges related to connectivity and message schedulability. As a consequence, effective methods for network formation and operation are required. This work proposes a combination of methods of neighborhood discovery, network formation and message scheduling to deal with this problem. This proposal aims to build a flexible network, with better performance and is also able to adapt for different scenarios. Simulation results highlight significant improvements of the network performance, when compared to the use of LEACH protocol.
Performance Analysis for D2D-Enabled Cellular Networks With Mobile Edge Computing

In this paper, we propose an analysis framework for the D2D-enabled cellular networks with mobile edge computing, which is composed of two types of computing nodes, i.e., access point (AP) nodes and device-to-device nodes (DNs). The mobile users (MUs), whose locations are modeled as a Poisson Point Process (PPP) distribution, make an independent selection to offload their computation tasks to either the nearby AP or the D2D nodes according to the assumed task offloading modes. Besides, in order to alleviate the intra-tier interference both in uplink and downlink, a prioritized channel access policy (PCAP) is explored. By using the stochastic geometry and queuing theory, the performance of the proposed system model is evaluated in terms of the total successful edge computing probability, which is influenced by both the communication and computation performance of the two offloading modes. We obtain interesting tradeoffs that depend on the effects of D2D node densities and distance thresholds of the assumed offloading mode selection on the above performance metrics. When compared to the scheme without PCAP, the PCAP can contribute more to the total successful edge computing probability of a MU. The derived results could provide an useful guideline for the provisioning and planning in employing D2D-enabled cellular network with edge computing units.
Multi-Terminal Matrix Converters as Enablers for Compact Battery Management Systems

In this paper, a Multi-Terminal Matrix Converter (MT-MC) is proposed for a Battery Energy Storage System (BESS). The proposed MT-MC enables the connection of multiple batteries allowing their independent control. A nonlinear Predictive Optimal Controller is proposed to manage independently the charge/discharge process of the batteries and guarantee the connection to the grid with nearly unitary power factor. The validation of the proposed system is performed through simulation in MATLAB/Simulink. Simulation results show the high performance of MT-MC for two batteries at different operation scenarios allowing charging of one battery while the other is discharging, still guaranteeing current THD lower than 5% in the connection to the grid.
Four quadratic DC/DC converters with one low-side switch and a tapped inductor at the input side

Four new quadratic step-up-down converters with one low side switch and coupled inductors are presented. One is treated in detail, for the other converters results are shown. The voltage transformation ratio and the relationship of the inductor currents in dependence of the load current are calculated. An idealized mathematical description of the converter is derived and the large signal and the small signal model are determined. Furthermore the transfer functions between the output voltage and the duty cycle and the input voltage are calculated. Dimensioning hints are given. The most important voltage and current shapes are shown.
An approach to reconstruction of data set via supervised and unsupervised learning

Stock markets are nowadays producing vast portions of data. In order to keep up with the pace also the technology stack of research institutions needs to adapt. However, the portion of data is not the only reason. With the boom in the area of Artificial Intelligence one can seize the advantage of insights hidden in the data. In order to develop trading strategies, describe the behavior present in the market, one can grasp the concepts of supervised and unsupervised learning. In this paper we describe the basic idea behind these types of learning and propose a framework which ingests the limit order book data and prepares them onto a form where the learning is easily applicable.
Dielectric Recovery of Ultrafast-Commutating Switches used for HVDC and Fault Current Limiting Applications

This paper is devoted to numerical simulation of arcs in ultra-fast switches with an electrodynamic (Thomson) drive.Ultra-fast switches with Thomson drive mechanisms are used to generate the high arc voltage necessary for current commutation in hybrid HVDC circuit breakers (HVDC CB) / fault current limiters (FCL). To make such a hybrid circuit breaker / fault current limiter a practical solution, the dielectric recovery of the commutating switch is to be sufficiently fast to avoid the re-ignition of the switching gap as a consequence of the voltage build up during the current interruption and limitation process.In this study, the dielectric recovery characteristic of a prototype switch with air as insulation medium has been investigated. A finite element model with the use of moving mesh is created. Different arcs at three different contact speeds (5, 7 and 9 m/s) and different arc currents (200 A and 300 A) are simulated, then different voltage stresses with Rates of Rise of Recovery Voltage (RRRV) from 70 to 600 V/μs have been applied 50 μs after the simulated arc was extinguished to obtain the dielectric recovery curves. The Volume Flow Rate (VFR) in the switch, which is directly related to the contact speed, plays a crucial role in insulation recovery in the post-arc phase. The impact of contact opening speed and the transient recovery voltage parameters on the dielectric recovery of the prototype ultra-fast switch has been thoroughly discussed.
Optimized battery sizing for merchant solar PV capacity firming in different electricity markets

This work analyses the minimum energy capacity requirements to be demanded to battery energy storage systems used in megawatt-range merchant solar PV plants to grant capacity firming. The operation of such a plant is simulated (with a 2-minute time step, at three different locations of the Iberian Peninsula, and for different battery sizes) after solving a quadratic programming optimization problem. The control algorithm takes into account the irradiance forecast and the intraday electricity market configuration, which presents certain peculiarities in the Iberian region with regard to other European markets. The analysis has been performed in an annual basis and current irradiance measured values have been used.
A Study of Climate Change Resilience, Conservation Easements and the Protection of Biodiversity in the Eastern U.S., Great Lakes and Northern Great Plain Regions

Climate change presents a threat to the stability of species habitat and biodiversity. In the United States, public and private organizations have effectively used conservation easement (CEs) as a tool for preserving wildlife habitats and species. Despite their considerable success in providing a public good through environmental protection, conservation organizations now face uncertainty over future costs, CE legal standing and maintaining ecosystems undergoing compositional and functional change due to climate change. Researchers have discovered strong correlations between specific natural landscape attributes and greater levels of species diversity. These discoveries create an opportunity for CEs to increase their capacity for biodiversity protection and reduce organizational risk. This project devised a series of analyses to explore the use of natural landscape attributes resilient to climate change within current CEs and consider their impact on future CE planning. Three research questions and related hypotheses focused the investigation: 1) Do commitments to protecting biodiversity result in higher landscape diversity scores? 2) Do higher scores in climate change resilience correlate with larger land parcels? 3) Do higher landscape diversity scores correlate with greater connectedness to adjacent parcels? T-tests of hypothesis 1 revealed no statistically significant difference in mean landscape diversity scores between CEs explicitly managed for biodiversity and those with no declared biodiversity protection objectives. Results are compatible with a possible knowledge adoption gap by conservation organizations regarding the benefits of climate change resilience. Regression analysis of hypothesis 2 did not find any evidence that easement size was correlated with the measurements of climate change resilience. The findings imply that investments in larger parcels may not be the most cost effective way to increase scores in easement resilience. However, regression analysis of hypothesis 3 demonstrated a high level of correlation between landscape diversity and local connectedness to surrounding parcels. The outcome supports the possibility of gaining multiple conservation values by investing in just one element of climate change resiliency. The evidence suggests that CE designers, interested in the preservation of biodiversity, are not leveraging the highly relevant benefits that landscape diversity can contribute to their declared conservation objectives. This finding points to a valuable opportunity for advancing the capacity of land conservation to preserve biodiversity by integrating climate change resiliency. Furthermore, the strong correlations between metrics of climate change resilience suggest that by incorporating the methods outlined in this thesis into future CE design, organizations can realize the triple bottom line value of biodiversity protection, lower land stewardship costs and community conservation benefits.
Towards Fully Reactive Multi-step Generation for Humanoids against Instantaneous Push: A Case of Walking in Place in Sagittal Plane

In this paper, we address the problem of generating a trajectory of the zero-moment point (ZMP) and the rate of angular momentum for a bipedal robot in the sagittal plane, with which the balance of the robot is recovered from external push. Unlike most previous works that adjusted a pre-designed ZMP or solved (possibly too heavy) nonlinear optimization problems, our main purpose is to develop a fully reactive step generator in the sense that (a) no pre-calculation of nominal trajectory is required, and (b) the algorithm is simple enough to operate in real time, only by utilizing the current state of the robot. For the design, it is seen by reinterpreting the centroidal dynamics in the hybrid model framework that the balance recovery problem can be recast as the problem of stabilizing a hybrid-type (linear) inverted pendulum model. On the basis of the concept of the divergent component of motion, a simple hybrid control law is then constructed to stabilize the hybrid system, which serves as a step generator that automatically determines where and when to step. This paper briefly sketches a mathematical proof on the performance of the proposed generator from a control-theoretic perspective, which is also supported by simulation results.
A Graph-Neural-Network Decoder with MLP-based Processing Cells for Polar Codes

Compared with traditional decoders, the Neural Network Decoder (NND)has the advantages of being low-latency, high-throughput and one-shot. However, the conventional deep neural network decoder (DNND)usually suffers the computational mismatch and the lack of generalization capability. In this paper, we propose a novel Graph-Neural-Network Decoder for the popular Polar Codes, which is directly constructed based on the regular decoding graph of Polar Codes and by replacing each of its basic 2-by-2 polarization elements with a simple Multi-Layer-Perceptron (MLP)based processing cell. The resultant decoder, namely PC-GNND, is thus endowed with the ability to infer over the skeleton of the decoding graph and has a greatly improved generalization capability. Simulation results show that, the proposed PC-GNND is capable of learning the exact code structure as well as the channel noise very efficiently with only a tiny fraction of the entire codebook and achieving better performance than that of conventional NNDs with far less parameters and training epochs. Moreover, the PC-GNND trained in a particular block length can be scaled to another one for different block lengths after fine tuning, which significantly reduces the computational cost of training.
Implementation for UAVs Aided Edge Sensing System in Wireless Emergency Communications

Disaster environmental data sensing plays an important role in ensuring the timeliness of emergency rescue. Combining mobile edge caching and mobile edge computing (MEC)function, this paper proposes an unmanned aerial vehicles (UAVs)aided edge sensing system (UESS)in wireless emergency communications, where UAVs with flexibility and mobility work as edge servers for flexible data sensing. This system is developed and implemented involving the UESS server in the cloud, the Message Queuing Telemetry Transport (MQTT)server, and the UAV servers at the edge. Besides, this system implements rapid network deployment of UAVs with three protocols of IEEE 802.11. In the case of natural disasters even when the communication infrastructures are damaged in whole or in part, the proposed UESS can be rapidly deployed to provide network access and expand the coverage of the network. Moreover, this system can also accurately sense disaster scenarios' information data, such as video data and sensor data, as well as the capability data of UAVs in terms of computation, caching, and energy resources. The experimental results show that the UESS system not only can reduce the size of transmission data uploaded to the UESS server, but also can achieve the low transmission latency between UAVs at the edge.