Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Wideband Self-Interference Cancellation Techniques for FDD and Full-Duplex Wireless Communication

The continued demand for higher levels of wireless access and increased data rates for a variety of applications from mobile smart phones to back haul point-to-point communication, continues to drive research that enables new spectrum opportunities, reduces form factor and lowers the cost of hardware solutions. The current RF spectrum, which is often referred to as the frequency band from 1-6GHz, has become increasingly crowded with only a limited amount of unused and unlicensed spectrum. This dissertation explores and implements, single-chip hardware front-end solutions which the specific aim to increase data rates for each single-user using two techniques: a. Using In-band full-duplex radio techniques with self-interference cancellation; b. High-speed communication using large bandwidths available at mmWave frequencies. The advantages, challenges and achievements associated with the two proposed techniques will be described in the following paragraphs. First, in-band full-duplex communication potentially increases spectral efficiency within existing RF standards. This will allow the combination of dedicated transmitting and receiving bands into a single band which would more than double the spectral efficiency. However, this leads to an extremely challenging problem associated with transmitter self-interference cancellation. To date, two full-duplex chips have been designed, fabricated and tested, each with a self-interference cancellation function. The first IC is an analog full-duplex front-end for Bluetooth (BLE) applications. Inside the proposed chip, a self-interference cancellation (SIC) circuitry, a low-power receiver, combined with a harmonic-rejection power amplifier (HRPA) are implemented to reduce the transmitter-to-receiver self-interference, and enable full-duplex operation. These techniques were applied towards the realization of a prototype silicon device which implements a tunable self-interference mitigation canceller function with a current-mode LNA and passive-mixer based front-end, and a power amplifier topology to reduce out-of-band emissions. This chip was fabricated in 40nm 6-metal stack CMOS process to achieve more than 30dB measured self-interference cancellation over 4MHz bandwidth, and an integrated power amplifier (PA) which suppresses the 3rd and 5th harmonics by 30dB and 15dB, respectively. The PA delivers a maximum output power of +14dBm with a drain efficiency of 33%. The self-interference cancellation circuitry utilizes an active area of 131×112.5 µm2, has a power consumption of 0.25mW, and degrades the receiver noise figure (NF) by less than 0.6dB. The second IC is a transceiver front-end, which includes a dual-injection path self-interference (SI) cancellation circuitry to enable wideband full-duplex communication with a high-power transmitter. The proposed SI cancellation circuitry is implemented using: (1) one feedforward cancellation path containing a 5-tap analog adaptive filter (AF) between the transmitter (TX) output and the receiver (RX) input; (2) a second cancellation…
La lectura y la escritura académica en educación superior: el taller como estrategia didáctica

Este artículo recoge una propuesta didáctica de intervención en el aula universitaria centrada en la escritura y la lectura académica, puesto que el Espacio Europeo de Educación Superior (EEES) y la implantación del Plan Bolonia han supuesto un incremento de las horas prácticas y la implantación de un trabajo final de grado para poder graduarse. Por tanto, en este artículo, ofrecemos una propuesta de intervención didáctica basada en el modelo pedagógico de taller por su carácter reflexivo-experimental. En dicho taller nos centramos en las destrezas de comprensión lectora y expresión escrita centradas en los textos científicos académicos: atender a la especificidad de estos textos, el tipo de lenguaje estereotipado y convencional, la estructura de un trabajo académico, la citación, entre otros aspectos. Todas estas características discursivas suponen un desafío para los estudiantes de nuevo ingreso en la educación superior que ha de manejar tanto para realizar sus estudios como para dotarse de las competencias profesionales que requiere sus estudios para su ingreso laboral. En el artículo, además, ofrecemos el análisis de los datos relativos a la intervención didáctica basándonos en un modelo metodológico de investigación-acción.
Communication and Agreement Abstractions in the Presence of Byzantine Processes

Byzantine process is a process that --intentionally or not-- behaves arbitrarily (Byzantine failures include crash and omission failures). Considering message-passing systems, this paper presents communication and agreement abstractions that allow non-faulty processes to correctly cooperate, despite the uncertainty created by the net effect of asynchrony and Byzantine failures. The world is distributed. Consequently more and more applications are distributed, and the ''no Byzantine failure'' assumption is no longer reasonable. Hence, due to both the development of clouds and security requirements, such abstractions are becoming more and more important. The aim of this paper is to be a simple and homogeneous introduction to (a) communication and agreement abstractions, and (b) algorithms that implement these abstractions, in the context of asynchronous distributed message-passing systems where an a priori unknown subset of processes may exhibit Byzantine failures. To that end the paper presents existing abstractions and algorithms, and new ones. In this sense the paper has a mixed ''pedagogical/survey/research'' flavor.
Guaranteeing Reactive Missions for Complex Robotic Systems

ions and switching protocol synthesis for reactive tasks. Tools for automatically synthesizing controllers based on high-level specifications written over task-oriented abstractions of nonlinear systems have been introduced recently in [20]. When there does not exist a controller that is guaranteed to satisfy a specification under the worst-case behaviors of the environment, i.e. it is unrealizable, the task of debugging the specification can be difficult and may be worsened with the existence of a discrete abstraction. For instance, if a manipulator tasked with fetching parts on a conveyor belt cannot reach for fast-moving parts, the designer must revise the specification with additional statements that consider both the physics of the manipulator and the underlying assumptions on the uncontrolled environment (in this case, the motion of the parts). To assist the designer, automated frameworks have been introduced recently for debugging unrealizable specifications [49, 73]. Further work has shown progress toward automated repair of unrealizable specifications through the synthesis of revisions to such specifications [5, 34, 55]. In this chapter, we address the problem of realizability of specifications caused by the dynamics of a system by introducing a framework that automatically suggests additions to such specifications and provides them to the user in a clear, understandable manner. We focus on systems involving physical motion, encompassing tasks carried out by mobile robots, land or air vehicles, or industrial process machinery, to name a few. We adopt an iterative procedure that uses the discrete abstraction of the physical process to assist in interactively computing revisions to the specification. Using a graphical visualization tool, the user may accept or deny the revisions at each step. The goal is to give the
Combination of Rivest-Shamir-Adleman Algorithm and End of File Method for Data Security

Data security is one of the crucial issues in the delivery of information. One of the ways which used to secure the data is by encoding it into something else that is not comprehensible by human beings by using some crypto graphical techniques. The Rivest-Shamir-Adleman (RSA) cryptographic algorithm has been proven robust to secure messages. Since this algorithm uses two different keys (i.e., public key and private key) at the time of encryption and decryption, it is classified as asymmetric cryptography algorithm. Steganography is a method that is used to secure a message by inserting the bits of the message into a larger media such as an image. One of the known steganography methods is End of File (EoF). In this research, the cipher text resulted from the RSA algorithm is compiled into an array form and appended to the end of the image. The result of the EoF is the image which has a line with black gradations under it. This line contains the secret message. This combination of cryptography and steganography in securing the message is expected to increase the security of the message, since the message encryption technique (RSA) is mixed with the data hiding technique (EoF).
Sentiment Mining from Text: A Technical Review

Expression of sentiments is an integral aspect of human communication. The prevalence of Internet and social media platforms has facilitated the exchange and storage of large volumes of human communication in the form of natural language text which can be mined for various intelligence-based applications. In order to build computers that can serve humans better, there are ongoing efforts in computer science research to develop machine learning algorithms that can process the textual data and perform sentiment mining tasks such as, ‘detecting the presence of emotion in text’, ‘selecting a model for representing emotion’, ‘classifying the sentiment polarity of text’ and ‘measuring the intensity of the expressed sentiment in text’. This paper aims to discuss the challenges posed to the various tasks associated with mining sentiment from text, review the existing methods to address these challenges in contemporary literature and identify important areas for future scope of research in this field.
Processing Electromyographic Signals for Use as Computer Input

With the advancement of technology, daily interaction with computers is being relied on more and more. However, conventional interaction methods remain difficult or impossible to use for people with certain physical disabilities. One potential solution is to capture functioning electromyographic (EMG) signals from the user’s muscles to be translated into computer input. We seek to show that EMG signal recognition is a viable alternative input method for people with certain physical disabilities. Modern signal processing is accurate enough for us to develop a proof of concept input method for both keyboard and mouse interaction. With further testing, a complete replacement for keyboard and mouse that relies only on the user’s EMG activity could be achieved.
Automatic indexing of comic page images for query by example based focused content retrieval

Automatic indexing of comic-page-image reposito- ries has evolved as an interesting research problem for graphics recognition research community. In this paper we present a sys- tem for automatically indexing the comic-page-images in order to achieve query by example (QBE) based focused content retrieval. In our system we represent the comic-page-images by attributed graphs and translate the problem of automatic indexing / QBE based focused content retrieval as a subgraph spotting problem. Our system uses an explicit graph embedding technique to embed the comic-page-image graphs into numeric feature vectors and then employs state-of-the-art machine learning tools for automatic indexing / QBE based focused content retrieval. Experimental results are presented for automatic indexing and QBE based focused content retrieval in a comic-page-image repository.
Medical case-based reasoning: A review of retrieving, matching and adaptation processes in recent systems

Case Based Reasoning (CBR) is an active research area and a well established method for developing decision support systems in the medical domain. CBR systems retrieve and reuse solutions from previously solved problems to solve new problems. This paper summarizes CBR applications for diagnosis and planning in the medical domain over the last five years, reviewing their retrieval, matching, and adaptation processes in particular.
CVSS Implementation Guidance

This Interagency Report provides guidance to individuals scoring vulnerabilities using the Common Vulnerability Scoring System (CVSS) Version 2.0 scoring metrics. CVSS defines a vulnerability as a bug, flaw, weakness, or exposure of an application, system device, or service that could lead to a failure of confidentiality, integrity, or availability. The guidance in this document is the result of applying the CVSS specification to over 50 000 vulnerabilities scored by analysts at the National Vulnerability Database (NVD). This document is intended to serve as an extension to the CVSS Version 2.0 specification, providing additional guidance for difficult and/or unique scoring situations. To assist vulnerability analysts, common keywords and phrases are identified and accompanied by suggested scores for particular types of software vulnerabilities. The report includes a collection of scored vulnerabilities from the NVD, alongside a justification for the provided score. Finally, this report contains a description of the NVD’s vulnerability scoring process.
Fuzzy and possibilistic clustering methods for computer vision

Uncertainty abounds in all aspects of computer vision. As a result, methods which explicitly model and manage that uncertainty have a better chance in producing meaningful results in such complex situations. Fuzzy set theory provides a framework to initially model the uncertainty in a vision problem, and many methods to exploit it in producing realistic results. One very important tool which has been used extensively in pattern recognition and computer vision is that of objective function based clustering. In this chapter, we will review classical and novel clustering methods as they apply to computer vision and show examples of their utility. In particular, we will focus on the use of clustering to detect and recognize regular boundaries of objects from images of edge magnitudes. The problem of fitting an unknown number of boundary curves to edge magnitude data is one of the major challenges of computer vision. We will show that fuzzy clustering can be readily adapted to the problem of curve detection, and that a new possibilistic clustering method introduced by the authors can produce significantly more accurate results than either crisp or fuzzy clustering in noisy situations.
An Adaptive Neuro Fuzzy Interference System for Feature Extraction of Hyperspectral Image

In this paper, a novel feature extraction method based on proposed for hyperspectral image classification. Hyperspectral images contain a large amount of data. Techniques are presented in this paper for visualizing important features contained in a hyperspectral data set. The major cause is that the size of training data set does not correspond to the increase of dimensionality of hyperspectral data. Actually, the problem of the “Finding minerals in hyper spectral images is too tough” emerges when a statistic-based classification method is applied to the hyperspectral data. It was discovered that the resulting image is heavily influenced by the choice of focus bands used for display. When averaging hyper spectral signatures, choosing the correct pixels makes a difference, and desirable results are not always obtained. It was discovered that a procedure for visualizing hyper spectral image data that uses the peaks of the spectral signatures of pixels of interest provides a promising method for visualization. Using wavelet coefficients and data from the hyperspectral bands produces noticeably different results, which suggests that wavelet analysis could provide a superior means for visualization in some instances when using bands does not provide acceptable results. The proposed Anfis (Adaptive neuro fuzzy interference system) method proves exceptional performance in terms of classification accuracy and computational efficiency.
Predictability effects during reading in the absence of parafoveal preview

ABSTRACT The predictability of upcoming words facilitates both spoken and written language comprehension. One interesting difference between these language modalities is that readers’ routinely have access to upcoming words in parafoveal vision while listeners must wait for each fleeting word from a speaker. Despite readers’ potential glimpse into the future, it is not clear if and how this bottom-up information aids top-down prediction. The current study manipulated the predictability of target words and their location on a line of text. Targets were located in the middle of the line (preview available) or as the first word on a new line (preview unavailable). This represents an innovative method for manipulating parafoveal preview which utilises return sweeps to deny access to parafoveal preview of target words without the use of invalid previews. The study is the first to demonstrate gaze duration word predictability effects in the absence of parafoveal preview.
Problem Solving Heuristics on Non-Routine Problems of College Students

In the K-12 curriculum, problem solving and critical thinking skills have been the center of the framework for Mathematics curriculum in order to develop lifelong learners. With this, problem solving in Mathematics is highly valued. This descriptive comparative study determined the level of problem solving heuristics on non-routine problems of college freshmen at Mindoro State College of Agriculture and Technology. Employing a self-structured problem solving test composed of five non-routine problems, result showed that most of the students were classified as apprentice in heuristics knowledge which means that the skills and strategies used in general have focus but with limited clarity. In procedural knowledge, most of the students were also classified as apprentice which means that generally, most of the students made partial use of appropriate procedures and were not precise in using mathematical terms, principles and procedures. In conceptual knowledge, most of the students were also classified as apprentice which means that the students can extract the essence of the problem but most of them were unable to use this information to solve the problem properly. However, problem solving skills of college students did not differ in terms of heuristics, procedural and conceptual knowledge. Furthermore, the study showed that the most commonly used heuristics are making a model or diagram, using a formula, eliminating, and considering a simpler case. Relevant to this, the study recommended that Mathematics faculty should develop effective measures to enhance problem solving skills of the students such as integration of heuristics in teaching Mathematics problem solving and more exposure to non-routine problems.
A line segment extraction algorithm using laser data based on seeded region growing

This article presents a novel line segment extraction algorithm using two-dimensional (2D) laser data, which is composed of four main procedures: seed-segment detection, region growing, overlap region processing, and endpoint generation. Different from existing approaches, the proposed algorithm borrows the idea of seeded region growing in the field of image processing, which is more efficient with more precise endpoints of the extracted line segments. Comparative experimental results with respect to the well-known Split-and-Merge algorithm are presented to show superior performance of the proposed approach in terms of efficiency, correctness, and precision, using real 2D data taken from our hallway and laboratory.
Scalable Job Startup and Inter-Node Communication in Multi-Core InfiniBand Clusters

Moores Law – frequency scaling and exploitation of Instruction Level Parallelism due to increasing transistor density no longer leads performance gains in modern systems due to limitations in power dissipation. This has led to an increased focus on deriving performance gains by taking advantage of Data Level Parallelism through parallel computing. Clusters – groups of commodity compute nodes connected via a modern interconnect have emerged as the top supercomputers in the World and the Message Passing Interface (MPI) has emerged as the de facto standard in parallel processing models on large clusters. Scientific and financial applications have ever increasing demands for compute cycles and the emergence of multi-core processors has driven an enormous growth in the cluster sizes in recent years. InfiniBand has emerged as a popular low-latency, high-bandwidth interconnect of choice in these large clusters. With cluster sizes continuing to scale, the scalability of MPI libraries and associated system support and resources such as the job launcher have been at the center of attention in the High Performance Computing (HPC) community. In this work we examine the current job launching mechanisms that have scalability problems on large scale clusters due to resource constraints as well as performance bottlenecks. We propose a Scalable and Extensible Launching Architecture for Clusters (ScELA) that scales to modern clusters such as the 64K processor TACC Ranger. ii We also examine the scalability constraints with point-to-point InfiniBand channels in MPI libraries. We use the eXtended Reliable Connection (XRC) transport available in recent InfiniBand adapters to design a scalable MPI communication channel with a smaller memory footprint. The designs proposed in this work are available in both MVAPICH and MVAPICH2 MPI libraries over InfiniBand, which are used by more than nine hundred organizations around the World.
The Optional Qualitative Assessment in Impairment Tests

International and U.S. accounting standards mandate a one-step quantitative impairment test for goodwill and other indefinite-lived intangibles, but U.S. standards allow an optional qualitative assessment before the quantitative test. This option is intended to reduce the complexity and costs of the quantitative test. We demonstrate that U.S. firms using this option have comparatively higher valuations and face higher expected costs for conducting quantitative tests. Next, using a difference-in-differences research design, we show that firms using this option have a marginally higher incidence of impairments, suggesting that the qualitative assessment does not systematically allow companies to avoid write-downs. Moreover, we do not find clear evidence that using this option decreases the timeliness of impairments or increases monitoring costs for auditors, regulators, and investors. Our study provides evidence about the consequences of a revised impairment approach and speaks to the broader issue of allowing unconditional options and qualitative judgments in financial reporting.