Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating V-SLAM and LiDAR-based SLAM for Map Updating

Vehicle positioning generally uses the global navigation satellite system (GNSS), but systems of different levels significantly affect positioning accuracy. Moreover, it is greatly affected by weather that may cause inaccurate positioning due to excessive cloud cover. In the technology of self-driving cars, how to achieve stable and accurate positioning is a critical topic. This research first uses RTK-GPS, LiDAR, and cameras to build high-precision map information and realize vehicle positioning functions. By using the normal distribution transform (NDT), ORB-SLAM, and iterative closest point (ICP) were matched to the feature points of the current map with the pre-built map to complete the vehicle positioning system. If the change of static objects in the map was detected during the positioning process, the map was updated. Finally, the performance of the proposed system was verified on the road on the campus.
A General Control Architecture for Visual Servoing and Physical Interaction Tasks for Fully-actuated Aerial Vehicles

In this paper, we present a general control architecture that allows fully-actuated aerial robots to autonomously accomplish tasks that require both perception and physical interaction with the external environment. We integrate the novel Flying End-Effector paradigm and a Hybrid Visual Ser-voing (HVS) scheme to design a general control architecture for fully-actuated aerial robots. Thanks to the proposed solution, a fully-actuated aerial robot can autonomously accomplish tasks that require both perception and physical interaction without resorting to any external force/torque sensor. The control architecture is entirely described, features a wrench observer and an admittance filter, and is subsequently validated on real experiments. The code for the proposed control architecture is provided open-source.
An Experimental Study of LoRa Transmission Parameter Selection in Various Circumstances

As a family member of the Low Power Wide Area Networks (LPWAN), LoRa provides resistance to interference and multipath fading by the Chirp Spread Spectrum (CSS) technique. It improves its performance by adjusting transmission parameters to adapt to different circumstances. This paper presents LoRa transmission parameter evaluation under different circumstances, with and without building obstacle blocking or distorting the wireless signals. The parameters include Spreading Factors (SF), Bandwidth (BW), and Coding Rate (CR). We tested different parameter combinations. Yield tests of 72 different combinations on transmission parameters are carried out to evaluate the performance under each circumstance.
Electromechanical Co-Design and Experimental Testing of Package Layer in Structurally Embedded Phased Array Antenna

Structurally embedded phased array antenna (SEPAA) provides a new paradigm where the structural surface of a mobile vehicle acts as an antenna. This letter presents an optimal design method for a new SEP AA which consists of a package layer, a RF layer and a power-signal process layer. Electromechanical co-design model of the package layer was formulated to obtain an optimal mechanical and electromagnetic performance of the SEP AA. Moreover, in order to efficiently solve the model, this paper also proposes a parallel Bayesian optimization (PBO), which can utilize the parallel computing of EM simulations to alleviate the computational cost. The comparison results show that the PBO achieves better optimization results with significantly less iteration numbers, and the measured results of the fabricated Ka-band prototype demonstrate that the designed SEP AA has good mechanical load-bearing and radiation performance.
Dimension Reduction of Polynomial Regression Models for the Estimation of Granger Causality in High-Dimensional Time Series

The causality analysis of multivariate time series and formation of complex networks relies on the estimation of the direct cause-effect from one observed variable to another accounting for the presence of other observed variables. Such effects are quantified by the conditional Granger causality index (CGCI), derived by linear vector autoregressive models (VAR). Dimension reduction approaches have been developed to restrict VAR models, such as the modified backward-in-time selection method (mBTS) giving the restricted CGCI (rCGCI). The rCGCI is limited to linear systems. In this study, we extend the mBTS dimension reduction scheme to polynomial VAR, so as to model nonlinear dynamics and cause-effect relationships and derive the Granger causality measure termed restricted polynomial CGCI (rpCGCI). The complications in the adaptation of mBTS to the restricted polynomial VAR and the construction of the Granger causality index are addressed, involving also a randomization significance test in the steps of mBTS. The rpCGCI has the advantage against other nonlinear Granger causality measures that it can be applied to short time series of high dimension (many observed variables). The simulation study on different types of multivariate stochastic processes and different lengths of generated time series showed the superiority of the proposed rpCGCI as compared to CGCI, rCGCI, pCGCI (using polynomial VAR) and other nonlinear causality measures. Further, rpCGCI was compared favorably to the other measures on signals of heart rate variability, respiration, and oxygen concentration in the blood, as well as multi-channel scalp electroencephalogram (EEG) recordings of epileptic patients containing epileptiform discharges.
On the Evolution of Speech Representations for Affective Computing: A brief history and critical overview

Recent advances in the field of machine learning have shown great potential for the automatic recognition of apparent human emotions. In the era of Internet of Things and big-data processing, where voice-based systems are well established, opportunities to leverage cutting-edge technologies to develop personalized and human-centered services are genuinely real, with a growing demand in many areas such as education, health, well-being, and entertainment. Automatic emotion recognition from speech, which is a key element for developing personalized and human-centered services, has reached a degree of maturity that makes it of broad commercial interest today. However, there are still major limiting factors that prevent a broad applicability of emotion recognition technology. For example, one open challenge is the poor generalization capabilities of currently used feature extraction techniques to interpret expressions of affect across different persons, contexts, cultures, and languages.
200-GS/s ADC Front-End Employing 25% Duty Cycle Quadrature Clock Generator

A 55nm SiGe BiCMOS ADC front-end is reported with record 200-GS/s sampling rate and SNDR larger than 32 dB and 25.3 dB up to 45 GHz and 63 GHz, respectively. This performance is enabled by the architecture of the front-end with a single level of samplers which maximizes bandwidth and linearity, by the reduced-voltage MOS CML switch, and by a dc-to-62 GHz, 25% duty-cycle non-overlapping quadrature clock generator. The total power consumption of the ADC front-end is 635 mW.
On The Building Map for Radio Propagation Prediction Using Machine Learning

We discuss how to extract building map images to be used in a learning-based method for predicting radio wave propagation. Learning-based prediction methods for radio wave propagation use building map images as spatial data, and we have already proposed a prediction method that uses images around Tx, Rx, and their midpoint. The method works well but has missing regions between Tx and Rx when the distance between them increases. In this paper, we further modify the method to include the whole area between Tx and Rx and also their surrounding regions in a single image. We present the method of using the dataset generated from measured data and an evaluation of how much the proposed method improves the prediction accuracy of radio wave propagation.
Effect of Temperature Cycling, High Temperature Storage and Steady-State Operation Life Test on Reliability of GaN HEMTs

With the advantages of high frequency and high power performance capabilities, AlGaN/GaN high-electron-mobility transistors (HEMTs) have attracted more and more attention. However, the reliability under stress conditions limits the application of GaN HEMTs in microwave power markets. In this paper, the effect of temperature and voltage on GaN HEMTs reliability were studied. A temperature cycling test with the temperature range from -55°C to 175°C, a high temperature storage test of 175°C and a steady-state operation life test with the channel temperature of 175°C have been conducted on GaN HEMTs. The variation of key parameters such as drain current (IDS), threshold voltage (VGS(th)), gate-to-source leakage current (IGss) have been analyzed before and after the test.
Chipless RFID Temperature and Humidity Sensing

In this paper, a temperature and humidity chipless RFID sensor is presented. A conventional chipless RFID interrogation process is used to extract the tag identification as well as the two physical parameters. A model describing the evolution of the resonance frequency of a scatterer as a function of temperature and humidity is introduced. It allows the two quantities to be extracted independently of each other while each of them impacts the resonance frequency. On average, an error lower than 0.7 °C is obtained on the temperature and less than 1.3% on the humidity.
A 272-GHz CMOS Analog BPSK/QPSK Demodulator for IEEE 802.15.3d

A 272-GHz CMOS receiver (RX) for IEEE 802.15. 3d, capable of an analog BPSK/QPSK demodulation based on a Costas loop, is presented. A QPSK demodulation is realized with the non-linear operation of a phase detector at the BPSK-demodulation Costas loop. An 8-Gbaud BPSK and a 4-Gbaud QPSK demodulation at the carrier frequency of 272.16 GHz (channel 58 of IEEE 802.15.3d) are achieved.
A 24–30 GHz Low-Loss Compact Differential Four-Way Power Divider

A differential four-way power divider in 65 nm CMOS process for a beamforming front-end IC is presented, which provides stable virtual grounds regardless of grounding inductance variations. The chip size is reduced by using a 4-way Wilkinson power divider based on lumped elements. It has a small core chip size of 0.09 mm’. At 24 to 30 GHz, it has a low insertion loss of only 0.93 to 1.94 dB, a port-to-port isolation of 19.1 to 25.6 dB from 24 to 30 GHz. It has small amplitude and phase difference which are much better than a single-ended structure.
Impact of Network Densification on the Performance of a Non-Public URLLC Factory Network

Densification of the network deployment, for example by adding new sectors or sites within an existing mobile communication network, has traditionally been an efficient way to improve the system coverage and capacity. That will be the case even for the ultra-reliable low-latency communication (URLLC) services, but the overall situation and the feasibility of the different deployment options will depend on the characteristics of the URLLC service in question. URLLC services with relaxed latency requirements, allowing multiple transmission attempts, can tolerate a decent level of inter-cell interference while still being able to guarantee the desired quality-of-service for all users, which makes it possible to improve the URLLC service capacity by adding new gNodeBs with omnidirectional or directional antennas. However, when the URLLC services require extremely low latency that do not allow for retransmissions, the system performance becomes quite sensitive to the inter-cell interference, which means that in practice the gNodeBs should be equipped with beamformed antennas to reach high levels of URLLC service capacity. Finally, an active distributed antenna system could be an efficient way to secure good coverage throughout the desired service area.
Stability Analysis of a DC-link with Nonlinear Load – An Overview

This paper studies the stability of a dc-link when supplying a nonlinear load through a power converter. The dc-link often comprises an LC filter, which is subject to oscillation. The nonlinear behavior of the load reduces the damping of the system and may result in an unstable operation and system failure. This paper reviews the major modeling and active stabilization methods. It shows the shortcomings of the common assumption of constant-power load, namely, it fails to predict the practical stability limits and leads to an over-conservative design. A more realistic and systematic approach, based on the small-signal model of the system, is presented and analyzed. A comprehensive review of active stabilization methods is presented. Numerical simulation is used to validate the theoretical concepts.
Analysis and Design of a Single-Phase Half-Bridge Rectifier/Inverter with an Active Resonant DC Bus Voltage Balancer

This paper presents the analysis and design of the single-phase half bridge rectifier with an active voltage DC bus balancer (ARVB). Impact of the power ripple on the DC bus of the ordinary rectifier/inverter and the rectifier/inverter with the ARVB is examined. It will be shown that with the ARVB, the volume and size of the DC bus; hence the converter can be significantly reduced, since the ARVB eliminates fundamental and the most significant component of the DC bus current.
MARSA: Multi-domain Arabic Resources for Sentiment Analysis

The Arabic language has many spoken dialects. However, until recently, it was primarily written in Modern Standard Arabic (MSA), which is considered to be the formal variant of Arabic. Social media platforms have changed the face of written Arabic because their users converse freely in various dialects, thus offering a massive number of resources for the study of dialectal text. The Arabic dialects differ from MSA in morphology, syntax, and phonetics. Consequently, since the effectiveness of NLP tasks—like sentiment analysis—is dependent on the availability of representative resources, there is currently a great need for such resources in these dialects. In this paper, we present MARSA—the largest sentiment annotated corpus for Dialectal Arabic (DA) in the Gulf region, which consists of 61,353 manually labeled tweets that contain a total of 840 K tokens. The tweets were collected from trending hashtags in four domains: political, social, sports, and technology to create a multi-domain corpus. The importance of such a corpus is to facilitate the study of domain-dependent sentiment analysis in Arabic. In addition to this corpus, the annotators extracted indicator words to form affect lexicons for each domain. We draw insights from these lexicons regarding contextual polarity of certain words. Furthermore, we present benchmark experiments on the MARSA corpus in order to establish a baseline for further studies.
Investigation of the RDL reliability based on RF characterization

In this letter, the RF reliability of the redistribute layer (RDL) under different environment stresses was investigated and the degradation mechanism of the RDL was analyzed. The inter-connect performance was characterized by the S parameters from 10MHz to 40GHz. The test vehicles of the RDL with coplanar waveguide (CPW) and microstrip (MS) were fabricated on Si and epoxy resin substrates. It was found that the insertion loss of CPW on Si substrate degraded seriously after reliability tests, especially at the low frequency range of 10MHz-30GHz. For other samples, the humidity stress has more impacts on the RDL reliability compared with thermal stress. Warpage was found for the RDL on the epoxy substrate, while the warpage of the RDL on the Si substrate was under detection limits. The 3D-Xray and SEM of the cross section of the samples were characterized. No declamation was found in the RDL after reliability tests. The surface of the Cu contact pad were oxidized and an obvious declamation was found in the contact region, which results in the insertion loss changing.
Effect of daisy chain structure on electromigration reliability of microbumps: A simulation study

A simulation method was utilized to study the electromigration reliability of two daisy chain structures which were subjected to electric current of 0.05 A at environment temperature of 22°C. The current density and temperature distribution of the two daisy chain structures were compared and the electromigration reliability of two types of daisy chain structures were analyzed. The results show that the current crowding effect of typical bump in parallel daisy chain structure was more prominent than that of typical bump in series daisy chain structure. The current density followed eight-order polynomial distribution. The hot spots of both structures coincided with the current crowding regions and the maximum temperature in typical bump in parallel daisy chain structure was obviously higher than that in typical bump in series daisy chain structure. The electromigration reliability of parallel chain structure was lower than that of series chain structure due to higher temperature and higher current density in hot spots of parallel chain structure. These findings will offer fundamental insights into electromigration and reliability theory.
Application of decision tables transformations for prototyping knowledge bases in the case of forest fire risk forecasting (short paper)

In this paper, we consider the application of the PESoT technology and a tool (namely, Personal Knowledge Base Designer) for prototyping rule-based knowledge bases by using the automated analysis and transformation of decision tables presented in the CSV format. Created prototypes of knowledge bases designed for intelligent decision-making support when analyzing and forecasting the risk (probability) of forest fires based on information about the forest fire hazard class, weather conditions, and other factors. A description of the main stages of the approach and an illustrative example are presented.
Dynamic Action Selection Using Image Schema-Based Reasoning for Robots

Dealing with robotic actions in uncertain environments has been demonstrated to be hard. Many classic planning approaches to robotic action make the closed world assumption, rendering them inefficient for everyday household activities, as they function without generalizability to other contexts or the ability to deal with unexpected changes. In contrast, humans robustly execute underspecified instructions in unfamiliar environments. In this paper, we initiate our research program where we propose the use of functional relations in the form of image-schematic micro-theories, formally represented in ISL 𝐹𝑂𝐿 , to enrich action descriptors with semantic components. It builds on the body of work in embodied cognition showing that human conceptualization of action sequences is founded on abstract patterns learned from physical experiences in the form of spatiotemporal relationships between object, agents and environments. These theories are used to inform action selection mechanisms for behavioral robotics written in EL++ and we argue how these micro-patterns can be applied in a more general way to deal with underspecified action commands and commonsense problem-solving.
CC4Spark: Distributing event logs and big complex conformance checking problems

Conformance checking is one of the disciplines that best exposes the power of process mining, since it allows detecting anomalies and deviations in business processes, helping to assess and improve the quality of these. This is an indispensable task, especially in Big Data environments where large amounts of data are generated, and where the complexity of the processes is increasing. CC4Spark enables companies to face this challenging scenario in twofold. First, it supports distributing conformance checking alignment problems by means of a Big Data infrastructure based on Apache Spark, allowing users to import, transform and prepare event logs stored in distributed data sources, and solve them in a distributed environment. Secondly, this tool supports decomposed Petri nets. This helps to noticeably reduce the complexity of the models. Both characteristics help companies in facing increasingly frequent scenarios with large amounts of logs with highly complex business processes. CC4Spark is not tied to any particular conformance checking algorithm, so that users can employ customised algorithms.
A Multipreference Semantics from Common Sense Reasoning to Neural Network Models: An Overview

In this short paper we report about a “concept-wise" multipreference semantics for weighted condition-als and its use to provide a logical interpretation to some neural network models, Self-Organising Maps (SOMs) and Multilayer Perceptrons (MLPs). For MLPs, a deep network can be regarded as a conditional knowledge base, in which the synaptic connections correspond to weighted conditionals.
Concept Combination in Weighted Logic

We present an algorithm for concept combination inspired and informed by the research in cognitive and experimental psychology. Dealing with concept combination requires, from a symbolic AI perspective, to cope with competitive needs: the need for compositionality and the need to account for typicality effects. Building on our previous work on weighted logic, the proposed algorithm can be seen as a step towards the management of both these needs. More precisely, following a proposal of Hampton [1], it combines two weighted Description Logic formulas, each defining a concept, using the following general strategy. First it selects all the features needed for the combination, based on the logical distinction between necessary and impossible features. Second, it determines the threshold and assigns new weights to the features of the combined concept trying to preserve the relevance and the necessity of the features. We illustrate how the algorithm works exploiting some paradigmatic examples discussed in the cognitive literature.
A Concept of Self-Supervised Logical Rule Inference in Symbolic Classifications

An approach to modelling self-supervised learning for automated inferring good classification tests is proposed. The concepts of internal and exter-nal learning contexts are formulated. A model of intelligent agent, capable of improving own learning process of inferring good classification tests in the ex-ternal context is advanced. Internal evaluation is used in an internal process of learning with the aim of tuning the external learning process. The same learning algorithm is used for supervised learning both in the external context and in the internal context. The structure of good test inferring is described and a procedure to recognize the end of inferring process is proposed.
Onto-IKEA: A Knowledge Retrieval Framework Based on IKEA Ontology

The evolution of Linked Open Data (LOD) has encouraged developers to create more and more context related ontologies. This advance is extremely important because Artificial Intelligence (AI) applications can access domain specific information that is in machine understandable format. In this paper, we present a knowledge retrieval framework which is based on our custom made IKEA ontology. The ontology is created with information from the IKEA dataset that we also created. The dataset contains information about household objects found in the IKEA website, relations between objects, and features such as dimensions, and material for the objects. Moreover, we provide a query interface that the user can use to address queries to the ontology.