Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Management in 2D Lidar-Based Underground Positioning

Lidar-based positioning in a 2D map is analyzed as a method to provide a robust, high accuracy, and infrastructure-free positioning system required by the automation development in underground mining. Expressions are derived that highlight separate information contributions to the obtained position accuracy. This is used to develop two new methods that efficiently select which subset of available lidar rays to use to reduce the computational complexity and allow for online processing with minimal loss of accuracy. The results are verified in simulations of a mid-articulated underground loader in a mine. The methods are shown to be able to reduce the number of rays needed without considerably affecting the performance, and to be competitive with currently used methods. Furthermore, simulations highlight the effects of errors in the map and other map properties, and how imperfect maps degrades the performance of different selection strategies.
Cytotoxicity evaluation of sixteen Nigerian medicinal plant extracts using the human rhabdomyosarcoma cell line

As part of our evaluation of plants from the Nigerian ethnobotany,sixteen extracts from fourteen medicinal plants were evaluated for toxicity and inhibition of tumour cell growth using human rhabdomyosarcoma (RD) cell line. The plant samples were extracted by maceration in methanol at room temperature and were investigated for cytotoxicactivity. The ability of each extracts to induced cell death in tissue culture was evaluated by colorimetric method using MTT dye (3-[4, 5-dimethylthiazol–2-yl]-2,5-diphenyltetrazolium bromide) and cyclophosphamide was used as control. Cell death at 50% (CC 50 ) was evaluated for all the extracts. Fourteen of the assessed extracts were cytotoxic (CC 50 20µg/mL). Methanol extracts of Quassia africana and Quassia amara stembark possessed the most significant cytotoxic activity (CC 50 = 0.09 and 0.08 µg/mL, respectively)against RD cancer cell andactivity significantly correlated with that of the control drug cyclophosphamide which had a CC 50 of 1.80 µg/mL. Conclusively, the results of the present study indicatethat selected plants demonstrated awide range of cytotoxic activities. This will be of tremendous assistance in assesssing the safety of the medicinal plants and also give direction for future anticancer drug development.
Extended Rigid Multi-Target Tracking in Dense Point Clouds with Probabilistic Occlusion Reasoning

Tracking of multiple extended three-dimensional targets in the presence of partial occlusions from point cloud measurement sets remains a challenge but has obvious applications in autonomous vehicles. We demonstrate that existing range-image based approaches to point cloud segmentation and motion detection can be fused with fast point cloud registration techniques to aid track association. Consequently, multiple generic extended targets in point clouds can be tracked. We also show that robust occlusion handling can be achieved using temporally accumulated geometric information from co-registered point clouds in a static local reference frame. The occlusion logic is based on a novel probabilistic occlusion reasoning approach combined with a simulated geometric handling of self-occlusions. The proposed tracking pipeline was tested on synthetic target geometries superimposed on real-world backgrounds, as well as against measured data from benchmark autonomous driving datasets. The targets tracked encountered both artificially introduced and naturally occurring foreground occlusions as well as self-occlusions. Results show notable robustness to foreground occlusions. Incorrect track associations are possible when multiple targets in proximity display similar dynamics and possess nearly identical geometries. Our sequence of tracking algorithms relies on multiple tunable hyper-parameters. These parameters require further automated runtime-optimization before robust, real-time field application can be achieved.
Use cases for social data analysis with URREF criteria

Social data analysis has gained prominence in a wide range of domains as it provides users with the opportunity to communicate and share posts and topics. Automated analysis and reasoning about such data potentially derive meaningful insights, with tremendous potential for applications. However, the sheer volume, noise, and high dynamics of social data impose challenges that hinder the efficacy of algorithms. Automated approaches and classification models require then significant resources to be developed and prove to be often relevant to only a limited number of tasks. Imperfections of inputs, precision of techniques and accuracy of results need to be accounted and assessed as the process runs. This paper discuses two use cases allowing the investigation of implicit and explicit uncertainty arising when processing data gleaned on social media. The objective of this paper is twofold. The first objective is to set up the ETUR use case on social media analysis by adopting two tasks on opinion mining for cyberspace surveillance and information extraction for crisis analysis, respectively. The second objective is to discuss an overall methodology allowing the identification and assessment of uncertainties underlying each task The paper introduces two illustrations of social data analysis, investigates various sources of uncertainty and describes a methodology to select criteria for uncertainty assessment.
REDBUL: An Online System for Reverse Engineering of Relational Databases

The paper presents an online system named REDBUL, which is aimed at reverse engineering of relational databases. REDBUL enables database designers to automatically extract the schema from an existing relational database, and visualize it in a web browser, whereby the extracted schema is represented by the standard UML class diagram. Currently, two relational database management systems are supported by the REDBUL system, MS SQL and MySQL, while the paper illustrates reverse schema engineering for a MySQL database.
Temporal Properties over Contextualized Description Logics

A context-sensitive system is often employed in a dynamic environment due to its adaptivity. To represent temporal properties over an evolving system, we study an extension of Contextualised Description Logics (ConDLs) with linear temporal logic (LTL) operators, where ConDL axioms are used in place of propositional variables. The resulting language is interpreted over an in(cid:28)nite sequence of nested DL-interpretations. With EL , ALC , and SHOQ in consideration, we show that the formalism is rather well-behaved in the sense that the satis(cid:28)abil-ity problem in most of the instances have the same complexity with underlying ConDLs. This holds even in the presence of rigidity constraints on the object level.
Deep learning framework for short term power load forecasting, a case study of individual household energy customer

Due to the seamless benefits of the integration of Distributed Energy Resources (DERs) for the residential customers, the forecasting of the short term power load of individual household energy customer is becoming an essential task for the future operation and planning of the smart grids. Recently, different studies concluded that due to lack of fast connectivity and awareness, the energy customer were not able to exploit the benefits of the DERs to the full extent. Nevertheless, with the rapid advancement in connectivity, data analytics, internet of things, artificial intelligence and machine/deep learning, the prospective benefits of the DERs can fully be explored. But both the short term power load of the individual energy customer and the power generated through DERs is dependent on the weather conditions and seasonality. In this paper, our focus is on forecasting the short term power load of the end energy customer using a deep learning framework. The proposed deep learning framework is based on a pyramid architecture of convolutional neural network. We developed and trained/evaluated the model for forecasting the short term power load of the individual household customer based on a large database of energy data from Australia. Our analysis indicates that forecasting the individual household power load is highly unpredictable. More than 57% of the customers (40 out 0f 69) have more than twenty outliers in the daily energy consumptions (which means highly unpredictable power load). The results show that our pyramid-CNN based deep learning approach is successful in predicting the individual household power consumption.
Gathering Effective Information for Real-Time Material Recognition

Material recognition is a fundamental problem in the field of computer vision. Material recognition is still challenging because of varying camera perspectives, light conditions, and illuminations. Feature learning or feature engineering helps build an important foundation for effective material recognition. Most traditional and deep learning-based features usually point to the same or similar material semantics from diverse visual perspectives, indicating the implicit complementary information (or cross-modal semantics) among these heterogeneous features. However, only a few studies focus on mining the cross-modal semantics among heterogeneous image features, which can be used to boost the final recognition performance. To address this issue, we first improve the well-known multiset discriminant correlation analysis model to fully mine the cross-modal semantics among heterogeneous image features. Then, we propose a novel hierarchical multi-feature fusion (HMF2) model to gather effective information and create novel yet more effective and robust features. Finally, a general classifier is employed to train a new material recognition model. Experimental results demonstrate the simplicity, effectiveness, robustness, and efficiency of the HMF2 model on two benchmark datasets. Furthermore, based on the HMF2 model, we design an end-to-end online system for real-time material recognition.
A Resource-Periodic-Arrangement Strategy for RMSA Problem in Elastic Optical Networks

Elastic optical networks (EONs), which can flexibly assign spectrum resources to connection requests, have become the most promising technology for the next generation of optical core networks. Routing, modulation and spectrum assignment (RMSA) problem is one of the key issues in elastic optical networks. In the RMSA problem, spectrum resources in links are generally divided into frequency slices and connection requests with different capacities can use different numbers of frequency slices to transfer their data; however, this may lead to misalignment of idle frequency slices in links and then result in the increase of blocking probability of connection requests. In this article, we propose a resource-periodic-arrangement (RPA) strategy. The strategy partitions frequency slices into periodically arranged standard frequency blocks and each connection request exactly uses one or more standard frequency blocks. The RPA strategy can effectively reduce the misalignment of frequency slices. Furthermore, an RPA-based RMSA algorithm is designed, in which an enhanced-most-used policy is adopted to choose standard frequency blocks to connection requests. Simulation results show that the RPA-based RMSA algorithm can distinctly reduce blocking probability of connection requests, comparing with existing algorithms.
The Application of Genomics and Epigenetics in Tumor Marker Research

The tumor markers indicate the existence and conditions of malignant tumors,which play very important roles in the early cancer screening,diagnosis,prognosis,therapeutic evaluation,recurrence and metastasis monitoring. Currently,huge number of novel tumor markers,especially the genomic and epigenetic markers have been investigated with the development of molecular and cellular biology, particularly after the human genome project( HGP). With more and more mechanisms of tumorigenesis being found,scientists pay much attention to tumor markers. In this review,the history of tumor markers,classification and clinical application are introduced. We also present the limitations of current markers and the research and application of new targets,such as epigenetic makers and circulating nucleic acids( CNAs). Finally,the tendency of tumor marker application is proposed.
Dynamic and Accurate Force Feedback for Electromagnetic Haptic Display

As the implementation way of bilateral interactions in virtual reality, haptic display can not only bring users touch feedback for the better sense of immersion, but also enable them to operate objects in virtual environments on their own initiative. In this article, we present an electromagnetic haptic display system, which performs contactless forces on the magnetic stylus in the magnetic field excited by three iron-core coils with adjustable position and orientation, for the production of haptic feedback. High-frequency pulses and H-bridge circuits are employed for the rapid current drive in coils, and it is proved that the refresh rate of our haptic device is able to fulfill the simulation of dynamic haptic feedback like push, pull and squeeze. In addition, the processes of current generation in single coil and magnetic field generation for coil array are clearly analyzed, and a Varying-Model Predictive Control algorithm is proposed, where the predictive model is updated adaptively according to the real-time motions of stylus and coils, to generate the required magnetic field smoothly and precisely. Experiments show that the magnetic flux density measured at the position of stylus follows the reference value well in three dimensions with the aid of the proposed algorithm, and dynamic and accurate electromagnetic haptic display can be achieved.
An MQTT-based Resource Management Framework for Edge Computing Systems

The complexity of IoT systems and tasks that are put before them require shifts in the way resources and service provisioning are managed. The concept of edge computing is introduced to enhance IoT systems' scalability, reactivity, efficiency, and privacy. In this paper, we present an edge computing solution for resource management of context-aware decision-making processes distributed between IoT gateways. The solution performs decision-making process management for smart actuation, based on analysis of sensory data streams, and context-informed edge computing resource and service provisioning management based on topology and operational changes. Our architectural solution showcases the first version of a Resource Management Framework - a generic framework for software resource orchestration best-suited to IoT platforms with event-driven, publish-subscribe communication mechanisms. Proof of concept experiments that are executed in a simulated edge computing testbed validate our solution's performance in improving the resilience and responsiveness of the edge computing system when there are operational and topology changes. Furthermore, the framework addresses the recovery of failed decision-making processes, impacting the overall health of the underlying IoT system.
H∞ Control for Stochastic Singular Systems With Time-Varying Delays

In this article, we study the <inline-formula> <tex-math notation="LaTeX">$\mathrm {H}_{\infty }$ </tex-math></inline-formula> control problems for stochastic singular systems with time-varying delays. Firstly, a new Lyapunov-Krasovskii functional is constructed, employing the free weighting matrix technique and Jensen inequality, the stochastic admissibility criteria in the mean square for stochastic singular time-varying delay systems are proposed on the basis of the auxiliary vector function. Secondly, the state feedback controller is designed such that the resulting closed-loop system meets regular, impulse-free, stochastically stable in the mean square and has <inline-formula> <tex-math notation="LaTeX">$\mathrm {H}_{\infty }$ </tex-math></inline-formula> performance <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>. In the proof process, the dual equation is used to derive the conditions of stochastic admissibility in the mean square. Finally, a practical example of DC motor model is presented to show the validity of our proposed theoretical results.
Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This article introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this article provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.
Adaptive compensation of measurement delays in multi-sensor fusion for inertial motion tracking using moving horizon estimation

Robust and accurate pose estimation of moving systems is a challenging task that is often tackled by combining information from different sensor subsystems in a multi-sensor fusion setup. To obtain robust and accurate estimates, it is crucial to respect the exact time of each measurement. Data fusion is additionally challenged when the sensors are running at different rates and the information is subject to processing- and transmission delays. In this paper, we present an optimization-based moving horizon estimator which allows to estimate and compensate for time-varying measurement delays without the need for any synchronization signals between the sensors. By adopting a direct collocation approach, we find a continuous-time solution for the navigation states which allows us to incorporate the discrete-time sensor measurements in an optimal way despite the presence of unknown time delays. The presented sensor fusion algorithm is applied to the problem of pose estimation by fusing data of a high-rate inertial measurement unit and a low-rate centimeter-accurate global navigation satellite system receiver using simulated and real-data experiments.
A 0.9V 64Mb 6T SRAM cell with Read and Write assist schemes in 65nm LSTP technology

A low Vmin, 6T SRAM is designed in 65nm LSTP (low standby power) technology using read and write assist schemes. In this work, we reduced the Vmin of SRAM cell from 1.2V to 0.9V by using assist schemes. Static noise margin (SNM) is improved by using Compensated Wordline Underdrive (WLUD) scheme as read assist. Using this read assist, maximum WLUD of 261mV is achieved for SNM critical PVT i.e., FS/0.9V/125C. SNM achieved is 465mV ensuring that it qualifies required cell sigma robustness. The underdrive at other PVT corners is lesser therefore it limits the impact on speed and write margin (WM) at their respective critical PVT. Negative BL approach using capacitive coupling is used as write assist in which a small capacitor of 10fF is placed in the write driver to achieve required bitline undershoot. BL undershoot obtained is 166mV which improves the WM at critical PVT i.e., SF/0.9V/-40C and thereby write-ability. WM at this PVT is 286mV. By enabling lower voltage of operation upto 45% of dynamic power reduction is achieved in the design.
Dynamic quantitative trait loci underlies isoflavone accumulation in soybean seed

Most of quantitative trait loci (QTL) underlying soybean seed isoflavone contents were derived from the harvest stage of plant development, which uncover the genetic effects that were expressed in earlier seed developmental stages. The aim of this study was to detect conditional QTL associated with isoflavone accumulation during the entire seed development. A total of 112 recombinant inbred lines developed from the cross between ‘Zhongdou 27’ (higher seed isoflavone content) and ‘Jiunong 20’ (lower seed isoflavone content) were used for the conditional QTL analysis of daidzein (DZ), genistein (GT), glycitein (GC) and total isoflavone (TI) accumulations through composite interval mapping with mixed genetic model. The results indicated that the number and type of QTL and their additive effects for individual and total isoflavone accumulations were different among R3 to R8 developmental stages. Three unconditional QTL and six conditional QTL for DZ, four unconditional QTL and five conditional QTL for GT, six unconditional QTL and five conditional QTL for GC, six unconditional QTL and seven conditional QTL for TI were identified at different developmental stages, respectively. Unconditional and conditional QTL that affect individual and total isoflavone accumulations exhibited multiple expression patterns, implying that some QTL are active for long period and others are transient. Two genomic regions, Satt144-Satt569 in linkage group F (LG F; chromosome 13, chr 13) for DZ, GC, GT and TI accumulations and Satt540-Sat_240 in LG M (chr 07) for TI and GC accumulations, were found to significantly affect individual and total isoflavone accumulations in multiple developmental stages, suggesting that the accumulation of soybean seed isoflavones is governed by time-dependent gene expression.
Research on the Linear Precoding Algorithm Based on 5G Mobile Communication Technology

This paper mainly studies the large-scale MIMO downlink precoding algorithm in 5G key technology. Firstly, the key technology of 5G and precoding technology are summarized. Then, the maximum ratio emission (MRT), zero forcing (ZF), minimum mean square error (MMSE) and other precoding methods are analyzed in detail. Finally, the capacity and bit error rate of the main algorithms are compared by simulation.
Flex-to-Install Application Performance of Power Sources Subjected to Varying Fold Orientations, C-Rates and Depths of Charge

Asset monitoring using flexible electronics may require the power sources to be flexed-to-install on non-planar surfaces during installation close to the sensor locations. The power sources may power sensors and microcontrollers tasked with acquiring data periodically till the time that the data can be downloaded for purpose of offline processing and condition-assessment of the monitored asset. Review of published literature reveals that data on performance and reliability of power sources under flexure is limited and the existing studies are not as detailed. In this paper, a test methodology has been developed for thin form flexible Lithium-ion power sources subjected to simultaneous static flexure and accelerated life testing to evaluate their capacity retention. The tests have been repeated for varying charging C-rates (1C and 2C) and fold orientations (transverse and longitudinal). In order to replicate real-world conditions, the power sources have been subjected to partial as well as deep cycling tests which run up to 150 cycles. The battery terminal voltage and current are logged during testing and are used to compute the capacity and its degradation. The effects of cycle depth, charge C-rate, depth of charge, presence or absence of flexure, and fold orientation have been characterized and a regression model has been developed to capture the dependencies on predictor variables in the data-set.