Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Constrained Voronoi-Based Task Allocator for Smart-Warehouses

Multi-robot systems consist of a set of robots working together to achieve a common goal. In these systems, two types of problems are widely addressed: the multi-robot path planning (MPP) and the multi-robot task allocation (MRTA). While the first one consists of finding the best path between two points in the space, the second one consists of allocating tasks to the robots, meeting restrictions, and completing one or more general objectives. The objectives are usually related to time optimization and energy consumption. The constraints require attention because they impact the complexity of the problem and reduce the system’s performance. Smart warehouses are an important example of application in which these problems are relevant. In such applications, the picking and shipping products control happens in an automated way, and mobile robots completely operate them. The literature shows that few studies explore integrated MPP and MRTA strategies to solve task allocation restrictions in smart warehouses. The main contribution of this paper is to present an integrated MRTA and MPP approach for smart warehouses by using static, seasonal, and dynamic information. Static information is provided by fixed obstacles, battery level, and load capacity of the robots. Seasonal information comes from products’ location and availability of them in a given period. The dynamic information corresponds to battery consumption and dynamic obstacles. In this paper a Multi-Constraints Voronoi-based Task Allocator (MCVB-TA) is presented. Its implementation incorporates a variation of the Voronoi diagram to allocate robots to the nearest tasks according to constraints, robots, and the environment. The simulation results obtained show that the proposed solution considerably reduces the time and energy cost of executing tasks in a smart warehouse scenario compared to a regular scheduler.
A Full- and No-Reference Metrics Accuracy Analysis for Volumetric Media Streaming

Volumetric media streaming will be one of the fundamental technologies to enable near future immersive multimedia experiences. In it, objects represented as sets of points (i.e. point-clouds), are presented to remote users wearing Head-Mounted Displays (HMDs). Due to the stringent bandwidth and latency requirements of such applications, small changes in the network can affect the user in unexpected manners (physical discomfort, lack of concentration, etc.). Therefore, there is a need for assessing the perceived quality of this type of applications in real-time, i.e, the Quality of Experience (QoE). Given that subjective evaluations are not feasible for (near) real-time applications, objectively measuring this quality will be a must. While traditional objective metrics could potentially be used to fulfill the task, it is still unclear how accurate they are to assess volumetric media. To this end, this paper presents a thorough correlation analysis of both Full Reference (FR) and No Reference (NR) objective metrics to subjective Mean Opinion Scores (MOS) for different volumetric streaming scenarios. To enhance the accuracy, multiple Region-Of-Interest (ROI) selection and weighting procedures have been applied and their influence on the results have been investigated. Our results show that the classical video quality metric Video Multimethod Assessment Fusion (VMAF) is well-suited as an objective benchmark for volumetric media streaming in terms of correlation to subjective scores, while a combination of NR features could provide a suitable real-time assessment. Finally, ROI selection proves to widen the range of objective metrics, which is an important issue to apply traditional objective metrics to volumetric media.
Real-time Wave Monitoring on Coastal Areas Using LPWAN-Based Embedded Systems

A new embedded system is presented for real-time wave monitoring on coastal areas using SigFox communication. SigFox is a Low-Power Wide-Area Network technology that has been rarely used in coastal marine monitoring. The system is based on the low-power TD1205P module that includes a microcontroller, an accelerometer, a GNSS receiver and a SigFox transceiver. Each hour, the module estimates the wave's maximum height ($H$max) and mean period (Tz), determines the GPS position, and wirelessly transmits the data through the SigFox network. The procedure for wave parameter estimation is based on the zero-upcrossing method using the vertical acceleration data. It was experimentally validated by attaching the embedded system to a moored buoy and comparing $H$max and Tzwith that provided by a seafloor acoustic wave and current profiler, used as a reference. Results over a period of two months show a good match for $H$max but less for Tz, which crosscorrelation values at zero lag of about 0.85 and 0.5, respectively. Power tests of the embedded system were also performed resulting in a lifetime estimation of 420 days with a battery pack of 3 Ah.
Switching Transition Control to Improve Efficiency of a DC/DC Power Electronic System

Increasing the switching frequency of the power semiconductor devices (PSDs) reduces the size and cost of the passive elements, thereby positively affecting the power density of a high-frequency (HF) power electronic system (PES). To achieve a higher switching frequency of a PES, yet low switching losses, the speed of switching transitions of PSDs need to be increased. However, such fast transitions adversely affect PES performance in terms of electromagnetic interference (EMI) and device stress. Hence, a switching transition control (STC) scheme is developed to create optimality between switching PSDs with higher transitions yet maintaining safe levels of parasitic oscillations, that result from reducing the transition time of these devices. The switching transition control (STC) framework helps the HF PES achieve a target efficiency improvement by controlling the high di/dt and dv/dt regions of a PSD on the fly. Results are shown to validate that this improvement of efficiency is not feasible with a passive gate drive. An HF Ćuk PES using a Cree SiC MOSFET half-bridge module is fabricated for the testing purpose of the STC framework. The STC network is based on a simple switched resistor network, synthesized using high-speed GaN-FETs and built across two generations, Gen-1, and Gen-2. Practical operational issues of the STC network with fast switching GaN-FET in the Gen-1 board are analyzed and are overcome with design modification in Gen-2. The work has ramifications in meeting a system-level goal of an HF WBG PES, like a target efficiency increment, while not deteriorating the EMI performance and PSD stress levels that result due to parasitic oscillations in such PES.
Improving the accuracy of restoring a distorted image via determining the distortion parameters from the Fourier spectrum

The spectral method is developed for estimating the parameters of the point-spread function (PSF) in the problem of restorating the distorted (smeared, defocused) images. The method is based on the analysis of the Fourier spectrum of a distorted image. This method makes it possible to estimate the PSF parameters: the angle θ and magnitude Δ of image smearing, as well as the size r of image defocusing spot. New estimates are obtained for parameters θ and Δ using the Nyquist frequency and a estimate for r using the Bessel function. The results of applying this method to image processing are presented. This method can be used to enhance the accuracy of smeared and defocused image restoration via their mathematical processing by stable methods for solving the Fredholm integral equations of the first kind (ill-posed problem).
Mathematical Formula Representation via Tree Embeddings

We propose a new framework for learning mathematical formula representations using tree embeddings. By representing each symbolic formula (such as math equation) as an operator tree , we can explicitly capture its inherent structural and semantic properties. Our framework consists of a tree encoder that encodes the formula’s operator tree into a vector and a tree decoder that generates a formula from a vector in operator tree format. To improve the quality of formula tree generation, we develop a novel tree beam search algorithm that is of independent scientiﬁc interest. We validate our framework on a formula reconstruction task and a similar formula retrieval task on a new real-world dataset of over 770k formulae collected online. Our experimental results show that our framework significantly outperforms various baselines.
Parametric Variational Sum-Product Algorithm for Cooperative Localization in Wireless Sensor Networks

Communication overhead is a key task for the large-scale wireless sensor networks (WSNs) in the cooperative localization problems. This paper is aimed to propose a low-overhead algorithm without sacrificing the performance of localization, which is more practical for energy-saving WSNs. In this paper, the cooperative localization problem is formulated as a variational reference problem on a factor graph (FG). Combining the high performance of particle-based algorithms and the overhead advantage of parametric ones, a parametric variational sum-product algorithm (PVSPA) is proposed for the cooperative localization of WSNs. In prediction operation, to ensure high accuracy, messages referenced inside sensor nodes are particle-based. In correction operation, Gaussian parametric representation is adopted to the observation message and the communication overhead is obviously decreased. Simulation results show that the localization accuracy of PVSPA equally matches the classic particle-based algorithms, while the communication overhead of it is far lower.
Compact SIW Fed Dual-Port Single Element Annular Slot MIMO Antenna for 5G mmWave Applications

In this article, a compact dual-port single element MIMO antenna is presented for millimeter wave (mmWave) 5G wireless applications. The presented antenna design consists of an annular ring slot that is fed by a Substrate Integrated Waveguide (SIW) at the two opposite sides of the substrate. The antenna resonates at 28GHz and its 10dB impedance bandwidth is 400MHz. The isolation between the ports is reduced by introducing additional annular slot that acts as a cavity which results in an isolation better than 20dB at 28GHz. The measured gain of both ports are 6.9 dBi which is the highest gain per unit element at 28GHz in the literature. The measured diversity gain and envelope correlation coefficient are 9.98dB and 0.065, respectively, at the band of interest. The measured and simulated results are in good agreement.
Rheology and crystallization of long-chain branched poly(l-lactide)s with controlled branch length

A series of long-chain branched poly(l-lactide)s (LCB-PLAs) with controlled branch length were prepared by a simple and efficient method through a combination of ring-opening polymerization (ROP) of l-lactide and a coupling reaction between the terminal OH groups of the PLA prepolymers and the NCO groups of HDI. The influences of reaction conditions on the synthesis of the LCB-PLAs were investigated, and the structures of the resultant LCB-PLAs were characterized by 1H NMR spectroscopy and SEC-MALLS. By adjusting the degree of polymerization and the composition of the prepolymers, LCB-PLAs with different branch densities and molecular weights between branch points were obtained. The effect of macromolecular chain branching on the rheology and crystallization of PLA was also investigated. The LCB structure contributed to the enhancement of the zero-shear viscosity, complex viscosity, storage modulus, melt strength, and strain hardening under elongational flow. Thermal behavior indicated that the branch str.
Fast-Slow-Scale Interaction Induced Parallel Resonance and its Suppression in Voltage Source Converters

Multi-timescale interaction of power electronics devices, including voltage source converter (VSC), has made the stability and analysis of high penetrating renewable power systems very complicated. In this paper, the impedance model is used to analyze the multi-timescale characteristics and interaction of the VSC. Firstly, the multi-timescale impedance characteristics of VSC are investigated based on the Bode plots. It is found that the slow-timescale (within the DC-link voltage control scale) and fast-timescale (within the AC current control scale) models are separately consistent with the full-order model perfectly within their low- and high-frequency ranges. In addition, there exists a high impedance peak within the intermediate frequency range (roughly from 10 Hz to 100 Hz). Then, the impedance peak is theoretically estimated and explained by the slow-fast-scale impedance parallel resonance through transfer-function diagram analysis. Moreover, it is found that the impedance peak is more related to some outer controllers, such as the alternative voltage control and active power control. Specifically, larger proportional coefficients can greatly suppress the resonance peak. Finally, simulations and experiments are conducted to verify the generality of the multi-timescale characteristics and interaction of the VSC. Hence these findings are not only significant to provide a physical insight into the inner key structure of the impedance of VSC, but also expected to be helpful for controller and parameter design of the VSC.
Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability (Artifact)

Object-sensitive pointer analysis for an object-oriented program can be accelerated if context-sensitivity can be selectively applied to some precision-critical variables/objects in the program. Existing pre-analyses, which are performed to make such selections, either preserve precision but achieve limited speedups by reasoning about all the possible value flows in the program conservatively or achieve greater speedups but sacrifice precision (often unduly) by examining only some but not all the value flows in the program heuristically. In this paper, we introduce a new approach, named Turner , that represents a sweet spot between the two existing ones, as it is designed to enable object-sensitive pointer analysis to run significantly faster than the former approach and achieve significantly better precision than the latter approach. Turner is simple, lightweight yet effective due to two novel aspects in its design. First, we exploit a key observation that some precision-uncritical objects can be approximated based on the object-containment relationship pre-established (by applying Ander-sen’s analysis). This approximation introduces a small degree yet the only source of imprecision into Turner . Second, leveraging this initial approximation, we introduce a simple DFA to reason about object reachability for a method intra-procedurally from its entry to its exit along all the possible value flows established by its statements to finalize its precision-critical variables/objects identified. We have validated Turner with an implementation in Soot against the state of the art using a set of 12 popular Java benchmarks and applications.
Machine Learning based calibration time reduction for Gas Sensors in Temperature Cycled Operation

This paper shows the opportunities of data preprocessing and how it influences the time required to record a sufficient amount of valid calibration data samples. Specifically, we approach the minimum needed time for calibration from two sides: on the one hand, repetitions are omitted for training one by another to define the lowest number of valid data that is needed for a model to achieve a reasonable accuracy. On the other hand we add samples, that are labeled as valid data points by steady-state detection to the dataset compared to a time-consuming manual annotation. The results will be demonstrated on a dataset of a metal oxide semiconductor gas sensor in temperature cycled operation measuring mixtures of artificial room air containing several volatile organic compounds and quantifying formaldehyde which is carcinogenic and therefore of high concern in indoor environments. The dataset is generated with an automated gas mixing system and then optimized with the help of data pre-processing methods based on steady-state detection, outlier detection and ResNet neural networks. The dataset can be reduced to only 50 % of the original data and is still able to train an artificial neural network with a root mean square error smaller than 25 % compared to the guideline value for formaldehyde concentration defined by the WHO.
Performance Analysis of Mobile Broadband Networks With 5G Trends and Beyond: Urban Areas Scope in Malaysia

The performance of Mobile Broadband (MBB) services of Fourth Generation (4G) and Third Generation (3G) mobile networks over urban morphology is studied in Malaysia based on experimental measurements of drive test data. The aim of this study is to provide a roadmap for service providers to establish a realistic plan for future Fifth Generation (5G) networks. This work is a continuation of our previous work for the scope of rural areas in Malaysia. The MBB measurement data have been gathered through drive tests conducted in the urban areas of four states throughout Malaysia (namely, Klang Valley/Selangor, Johor, Sarawak and Sabah) to characterise and analyse MBB performances. The gathered data are from the cities, highways and federal roads of the chosen states, and encompasses three main Mobile Network Operators (MNOs). Data has been collected in a time span of 2 months, from January to February, using the Samsung Galaxy S6 smartphone handsets. Four MBB Key Performance Indicators (KPIs) are considered in this study (coverage, latency, satisfaction and speed) for two MBB services (web browsing and video streaming). The measurement data for characterising the performance of each MBB service has been collected using a dedicated smartphone handset. YouTube videos with 720p and 1080p resolutions have been sequentially streamed to assess the performance of MBB video-streaming services. Three distinct websites (Google, Instagram and mStar) have been accessed to evaluate the performance of MBB web-browsing services. The experimental methodology of this study integrates several diversified elements including four different urban states, four distinct KPIs, three main MNOs, two MBB services and two radio networks (4G and 3G), which are both accessible by the smartphones when available to mimic real-world scenarios. The results of this study reveal that the performance of 4G radio networks is generally superior to that of 3G. For instance, 4G networks achieved a vMOS score of more than 3 for both MBB video-streaming and web-browsing services, while 3G networks scored less than 3 across all four study areas. The analysed experimental results confirmed that compared to 3G networks, 4G technology presents an enhancement factor of up to 1.6 and 4.2 in download speed when streaming a video and browsing a web page, respectively. The study outcomes can contribute to the efficient planning of non-standalone (NSA) 5G networks in Malaysia where 5G networks will be aided by existing 4G infrastructures. Analysing the 4G coverage performance is the first step towards deciding the deployment rate of NSA 5G in Malaysia.
Process Optimization and Quality Analysis of Carambola (Averrhoa carambola L.) Wine

Abstract The juice extracted from carambola (Averrhoa carambola L.) fruits was used for wine production after reconstituting with distilled water, 50% (w/v) sugar syrup and 0.1 N oxalic acid at different concentrations to attain a range of different independent processing parameters. The processing conditions i.e., pH, fermentation temperature, inoculum size, and total soluble solids (TSS) were optimized on the basis of percentage of ethanol production. At optimum processing conditions of pH = 4.5, temperature = 26°C, inoculums size = 12% (v/v), and TSS = 24°B, the production of ethanol percentage in the wine was 12.15 ± 0.28% (v/v). At this optimum condition, the values of titratable acidity, total sugar, reducing sugar, and TSS were 0.76 ± 0.21% (w/w), 2.84 ± 0.22% (w/w), 2.65 ± 0.16% (w/w), and 4.6 ± 0.06°B, respectively. Color of the wine was observed to be light greenish yellow, but pH level which was 3.94 ± 0.17 was slightly higher than that of the acceptable limit. Sensory evaluation showed that the wine possessed very good taste, aroma, and clarity with moderately good body and aftertaste. It was also observed that the wine undergoes color changes during storage with more stability at lower storage temperature.
Differential Privacy for Coverage Analysis of Software Traces (Artifact)

This work considers software execution traces, where a trace is a sequence of run-time events. Each user of a software system collects the set of traces covered by her execution of the software, and reports this set to an analysis server. Our goal is to report the local data of each user in a privacy-preserving manner by employing local differential privacy, a powerful theoretical framework for designing privacy-preserving data analysis. A significant advantage of such analysis is that it offers principled “built-in” privacy with clearly-defined and quantifiable privacy protections. In local differential privacy, the data of an individual user is modified using a local randomizer before being sent to the untrusted analysis server. Based on the randomized information from all users, the analysis server computes, for each trace, an estimate of how many users have covered it. Such analysis requires that the domain of possible traces be defined ahead of time. Unlike in prior related work, here the domain is either infinite or, at best, restricted to many billions of elements. Further, the traces in this domain typically have structure defined by the static properties of the software. To capture these novel aspects, we define the trace domain with the help of context-free grammars. We illustrate this approach with two exemplars: a call chain analysis in which traces are described through a regular language, and an enter/exit trace analysis in which traces are described by a balanced-parentheses context-free language. Randomization over such domains is challenging due to their large size, which makes it impossible to use prior randomization techniques. To solve this problem, we propose to use count sketch , a fixed-size hashing data structure for summarizing frequent items. We develop a version of count sketch for trace analysis and demonstrate its suitability for software execution data. In addition, instead of randomizing separately each contribution to the sketch, we develop a much-faster one-shot randomization of the accumulated sketch data. One important client of the collected information is the identification of high-frequency (“hot”) traces. We develop a novel approach to identify hot traces from the collected randomized sketches. A key insight is that the very large domain of possible traces can be efficiently explored for hot traces by using the frequency estimates of a visited trace and its prefixes and suffixes. Our experimental study of both call chain analysis and enter/exit trace analysis indicates that the frequency estimates, as well as the identification of hot traces, achieve high accuracy and high privacy.