Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance improvement of filter bank multicarrier systems

Ever increasing demands for higher data rates in mobile communication present an ultimate challenge to deliver a consistent service experience to users. Filter bank multicarrier (FBMC) systems with very low out of band radiation facilitate aggregation of non-adjacent bands and asynchronous transmissions to provide the service enhancement. The primary research objective in these systems is followed by the contributions provided in this thesis. First of all, filter bank based multicarrier systems using fast convolution approach are investigated. We show that by applying FFT-based convolution without overlapped processing, the circular distortion can be discarded as a part of orthogonal interference term. The advantages of this property are spectral efficiency enhancement in the system and complexity reduction. The results show spectral efficiency enhancement by up to 15\% compared to conventional FBMC, while the complexity of the system is roughly half of conventional FBMC. Secondly, two channel estimation methods for MIMO-FBMC are proposed. The first one is a preamble-based approach aiming to reduce the preamble duration overhead and peak to average power ratio compared to the existing methods. The results confirm that this method outperforms the existing methods in terms of the mentioned parameters with up to 55\% in the overhead reduction. The second approach is a comb-based method with avoiding the contamination of pilots with symbol interference and saving time-frequency resources compared to existing methods. The results show a comparable performance of this method with is OFDM counterpart. Finally, we investigate the problems of transmitting complex symbols in FBMC systems. The challenge in these systems is designing appropriate filters to minimize the interference between adjacent subcarriers while maintaining the Nyquist property of the filter. We exploit fractional shift of the filtered symbols to compensate the deviation from the Nyquist property. The results show the proposed filter provides approximately 3 dB improvement of signal to interference ratio compared to the conventional filters.
Task/effector system models of dexterity for machine assembly

The author reviews the need for a quantitative measure of dexterity based on task/effector interactions. These are quantified in the human perspective, and machine dexterity is shown to have parallel measures and limitations. An information-based definition of dexterity is shown to provide a quantitative measure between different robotic systems that have varying degrees of dexterity and the interactions between a specific assembly task being performed. Values for the index of difficulty are shown to vary in the work space, and the loci of maximum dexterity which indicate the most favorable task/effector arrangements are determined. The peg-in-hole task is used as an example application of this approach. <<ETX>>
Graph-Constrained Sparse Performance Modeling for Analog Circuit Optimization via SDP Relaxation

In this paper, a graph-constrained sparse performance modeling method is proposed for analog circuit optimization. It builds sparse polynomial models constrained by an acyclic graph. These models can be used to solve analog optimization problems within local design spaces by using convex semidefinite programming relaxation both efficiently and robustly. Our numerical examples demonstrate that the proposed modeling and optimization method can quickly and accurately converge to a superior solution for analog circuits while the conventional method fails to work.
An error concealment scheme for MPEG-2 video transmission over ATM-based networks

In the process of transmission of digital-encoded video bitstreams over an ATM network, cells are inevitably exposed to delays and losses due to the statistical multiplexing and dynamic resource allocation mechanisms used in these networks. Error-loss concealment techniques have been proposed to limit the impact of such impairments over the video quality. These techniques alone may provide acceptable results when the cell loss probability does not exceed 10/sup -3/. For higher cell loss probabilities, these techniques must be used in conjunction with hierarchical coding schemes and packetization strategies in order to obtain satisfactory results. We propose the use of a hierarchical encoding scheme and a dynamic data-partitioning strategy for the transport of MPEG-2 video streams over ATM networks.
Heading-aided odometry and range-data integration for positioning of autonomous mining vehicles

An enhanced odometry technique based on the heading sensor called "clino-gyro" that fuses the data from a fiber optic gyro and a simple inclinometer is proposed. In the proposed scheme, inclinometer data are used to compensate for the gyro drift due to roll/pitch perturbation of the vehicle while moving on the rough terrain. Providing independent information about the rotation (yaw) of the vehicle, clino-gyro is used to correct differential odometry adversely affected by the wheel slippage. Position estimation using this technique can be improved significantly, however for the long term applications it still suffers from the drifts of the gyro and translational components of wheel skidding. Fusing this enhanced odometry with the data from environmental sensors (sonars, laser range finder) through Kalman filter-type procedure a reliable positioning can be obtained. This technique has been implemented on-board of an experimental skid-steered vehicle. Obtained precision is sufficient for navigation in underground mining drifts. For open-pit mining applications further improvements can be obtained by fusing proposed localization algorithm with GPS data.
Thermal regulation and balancing in modular multilevel converters

Modular multilevel converters (MMCs) are envisaged as the key power electronic converter topology to enable a multi-terminal pan-European high voltage direct current (HVDC) Supergrid for the interconnection of offshore wind farms and exchange of energy between different countries. A key feature of MMCs in the large number of semiconductor devices employed in each converter station, distributed over a stack of series-connected sub-modules (SMs). These semiconductors possess strict thermal limits, which can constrain the operating range on the converter by limiting its capability of providing enhanced functionalities to the AC grid such as short-term power overloads. Furthermore, due to different loading conditions and ageing, significant temperature differences can exist between SMs which can lead to a very different lifetime expectancies for the semiconductor modules. This thesis proposes active thermal control methodologies to act of two distinct converter levels. Firstly, two novel dynamic rating strategies are proposed to define the converter current injection limit as a response to the maximum semiconductor temperature feedback. This enables the exploitation of the semiconductors thermal headroom to provide short-term power overloads, which can be used for the improvement of the frequency support of a power-distressed AC grid. Secondly, a SM-level temperature regulation and balancing algorithm is proposed, aiming at the equalisation of the maximum semiconductor die temperature in all the SMs of an MMC arm. The proposed methods are validated in a detailed and combined electro-thermal simulation model with 3 and 10 SMs per arm developed in MATLAB®/Simulink® using PLECS® Blockset. An experimental platform has been designed and utilised to verify the effectiveness of the dynamic rating strategies and the SM temperature regulation and balancing strategy.
Kernel Optimized Regression Model for Product Recommendation Connecting Social media to E-Commerce

: Today, many e-commerce sites are accessible through the use of user social network accounts like Facebook, Weibo, etc. Users of social media can post their recently purchased products. The product is recommended to the user based on microblogging features. The microblogging features such as demographic attributes, Text attributes, Network attributes and temporal attributes. Based on demographic attributes (age, gender, marital status, education, and carrier) the product is recommend to the users in cold start recommendation. The LDA is used to extract the topics of each user from microblog. In order to increase the number of topics the NLP is used. After extracting microblogging features the MART (Multiple Additive Regression Tree) is performed. The SVM (Support Vector Machine) is used to learn the dataset. The similarities between the users are evaluated and product is recommended. Here the optimization problem very complex and the tuning of parameters are not done properly. Hence Genetic algorithm is used to obtain the parameters of SVM. Through iterative process the fitness of every solution is evaluated. Using simple cross over and mutation operation a most fitted solution is suggested. In this scenario, the most suited ranked products for each user is recommended.
Embedded coding for 1-D signals using zerotrees of wavelet coefficients

Shapiro's (1993) concept of embedded zerotree wavelet coding for images is used to develop an embedded coding method for 1-D signals. In this method, the zerotrees of the wavelet coefficients are used to predict insignificant coefficients across scales. The bit stream is generated in the order of importance. The decoder can generate the same zerotrees as the encoder. The embedded property of this method allows precise bit rate and signal quality control. The algorithm is illustrated by an example. Embedded coding is then applied to bathymetric waveforms. Simulation results indicate that the method is quite efficient and produces a fully embedded coding for 1-D signals.
Optimization for Vehicle Routing Based on Taxi Pick-up Models

With the increase of traffic congestion, public transport is gradually rising and energy saving and environmental protection is more and more advocated. According to the taxi pick-up model, the paper will optimize the path of a taxi running and analysis to figure out that the cost of the total useless cost depends on the empty running cost of the taxi for a certain period of time. On this basis, the original taxi pick-up problem is transformed into a classic TSP problem. The corresponding mathematical model is established and an improved adaptive genetic algorithm is designed.
Analysis of correlation between full-waveform metrics, scan geometry and land-cover: an application over forests

Abstract. For a correct use of metrics derived from processing of the full-waveform return signal from airborne laser scanner sensors any correlation which is not related to properties of the reflecting target must be known and, if possible, removed. In the following article we report on an analysis of correlation between several metrics extracted from the full-waveform return signal and scan characteristics (mainly range) and type of land-cover (urban, grasslands, forests). The metrics taken in consideration are the amplitude, normalized amplitude, width (full width at half maximum), asymmetry indicators, left and right energy content, and the cross-section calculated from width and normalized amplitude considering the range effect. The results show that scan geometry in this case does not have a significant impact scans over forest cover, except for range affecting amplitude and width distribution. Over complex targets such as vegetation canopy, other factors such as incidence angle have little meaning, therefore corrections of range effect are the most meaningful. A strong correlation with the type of land-cover is also shown by the distribution of the values of the metrics in the different areas taken in consideration.
Automated Web-Bot Implementation using Machine Learning Techniques in eLearning Paradigm

E-Learning is very powerful tool and technique that is used to train and educates the people in these days. A lot of world ranking degree awarding universities are begun offer to deliver numerous courses lectures for high school grade education to degree level and even at post-graduation level through eLearning and distance learning paradigm. This research paper extensively concentrates supervised and unsupervised learning methods & strategies which are helpful for the e-Learning framework to automated answer of learners and student inquiries. Primary disadvantage of e-Learning paradigm is not in time answers of learner’s questions, which decrease the learning curve of the student. The key demand in e-Learning environment is to dealing with Machine Learning classification can give higher to learning techniques but not fully automated with this paradigm. Training data set is used form to training the machine and then test data set is used to validate the this approach. This paper analyzes the different machine learning technique and proposed a solution from using these techniques.
Alignment of the Virtual Scene to the Tracking Space of a Mixed Reality Head-Mounted Display.

With the mounting global interest for optical see-through head-mounted displays (OST-HMDs) across medical, industrial and entertainment settings, many systems with different capabilities are rapidly entering the market. Despite such variety, they all require display calibration to create a proper mixed reality environment. With the aid of tracking systems, it is possible to register rendered graphics with tracked objects in the real world. We propose a calibration procedure to properly align the coordinate system of a 3D virtual scene that the user sees with that of the tracker. Our method takes a blackbox approach towards the HMD calibration, where the tracker's data is its input and the 3D coordinates of a virtual object in the observer's eye is the output; the objective is thus to find the 3D projection that aligns the virtual content with its real counterpart. In addition, a faster and more intuitive version of this calibration is introduced in which the user simultaneously aligns multiple points of a single virtual 3D object with its real counterpart; this reduces the number of required repetitions in the alignment from 20 to only 4, which leads to a much easier calibration task for the user. In this paper, both internal (HMD camera) and external tracking systems are studied. We perform experiments with Microsoft HoloLens, taking advantage of its self localization and spatial mapping capabilities to eliminate the requirement for line of sight from the HMD to the object or external tracker. The experimental results indicate an accuracy of up to 4 mm in the average reprojection error based on two separate evaluation methods. We further perform experiments with the internal tracking on the Epson Moverio BT-300 to demonstrate that the method can provide similar results with other HMDs.
Modem gain-cell memories in advanced technologies

With the advent of the slowdown in DRAM capacitor scaling [1] and the increased reliability problems of traditional 6T SRAM memories [2], industry and academia have looked for alternative memory cells. Among those, gain- cells have attracted significant attention due to their smaller size (compared to SRAM) and non-destructive read operation (compared to DRAM) as well as considerable low power and reasonable robustness. This paper first summarizes the available evidences of SRAM and eDRAM in commercial and test chips. Then, it analyzes the performance, reliability and scaling of eDRAM gain-cells in 10 and 7 nm FinFET technology; as well as above and below VT (i.e. sub-threshold).
Distraction Osteogenesis in Oral and Maxillofacial Reconstruction Applications: Feasibility Study of Design and Development of an Automatic Continuous Distractor

Distraction Osteogenesis (DO) is one of novel techniques widely used in bone Reconstruction Applications (RA). Recently, DO method has got an important role in oral and maxillofacial RA; by using DO bone defects and skeletal deformities in different cranio-maxillofacial areas can be reconstructed, with better results and reduced effects in comparison to conventional methods. In DO by using a tension-stress principle, mechanical stimulations induce bone generation and biological responses of the tissue. A DO procedure starts with bone osteotomy and implementation of distractor, before proceeding with distraction, there is a latency period which allows the callus to form initially. In the distraction phase, the generated external force goes through the moving bone segment and gradually distract the callus. After distraction phase, there is a consolidation phase and then the device is removed. Most of current DO methods are applied by manual devices; low accuracy and reliability, discontinuous force, manually-operated, and associated problems for patient are major disadvantages of manual devices. Recent studies have revealed using an automatic continuous distractor could significantly improve the DO results while decreasing the existing problems. The purpose of this study is to design and simulate a novel automatic continuous distractor to be used in DO applications. The device contains mini stepper motor and gear-box, controller, mechatronic system, LCD, and keypad. Design’s specification and simulations results show this device has the capability to generate a continuous distraction force for a successful automatic DO.
An Adaptive Random Search Algorithm with Tuning Capabilities

-This paper proposes an ad a. ptive random search algorithm called R a.s ID. A sophisticat 巴 dprobability density function is introduced for generating random search vectors The new algorithm h a.s powerful tuning capabilities such that it can perform intensified searches in perspective are a.s and diversified searches to escape from local minima.
Cloud Based Forensics Framework for Social Networks and A Case Study on Reasoning Links between Nodes

Recently, social networks have become one of the most popular tools for communication and information exchanges, and people are constructing social relationships and conducting social interactions over social networks. In this paper, we focus on digital forensics on social networks. Specifically, considering the emerging of cloud computing and big data tides, we propose a cloud based forensics framework for social networks, where social networking data is collected, stored, and analyzed through a multi-layered modularity framework using cloud computing techniques, including virtualization, distributed processing and storage, and collaboration. Besides, we also provide a case study on social link prediction, which is, reasoning links between nodes to infer possible relationships between criminals.
Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulated Electricity Markets

This paper presents a day-ahead (DA) multi-objective based joint energy and reactive power dispatch in the deregulated electricity markets. The traditional social welfare in the centralized electricity markets comprises of customers benefit function and the cost function of active power generation. In this paper, the traditional social welfare is modified to incorporate the cost of both active and reactive power generation. Here, the voltage dependent load modeling is used. This paper brings out the unsuitability of traditional single objective functions, e.g., social welfare maximization (SWM), loss minimization (LM) due to the reduction of amount of load served. Therefore, a multi-objective based optimization is required. This paper proposes four objectives, i.e., SWM, load served maximization (LSM), LM and voltage stability enhancement index (VSEI); and these objectives can be combined as per the operating condition. The simulation studies are performed on IEEE 30 bus test system by considering the both traditional constant load modeling and the proposed voltage dependent load modeling.
Natural GPU-friendly dynamic animation of human hair

Natural-looking hair is a key component for presenting believable virtual humans, because the head and face form natural focal points of the human figure. In non-static scenes, hair behaviour is just as important as its looks. Principles of physics and dynamic simulation are often used for animating hair, because other traditional animation approaches—such as skeletal animation or motion capture—are difficult to apply to hair. Dynamic animation of hair is still an open problem without a known best solution, because hair has quite specific mechanical properties which, combined with the high number of hairs typically comprising a hairstyle, make realistic and efficient simulation challenging. In this work, we focus on dynamic hair animation methods capable of providing real-time or interactive performance while staying physically plausible. Basing on research and analysis of hair properties from the cosmetic industry, we have devised a novel hair animation method which provides more realistic results than existing comparable methods while at the same time offering better performance and stability. We have applied this method to two different approaches to hair animation in order to prove its independence on any particular representation of hair. In one of these approaches, our method allows us to replace an iterative function minimisation with a direct computation, giving a speed-up of about an order of magnitude in this one stage of the simulation, while at the same time increasing its robustness. Based on further observations natural behaviour of real hair, we have also proposed a novel organisation of simulated hair. This allows representing a larger number of hair strands by simulating fewer primitives without introducing artificial interpolation. Additionally, we have analysed behaviour of real hair when in mutual contact and based on this analysis, proposed an efficient model of collision response for collisions between hair strands. Our entire method is designed to be easily usable with current massively parallel computation architectures such as GPUs. To validate this design decision, we have also created a proof-of-concept implementation of the core parts of our simulation system on the GPU.
Learning Outlier Ensembles: The Best of Both Worlds - Supervised and Unsupervised

Years of research in unsupervised outlier detection have produced numerous algorithms to score data according to their exceptionality. However, the nature of outliers heavily depends on the application context and different algorithms are sensitive to outliers of different nature. This makes it very difficult to assess suitability of a particular algorithm without a priori knowledge. On the other hand, in many applications, some examples of outliers exist or can be obtained in addition to the vast amount of unlabeled data. Unfortunately, this extra knowledge cannot be simply incorporated into the existing unsupervised algorithms. In this paper, we show how to use powerful machine learning approaches to combine labeled examples together with arbitrary unsupervised outlier scoring algorithms. We aim to get the best out of the two worlds—supervised and unsupervised. Our approach is also a viable solution to the recent problem of outlier ensemble selection.