Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding the Radio Imaging in 5G Networks: Signal Processing and an Airport Use Case

Integrating sensing and communications is becoming a rising trend in the architecture design of the foreseeable mobile communications system, which could be driven by multifold applications and scarce spectrum resources. Regarding the demand for the economic surveillance solution in the secondary airports, the inborn imaging function in the 5G networks could be a promising candidate. This paper investigates the feasibility and capability of using 5G uplink and downlink reference signals for imaging purposes. An ambiguity function-based signal processing method is proposed in this paper to elaborate the imaging functionality in the 5G networks. The 5G signal-based imaging idea is validated with a realistic ray-tracing channel model generated from a simulated 3D airport model. Our method empowers the imaging functionality of the wireless communications system solely without the aid of external signal resources. Different from the conventional synthetic-aperture radar processing, our methods are adjusted for unevenly allocated reference signal symbols, which causes mirror images problem. The mirror images are quantified in the simulation result, and the mitigation strategies such as lower flight speed and narrower beam are proposed to resolve the problem.
Machine learning based signal strength and uncertainty prediction for MEC mobility management

This paper addressed mobile edge computing (MEC) mobility management using machine learning methods. The mobility decision was based on a reference signal received power (RSRP) value and uncertainty predictor. Neural networks (NNs) were used to establish the predictor, which output the RSRP mean values and standard deviations of different neighbor cells. Closed-form expressions of handover probabilities were derived. With these probabilities, the MEC server was able to cache user services in advance in order to minimize disruptions during handover. Real field data were collected in a typical dense urban area and used to evaluate the performance of the proposed algorithm.
Collaboration and the Semantic Web: social networks, knowledge networks and knowledge resources

In a time where libraries worldwide are suffering funding cuts, Ditzler and Dumas’ book is a timely reminder that libraries must learn how to self-fund. This book provides a detailed account on how to do so by holding an annual book sale. Sisters Ditzler and Dumas are both volunteer book-sale co-coordinators, and the book is written from their own experience, with Ditzler as current Chair of the Lancaster Public library book sale and Dumas having introduced her sister’s successful book-sale strategies into the Potsdam Public Library and Oro Valley Public Library. The book illustrates how critical the planning and organising of a book sale are to its ultimate success in raising funds for a library. The book consists of 10 chapters, the first of which is titled ‘TheMain Attraction’ and details a variety of ways to seek donations and keep records of items received. Chapter 2, ‘Setting the Scene’, demonstrates practical considerations when planning the book sale, such as the importance of a sorting-room site and size, and storage issues. Chapter 3, ‘Directing the Show’, indicates the type and make-up of committees needed, while Chapter 4, ‘Casting Call’, focuses on seeking volunteers, providing examples of how the three previously mentioned libraries did so. Chapter 5, ‘Production’, relates to categorising and organising book donations, for example into genre/ categories and sub-categories. ‘Ticket Price’ (Chapter 6) is self-explanatory and focuses on the pricingof items,whileChapter 7 (‘Venue andPromotion’) provides a variety of useful and proven ways to promote the book sale to a wide variety of audiences, as well as highlighting the need to choose a relevant site to hold the book sale (not necessarily in the library). Chapter 8 (‘Showtime’) comes to the nitty-gritty and works through the book-sale day itself, from setup to clean-up, including diagrams of how a book sale might be laid out, with examples of which categories of items should be alongside each other and examples of thank-you notes to donors and buyers. ‘Box Office Receipts’ (Chapter 9) provides information and examples on how to tallymoney at the time of a sale and at the completion of thewhole book sale, aswell as examples of receipts to give to buyers. Chapter 10, ‘Encore, Encore’, provides advice and examples on other book-sale opportunities, such as selling online. This summary only touches on the amount and variety of information provided in each chapter. The book contains an immense amount of practical advice on organising and running a library book sale, and it is evident that the authors are not only passionate about their role but also very knowledgeable. While not every library holding a book sale will follow all the strategies noted in the book, it still provides a very useful resource for any library looking for ways to raise funds.
Mutually converted arc–line segment-based SLAM with summing parameters

This paper presents a mutually converted arc–line segment-based simultaneous localization and mapping (SLAM) algorithm by distinguishing what we call the summing parameters from other types. These redefined parameters are a combination of the coordinate values of the measuring points. Unlike most traditional features-based simultaneous localization and mapping algorithms that only update the same type of features with a covariance matrix, our algorithm can match and update different types of features, such as the arc and line. For each separated data set from every new scan, the necessary information of the measured points is stored by the small constant number of the summing parameters. The arc and line segments are extracted according to the different limit values but based on the same parameters, from which their covariance matrix can also be computed. If one stored segment matches a new extracted segment successfully, two segments can be merged as one whether the features are the same type or not. The mergence is achieved by only summing the corresponding summing parameters of the two segments. Three simultaneous localization and mapping experiments in three different indoor environments were done to demonstrate the robustness, accuracy, and effectiveness of the proposed method. The data set of the Massachusetts Institute Of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) Building was used to validate that our method has good adaptability.
Deep Reinforcement Learning With Bidirectional Recurrent Neural Networks for Dynamic Spectrum Access

This paper considers the problem of Dynamic Spectrum Access (DSA) with partial observations under the cognitive radio framework. It is the heart of the matter for improving spectrum utilization efficiency. In general, DSA can be formulated as a partially observable Markov decision process (POMDP). While, extensive computational complexity is prohibitive for existing traditional methods. And the lack of environmental information makes some data-driven methods perform poorly. Bidirectional recurrent neural network (BRNN) is an extended form of unidirectional LSTM. Processing input sequence from both directions gives BRNN strong ability to perceive and predict sequential information. By introducing BRNN to deep reinforcement learning (DRL), Deep Bidirectional Recurrent Q-Network (DBRQN) is formed to obtain sequential correlation of input and achieve the optimal strategy. Extensive experiments indicate the efficiency and robustness of the proposed method in the case of simulated data and real data.
Towards Delay-Optimal Multi-Connectivity Traffic Management for Edge Networks

With multiple radio access technologies such as Wi-Fi, LTE, 5G available at the edge, increasingly prevalent multi-radio end devices can establish multiple concurrent connections with the edge server to deliver data traffic with more bandwidth, lower latency and higher reliability. To fully harvest multi-connectivity benefits, the edge network requires intelligent traffic management to efficiently utilize radio resources as well as to optimize quality-of-service (QoS) metrics. This paper presents a general framework for multi-connectivity traffic management at the edge, and provides algorithms for routing traffic over multiple paths to enhance the latency QoS metric. Simulation results demonstrate that, with two concurrent radio connections such as LTE and WiFi, our proposed edge traffic management algorithm can achieve significant latency reduction by more than 4× for the 95-th packet latency, and 23× in delay violation rate reduction given 10 ms latency target when compared to state-of-the-art client-based traffic management solutions.
Kinematic Analysis and Prototyping of a Partially Decoupled 4-DOF 3T1R Parallel Manipulator

A four-degree-of-freedom (DOF) 3T1R parallel manipulator is presented in this paper. This manipulator generates the family of so-called Schonflies motions, SCARA motions or 3T1R motions, in which the moving platform can translate in all directions and rotate around an axis of a fixed direction. The kinematic analysis of this architecture is presented, including the study of the constraint singular configurations, kinematic singular configurations and the determination of the workspace. A prototype (the Quadrupteron) is also presented and demonstrated. The characteristics of the proposed prototype are: (a) there is no constraint singularity, (b) its input-output equations are partially decoupled, (c) its kinematic singular configurations can be expressed using an equation in the angle of rotation of the moving platform and are thus easy to avoid at the design stage, and (d) its forward displacement analysis requires the solution of a univariate quadratic equation and can therefore be solved efficiently.Copyright © 2006 by ASME
Voltage Support Capacity During Grid Disturbances Based on Flexible Excitation System with ESS

Flexible excitation system is a new type of excitation system. When it is combined with an energy storage system (ESS), it can control the exchange of active and reactive power between the AC side and the grid. This paper analyzes the qualitative and quantitative analysis of the flexible excitation system’s ability to support the grid voltage under large disturbances by simplifying the model and simulating. The results show that the flexible excitation system with ESS can provide effective voltage support under various faults. It is a prospective substitute to the conventional excitation system in the future large power grid.
SuperTrack

In this paper we show how the task of motion tracking for physically simulated characters can be solved using supervised learning and optimizing a policy directly via back-propagation. To achieve this we make use of a world model trained to approximate a specific subset of the environment's transition function, effectively acting as a differentiable physics simulator through which the policy can be optimized to minimize the tracking error. Compared to popular model-free methods of physically simulated character control which primarily make use of Proximal Policy Optimization (PPO) we find direct optimization of the policy via our approach consistently achieves a higher quality of control in a shorter training time, with a reduced sensitivity to the rate of experience gathering, dataset size, and distribution.
Multi-class inverted stippling

We introduce inverted stippling, a method to mimic an inversion technique used by artists when performing stippling. To this end, we extend Linde-Buzo-Gray (LBG) stippling to multi-class LBG (MLBG) stippling with multiple layers. MLBG stippling couples the layers stochastically to optimize for per-layer and overall blue-noise properties. We propose a stipple-based filling method to generate solid color backgrounds for inverting areas. Our experiments demonstrate the effectiveness of MLBG in terms of reducing overlapping and intensity accuracy. In addition, we showcase MLBG with color stippling and dynamic multi-class blue-noise sampling, which is possible due to its support for temporal coherence.
The predictive role of Troponin I levels for mortality in geriatric patients transferred to the intensive care unit for COVID-19 pneumonia

Aim: Troponin I is an important prognostic marker in critically ill patients with COVID-19, similar to cytokines and other inflammatory mediators. The aim of this study was to evaluate the predictive value of troponin I levels for mortality in geriatric patients transferred to the intensive care unit for COVID-19 pneumonia according to age group. Material and Methods: Seventy-four patients with COVID-19 pneumonia were grouped according to age (Group 1:65-74 years, Group 2: 75-84 years, and Group 3: >= 85 years) and retrospectively analyzed. Demographics, clinical findings, laboratory results upon admission to the intensive care unit, and outcomes were compared among the groups. Predictive value of troponin I levels upon admission to intensive care unit (Troponin Iicu), difference in troponin levels between general wards and intensive care unit (Troponin Idiff), C-reactive protein, ferritin, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, procalcitonin, and D-dimer levels for mortality were also investigated. Results: The mortality rate was 74.3% for the patients overall, and increased, albeit insignificantly, with increasing age. Neither Troponin Iicu nor Troponin Idiff was predictive for mortality for any of the age groups or for the patients overall. Ferritin, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, and C-reactive protein levels were predictive for mortality for patients overall (p= 0.016, p= 0.001, p= 0.013, and p < 0.001, respectively). Discussion: For geriatric patients, troponin I levels at the time of the first admission to the ICU are not sufficient to predict mortality alone and should be evaluated together with other parameters.
Survey of Real Time Task Scheduling Algorithms for Multicore Processors

We consider a survey on scheduling of real time on multicore processors. In this survey, we observe that the cores are distributed for the incoming task according to their workloads. Depends on the task workload the cores may be splitted into big and small core. The big cores are having more Floating Point and multiply accumulate(FPMAC) units also the small cores having less number of FPMAC units. We can use the big core for complicated tasks and small one for easy tasks. Because of this dividing of cores we can use cores efficiently and the output will be reliable one. The survey outlines fundamental results about multicore processor real-time scheduling that hold independent of the scheduling algorithms employed. The scheduling algorithm such as LTF scheduling, EDF scheduling, Priority scheduling are used according to their algorithm.
Kernel Functional Optimisation

Traditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitations induced by the parametric forms. In this paper, we propose a novel formulation for kernel selection using efﬁcient Bayesian optimisation to ﬁnd the best ﬁtting non-parametric kernel. The kernel is expressed using a linear combination of functions sampled from a prior Gaussian Process (GP) deﬁned by a hyperkernel. We also provide a mechanism to ensure the positive deﬁniteness of the Gram matrix constructed using the resultant kernels. Our experimental results on GP regression and Support Vector Machine (SVM) classiﬁcation tasks involving both synthetic functions and several real-world datasets show the superiority of our approach over the state-of-the-art.
A Survey Paper on Error Minimization in Indoor Wireless Sensor Network Localization Using Genetic Technique

:- In this field research paper, error minimisation in indoor wireless sensor network localisation using the genetic technique. Sensor localisation primarily based on techniques; several wireless device network applications need awareness of the physical location for every node. The discovery of the position is complete utilising range measurements and sensor localisation received signal strength (RSS) in time of arrival and sensor localisation received signal strength in a time difference of arrival and angle of arrival. Positioning techniques supported the angle of arrival info between neighbour nodes. A wireless sensor network using positioning techniques primarily based on WSN nodes position estimation techniques is thought to be localisation. There has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that can control their movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain Node Localisation in wireless device network is very important for several applications, and to seek out the position with Received Signal Strength Indicator needs a variety of anchor nodes. Receptive wireless device network techniques received signal strength and angle of arrival primarily based on the localisation technique for WSN.A proposed algorithm as a genetic technique for wireless sensor network genetic algorithmic based on positioning techniques as proposed techniques. In the study, this paper problem that the positioning accuracy is low with minimum anchor nodes. Find the optimum location by satisfying each of the factors with minimal error and the best possible solution.
Reduction of Quantizing Noise by Use of Feedback

Many information transmission systems use a discrete (digital) channel. Since most input signals are continuous, the conversion cannot be accomplished without an error which, for many cases, may be considered to have the characteristics of white noise. A method has been suggested to reduce this error by using linear feedback around the quantizer to shape the noise spectrum. Each output sample will then contain not only signal information but also information about the errors in the previous samples. Such a system is analyzed for random input signals of a rather general nature. Under assumptions allowing essentially no clipping in the quantizer and setting an upper bound on the coherence between samples of the input signal, the system can be represented by a simple model. A comparison is made of the mean-square error with and without feedback. It is shown that considerable reduction in noise power can be obtained by a slight increase in sampling rate. For example, an increase of 25 per cent in the sampling rate provides a 95 per cent decrease in error-noise power. This is equivalent to having about two additional bits per sample in the transmission channel.
Process control and monitoring with laser interferometry based endpoint detection in chemical mechanical planarization

First, a brief presentation of the principles behind laser interferometry based in-situ endpoint detection is made, including the underlying theory of operation, system architecture and filtering methodology. This is followed by experimental data taken with various process wafers, including tungsten, copper, blanket oxide, silicon-on-oxide, shallow trench isolation (STI), and interlayer dielectric (ILD) wafers. Pre- and post-processing thickness data and removal rates are compared with ex-situ measurements for accuracy and repeatability. Finally, specific examples are discussed to show the benefits of in-situ removal rate monitoring and endpoint detection in chemical mechanical planarization for improved line monitoring and process control.
Bringing order to a complex system: phenotypic and genotypic evidence contribute to the taxonomy of Tityus (Scorpiones, Buthidae) and support the description of a new species

﻿Abstract We present a molecular phylogenetic analysis including a survey for overlooked phenotypic characters. Based on both analysis and characters a new cave-dwelling species is described: Tityus (Tityus) spelaeussp. nov. from the Russão II cave, Posse, state of Goiás, Central Brazil. Characters such as the glandular regions of the female pectinal basal piece and basal middle lamellae of pectines, and the distribution of the ventral setae of telotarsi I–IV proved to be useful to constructing the taxonomy of species and species groups of Tityus. The new species is a member of the Tityustrivittatus species-group of Tityus (Tityus) and can be readily recognized by the immaculate coloration pattern and the more developed glandular region on the female pectinal basal piece. In addition, we provide a discussion of the phylogenetic relationships observed within Tityus, on the relevance of the new phenotypic characters to the modern taxonomy of the genus Tityus, and to the records of Brazilian cave scorpions.
LUXURY HOSPITALITY SERVICE ADOPTION AMONGST INDIAN MILLENNIAL: AN ANALYTICAL APPROACH

Understanding the Indian millennial travelers is vital in providing insights into their behavior while presenting additional challenges to successfully targeting different demographic cohorts for luxury products and services. The present study investigates the factors that define the value perceptions and attitude of Millennials towards luxury service consumption in India. The findings revealed social value as an important factor that influences the attitude of millennial consumers towards purchasing luxury tourism services. Along with providing a theoretical model for behavior, the study provides insights into measuring tourist value preferences by utilizing empirical data within luxury tourism contexts and suggests the tourism and hospitality business practitioners to make innovations in product, service management, and marketing to draw the attention of such a potential segment. Moreover, this study augments the existing knowledge of luxury value and travel patterns of emerging and potential consumers while developing a theoretical model to validate and analyze the luxury service consumption amongst them.
Active T‐shirt

Purpose – The purpose of this paper is to realize an electronic circuit design on the fabric surfaces to form a fully integrated functional active T‐shirt structure.Design/methodology/approach – Functional products combining textile, electronics and the software have attracted great attention in recent years. The integration of the electrical and electronic devices on the garment surface using conductive threads is a challenging issue considering conductiveness, long durability, washability and manufacturing process. As an application, a group of light emitting diode (LED) lights controlled by a light sensor, accelerometer and related electronic control circuits were placed on a fabric construction.Findings – The brightness of LED lights is controlled by using a light sensor depending on the perceived ambient light intensity. LED lighting patterns are controlled by means of an accelerometer which senses the physical activities of the wearer, such as walking, running and standing.Originality/value – In thi.