Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronization challenges in deep space communications

Deep space missions keep pushing for new frontiers affecting a wide spectrum of disciplines. To support the scientific achievements expected from new missions, communication technology is being pushed towards its limits [1]. A need to increase communication links data rate as well as to lower the operative signal-to-noise ratio (SNR) are identified. The adoption of advanced coding schemes such as turbo codes and low-density parity-check (LDPC) codes (e.g., Consultative Committee for Space Data Systems (CC-SDS) standards) allows receivers to operate at lower SNRs. However, in order to exploit the full potential of the coding gain, the receiver must be able to acquire and track a signal with a SNR much lower than expected in nominal conditions of state-of-the-art systems. The target operating point is given by the candidate LDPC codes [2], where the codeword error rate is set to WER ≤ 10−5, achieved at the bit energy to noise density ratio Eb/N0 ≥ 5.2 dB, ≥ 3.6 dB for LDPC(128,64) and LDPC(256,128), respectively. In [3] the first receiver bottleneck related with frame synchronization, a functionality required previous to channel decoding, was identified. Even though frame synchronization enhancements were proposed beyond standard correlation techniques [3], [4], [1], it was recommended to increase the synchronization word length in order to achieve the target performance. The recommendation was recently adopted by the CCSDS. In this work, the focus lies on the receiver synchronization stages (i.e., acquisition and tracking). Not only from a research standpoint, but also for the design of next generation Telemetry Tracking & Command (TT&C) transponders, it is of capital importance to understand the performance limitations of state-of-the-art deep space communications architectures, clearly identifying possible bottlenecks and the synchronization stages (i.e., acquisition and tracking) to be improved. Digital carrier and timing synchronization have been an active research field for the past three decades in applications such as satellite-based positioning or terrestrial wireless communications systems. In those scenarios, the limitations of standard delay, frequency, and phase-locked loop (delay-locked loop, frequency-locked loop (FLL), and phase-locked loop (PLL), respectively) architectures have been clearly overcome by Kalman filter (KF) based solutions [5], which provide an inherent adaptive bandwidth, robustness, flexibility, and an optimal design methodology. Despite the advances in the field, synchronization architectures for deep space communications links, implemented in current TT&C transponders, still rely on well-known conventional architectures, which may be insufficient if limits are pushed to extremely low SNR or harsh propagation conditions. With the advent of powerful software defined radio receivers and new system design rules, it is now possible to adopt new robust architectures that may enable going beyond the performance and reliability provided by legacy solutions.
Vocal Effort Based Speaking Style Conversion Using Vocoder Features and Parallel Learning

Speaking style conversion (SSC) is the technology of converting natural speech signals from one style to another. In this study, we aim to provide a general SSC system for converting styles with varying vocal effort and focus on normal-to-Lombard conversion as a case study of this problem. We propose a parametric approach that uses a vocoder to extract speech features. These features are mapped using parallel machine learning models from utterances spoken in normal style to the corresponding features of Lombard speech. Finally, the mapped features are converted to a Lombard speech waveform with the vocoder. A total of three vocoders (GlottDNN, STRAIGHT, and Pulse model in log domain (PML)) and three machine learning mapping methods (standard GMM, Bayesian GMM, and feed-forward DNN) were compared in the proposed normal-to-Lombard style conversion system. The conversion was evaluated using two subjective listening tests measuring perceived Lombardness and quality of the converted speech signals, and by using an instrumental measure called Speech Intelligibility in Bits (SIIB) for speech intelligibility evaluation under various noise levels. The results of the subjective tests show that the system is able to convert normal speech into Lombard speech and that there is a trade-off between quality and Lombardness of the mapped utterances. The GlottDNN and PML stand out as the best vocoders in terms of quality and Lombardness, respectively, whereas the DNN is the best mapping method in terms of Lombardness. PML with the standard GMM seems to give a good compromise between the two attributes. The SIIB experiments indicate that intelligibility of converted speech compared to that of normal speech improved in noisy conditions most effectively when DNN mapping was used with STRAIGHT and PML.
CONSTRUCCIÓN DEL PLANO DE ENTORNOS DE TRABAJO CERRADOS UTILIZANDO EL ROBOT KOALA

Resumen En el presente trabajo de investigacion se analizo, diseno e implemento un control via software adaptado especificamente para un robot movil de ruedas (RMR): el robot Koala de la empresa K-Team, el cual sirvio como plataforma experimental para manipular –inicialmente- e incrementar posteriormente de forma paulatina y definitiva la autonomia del robot. El software de control inicia con el analisis y documentacion de la estructura fisica y logica del Koala, su comportamiento, capacidades y limitantes. Se analizo la cinematica del RMR, propiedades de componentes y dispositivos de comunicacion como sensores y puertos. En la etapa de diseno, se trabajo con la informacion alimentada del exterior por el robot, estructurando la misma en: las decisiones, reconocimiento del entorno (obstaculos, espacios), estrategias de referencia, ubicacion y desplazamiento, tiempos y movimientos entre otros, que dieron forma en gran medida al control y autonomia del robot. Se implemento en lenguaje C/C++ Borland. Palabras clave : Robots moviles, trayectorias, cinematico, sensores. CONSTRUCTION OF THE PLAN OF CLOSED WORK ENVIRONMENTS USING THE KOALA ROBOT Abstract In the folllowing research it was analyzed, designed and implemented a control by software specifically adapted to a movil robot of wheels (RMR): the Koala robot of the K-Team enterprice, which was utilized as experimental platform to manipulate -to beginning- and eventually increase of a paulatine form the robot autonomy. The control software starts with the analize and documentation of physical structure and logical of the Koala, it behaviour, capacities and limitations. It was analized the RMR kinematics, properties of components and comunication devices such as ports and sensors. In the design stage, it was worked with the obtained information of outdoors by the robot, structuring the same in: the decisions, recognition of the environment (obstacles, outdoors), referency strategies, location and displacement, times and movements between others,that made it possible mainly to the control and robot autonomy. It was implemented through C/C## Borland language. Keywords: Mobile robots, Paths, Kinematics, Sensors.
Music for seeing – visualization of sounds

This article introduces one actually developed multimedia application, which can be a good tool for people with hearing disability. The aim of the multimedia solution is to provide software for people with hearing disability what can visualize the audio information – such as music – in a way what can trigger nearly the same stimulus that can be felt by the people without any hearing problems. With the using of cognitive perceptions' aspect, the development focuses on the human - ICT communication, and focuses on the possibilities given by cognitive infocommunication. The manuscript summarizes the solution which based on the senses of audible sounds and visible colors and introduces the method which combines this perceptible basis with the quantities typical of physical waves.
Agent Based Modeling of a Flange Climb Derailment

We report on the development of an Agent Based Model of a train derailment incident, considering a number of factors including friction and flange angle. We describe the background and objectives, and use the Rushall derailment as a Case Study. We use the NetLogo modeling environment to build our model. We describe the workings of the model. Two scenarios involving frequency of maintenance are tested using the model. We observe unexpected (emergent) results in one case.
Deep Learning Class Discrimination Based on Prior Probability for Human Activity Recognition

Convolutional neural network (CNN) depicts the transformation of the input image through a series of convolutions and other non-linear phases for recognition and classification purposes. This method has gained popularity for its significant contributions to computer vision applications and the improvement of the state-of-the-art. With CNNs, the softmax loss is used as the traditional loss function. This loss function allows deep features of distinct classes to be separated and promote the effective training of deep neural networks. An improvement on CNNs’ discriminative power for face recognition was recently reported, where softmax and center loss were jointly used as supervisory a loss signal. In this paper, it is shown that such a supervisory loss function is not optimal in human activity recognition, and hence a new likelihood regularization term aimed at improving the feature discriminative power of the CNN models. This regularization term is modeled from Bayesian distribution for the posterior estimation of class probability density. The regularization term is shown to improve different class discrimination, and it is capable of maximizing the distance between different classes and minimizing distances within the same class in human activity recognition. The results obtained on the KTH and Weizmann datasets were encouraging.
PD-SOI Enabling Adaptive RF Front-End Modules

Partially depleted silicon-on-insulator (PD-SOI) is a key enabling technology for adaptively tuned RF front-end modules (FEM). Unlike bulk CMOS, thanks to SOI's insulating buried-oxide (BOX) layer, switched capacitor banks can allow for large voltage swing and exhibit a high small-signal linearity. This in turn enables LTE standard-proof power and linearity capabilities for tunable duplexers based on electrical-balance duplexers (EBDs) and N-path filters. These building blocks help to reduce the amount of SAW filters typically required in LTE handsets and enable in-band full-duplex (IBFD). This paper presents an overview of work related to tunable FEM in 180nm PD-SOI technology.
An Efficient FPGA-based Overlay Inference Architecture for Fully Connected DNNs

Deep Neural Networks (DNNs) have gained significant popularity in several classification and regression applications. The massive computation and memory requirements of DNNs pose special challenges for FPGA implementation. Moreover, programming FPGAs requires hardware-specific knowledge that many machine-learning researchers do not possess. To make the power and versatility of FPGAs available to a wider DNN user community and to improve DNN design efficiency, we introduce a Single hidden layer Neural Network (SNN) multiplication-free overlay architecture with fully connected DNN-level performance. This FPGA inference overlay can be used for applications that are normally solved with fully connected DNNs. The overlay avoids the time needed to synthesize, place, route and regenerate a new bitstream when the application changes. The SNN overlay inputs and activations are quantized to power-of-two values, which allows utilizing shift units instead of multipliers. Since the overlay is a SNN, we fill the FPGA chip with the maximum possible number of neurons that can work in parallel in the hidden layer. On a ZYNQ-7000 ZC706 FPGA, it is thus possible to implement 2450 neurons in the hidden layer and 30 neurons in the output layer. We evaluate the proposed architecture on typical benchmark datasets and demonstrate higher throughput with respect to the state-of-the-art while achieving the same accuracy.
Angular Domain PAPR Reduction for Massive Multi-user MIMO-OFDM Downlink

This paper proposes a novel angular domain peak-to-average power ratio (PAPR) reduction technique in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Constant envelope (CE) precoding, OFDM modulation, and PAPR reduction are jointly implemented by solving a optimization problems in multi-beam (i.e., angle division multiple access, ADMA) systems. The proposed simplified iterative discrete estimation (SIDE) precoding scheme exploits the characteristics of massive MIMO-OFDM-ADMA system model and efficiently decrease PAPR effect with relatively lower computation complexity. Numerical results demonstrate that the proposed solution can achieve nearly optimal PAPR reduction performance, and scarcely any symbol error ratio (SER) performance loss.
Capacitively Coupled Resonator Models for Investigating Spatial Impedance Variation in WPT Systems

An analysis of a capacitive wireless power transfer system is presented to investigate the relationship between spatial impedance variation between the transmitter and receiver. The capacitively coupled link is modeled by an equivalent circuit that includes all coupling paths between the four conductors which make up the link. The analysis shows that a low Q resonator circuit is desirable to minimize impedance changes from spatial variation. On the other hand, the presence of crosslink capacitances can increase the Q which can be obtained after matching the circuit. Therefore cross-link capacitances should be minimized for spatially robust systems. Experimental results for ISM band frequencies of 13.56 MHz, 27.12 MHz, and 40.68 MHz are shown to verify the analysis.
Big Data Summarization Using Submodular Functions

Data summarization, a central challenge in machine learning, is the task of finding a representative subset of manageable size out of a large dataset. It has found numerous applications, including image summarization, document and corpus summarization, recommender systems, and non-parametric learning, to name a few. A general recipe to obtain a faithful summary is to turn the problem into selecting a subset of data elements optimizing a utility function that quantifies “representativeness” of the selected set. Often times, the choice of utility functions used for summarization exhibit submodularity, a natural diminishing returns property. In words, submodularity implies that the added value of any element from the dataset decreases as we include more data points to the summary. Thus, the data summarization problem can be naturally reduced to that of a constrained submodular maximization, or a submodular cover problem. Although, there are efficient centralized algorithms for the aforementioned problems, they are highly impractical for massive datasets, as sequentially selecting elements on a single machine is heavily constrained in terms of speed and memory. Hence, in order to solve the above submodular optimization problems at scale, we need to make use of MapReduce-style parallel computation models, or resort to streaming algorithms. In this Thesis, we develop large scale algorithms for submodular summarization. In particular, we present a simple, parallel protocol, called GreeD i for distributed (notnecessarily monotone) submodular maximization subject to cardinality, and other general types of constraints, including matroid and knapsack constraints. In addition, we develop a distributed algorithm, D isCover, for the submodular cover problem, as well as a fast distributed algorithm, FastCover, that enables us to solve the more general problem of covering multiple submodular functions in one run of the algorithm. We then consider the streaming setting, where at any point of time, the algorithm
Resource Management Challenges in the Next Generation Cloud based Systems: A Survey and Research Directions

Traditional resource management techniques for the conventional cloud computing systems will not be able to meet the QoS required for the IoT connectivity applications, big data analytics applications, and cognitive computing applications. Toward the next generation edge clouds, this work analyzes the dissimilarities of resource management issues between conventional cloud computing and edge cloud computing systems. In addition, we provide a classification and a summary for the resource management challenges of the next generation cloud based systems. Finally, the future research directions are highlighted.
A GRASP-Genetic Metaheuristic Applied on Multi-Processor Task Scheduling Systems

In the last decades, parallel processing systems are used in most applications. In these systems, task scheduling problems are considered as an important issue in managing multiprocessors. The challenge in task scheduling problems is to get the best schedule that achieves the best efficiency and the minimum make span. In this paper, a new hybrid metaheuristic algorithm called GRASP-GA is proposed to handle such problems. In the proposed algorithm, the greedy randomized adaptive search procedure is adopted to construct a population of high-quality solutions. Then, the genetic algorithm is applied on the constructed population to improve these solutions. Two heuristic functions are adopted to guide GRASP, bottom-level and top-level. The proposed GRASP-GA is verified against a set of the state-of-the-art algorithms using some test problems that considered as benchmarks. The experimental results indicated the superiority of the proposed GRASP-GA over all the tested algorithms. Since, it can achieve the best performance in all test problems used in this experiment. These results ensure the GRASP-GA is a good competitor and can be considered as a viable alternative.
Integrating Multiple Inferences for Vehicle Detection by Focusing on Challenging Test Sets

Due to recent advances in object detection with the help of deep convolutional neural networks and region proposal methods, object detection systems have become practical in numerous fields with high accuracy. This paper presents a method for vehicle detection in videos for automatic traffic monitoring. Compared to general object detection datasets such as the PascalVOC and MS-COCO, traffic surveillance datasets such as the UA-DETRAC dataset have different challenging issues: high variation of object size, severe occlusion, dissimilarity between training set and test set. To overcome these difficulties, we employ an unsupervised integration of multiple instances of an image by analyzing video sequences. We applied Faster R-CNN with Neural Architecture Search (NAS) framework as a base network. We achieved 85.76% mAP on the UA-DETRAC detection test set, and outperformed the winner method of the AVSS 2017 challenge on Advanced Traffic Monitoring by 9.19%.
Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting

In stock forecasting, the main concern is the accuracy of the stock market prediction. Various methods, techniques and algorithms have been proposed to achieve higher prediction accuracy. One of the popular techniques is Artificial Neural Network (ANN). ANN is a bio-inspired algorithm that tries to simulate the brain’s mechanism which is achieved through simulating a connected artificial neuron network. Although ANN has shown promising results in forecasting, researches have shown that combining multiple neural networks can further improve the forecasting result. The aim of this paper is to compare the performance of a single and multiple homogeneous ANN in forecasting CIMB stock market closing price. The empirical result shows that homogeneous ensemble ANN performs better than a single ANN in predicting the stock market price.
Backtracking Algorithm based Multi-Relay Selection in High Altitude Platforms System

In High Altitude Platforms (HAPs) system, rain fading is a burning problem needed to be settled. Because of the limited transmitting power and insufficient surface area of the aircraft, HAPs is unable to apply traditional rain attenuation mitigation technologies. In this paper, we propose a backtracking algorithm based multi-relay selection scheme (BA-MRS) to solve this problem. In the BA-MRS scheme, we use cooperative diversity technology to provide additional diversity gain, and utilize beamforming technology to enhance the antenna gain and suppress the interference between users. So as to speed up the search process, we set a selection criterion for the backtracking algorithm, which can reduce the size of the search space efficiently. Simulation results show that the proposed scheme can achieve a near-optimal performance with a lower complexity.
Indoor Navigation With Virtual Graph Representation: Exploiting Peak Intensities of Unmodulated Luminaries

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.
Digital Design using CMOS and Hybrid CMOS/Memristor Gates: A Comparative Study

Memristor is a passive element with two terminals, where the magnetic flux is related to the amount of the electric charge passed through the device. Memristive technologies are valuable as they are scalable, non-volatile and compatible with CMOS. In this paper, AND, OR and XOR logic gates which are the seed of any digital design are simulated using memristor, the used memristor model is Voltage ThrEshold Adaptive Memristor (VTEAM), to compare between memristor-based designs and the known CMOS-based logic gates knowing that CMOS technology used is TSMC 65 nm. A few parameters such as propagation delay, power consumption, number of devices used in each circuit have been calculated and studied in both approaches. The proposed logic shows lower power consumption and better area utilization compared to 65 nm CMOS logic circuits operating at standard 1.2 V. Our simulations were run using Cadence Virtuoso IC6.1. 4 simulator.