Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiplierless Filtered-OFDM Transmitter for Narrowband IoT Devices

In cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM)-based radio access, the coexistence of different technologies without precise time-frequency synchronization is limited due to high out-of-band (OOB) emissions. Therefore, the spectrum enhancement techniques play a key role in relaxing the synchronization and power control requirements. This allows a higher degree of opportunistic spectrum use with minimized interference. In addition, all the transmitting devices have to fulfill specific transmitted signal quality requirements, including the maximum OOB radiated signal power. With the orthogonal frequency-division multiplexing (OFDM)-based radio access, some additional signal processing for improved spectrum containment is commonly needed to achieve these requirements. The filtering and time-domain windowing are two fundamentally different approaches for spectrum enhancement. The filtered OFDM (F-OFDM) provides better spectrum localization than the time-windowing schemes [such as windowed overlap-add (WOLA)], with the cost of higher complexity. This article introduces low-complexity solutions for spectrally enhanced narrowband OFDM transmitters based on the use of lookup tables (LUTs). The proposed LUT approach, requiring only memory units and a low number of additions, allows to avoid all computationally expensive operations in online transmitter processing, as it builds the transmitted signal by summing the stored partial waveforms optimized offline. In certain cases, completely multiplication- and summation-free designs are possible. The transmitters of narrowband Internet of Things (NB-IoT) devices are natural applications for the proposed LUT approach, as they require additional digital baseband signal processing to reach the emission requirements. It is shown that the proposed LUT schemes can provide significant savings in real-time computations of NB-IoT devices, while fulfilling the 3GPP requirements.
The Effectiveness of Concurrent Design on the Cost and Schedule Performance of Defense Weapons System Acquisitions

This study investigates the impact of concurrent design on the cost growth and schedule growth of US Department of Defense Major Defense Acquisition Systems (MDAPs). It is motivated by the question of whether employment of concurrent design in the development of a major weapon system will produce better results in terms of cost and schedule than traditional serial development methods. Selected Acquisition Reports were used to determine the cost and schedule growth of MDAPs as well as the degree of concurrency employed. Two simple linear regression analyses were used to determine the degree to which cost growth and schedule growth vary with concurrency. The results were somewhat surprising in that for major weapon systems the utilization of concurrency as it was implemented in the programs under study was shown to have no effect on cost performance, and that performance to development schedule, one of the purported benefits of concurrency, was actually shown to deteriorate with increases in concurrency. These results, while not an indictment of the concept of concurrency, indicate that better practices and methods are needed in the implementation of concurrency in major weapon systems. The findings are instructive to stakeholders in the weapons acquisition process in their consideration of whether and how to employ concurrent design strategies in their planning of new weapons acquisition programs.
Learning Assisted System Using Computer Assisted Instruction (CAI)

The Unified Modeling Language (UML) is a language that helps to visualize, design and document models of software systems. Students are encouraged to work in groups to learn the terminology, notation and use Unified Modeling Language and by so doing learn the task of communicating with clients and with other members of the project team. This effectively leads to the students’ understanding of main factors associated in the project’s success or failure and complexities involved in software development and project management. So, this system presents an innovative approach to teaching the Unified Modeling Language concepts which provide practical hands-on experience for the students to learn, understand and appreciate the concepts of Unified Modeling Language. This system is implemented by Microsoft Visual Studio 2008 C#. Net and Microsoft Access Database.
El lupus eritematoso en la consulta de dermatología

Summary Lupus erythematosus (LE) is a sistemic disease with cutaneous manifestations in more than 80% of the patients. The aim of the study is to know the incidence of manifestations at oral mucosa and scalp, which are frequently unnoticed to the physician and the patient. Moreover, we evaluated the efficacy of topical immunomodulators in all the types of LE. Sixty three patients diagnosed of LE who came to a consultation at the Department of Dermatology at the Virgen Macarena Universitary Hospital from september to december of 2005 were reviewed. We included the systemic, subacute and discoid forms of LE. Demographic data, clinical types, location of the lesions, photoexposition, serology, histopathology and treatment followed by the patients were considerated. We observed a high proportion of lesions at the oral mucosa and scalp. Finally, we obtained good therapeutical results using topical immunomodulators in all clinical types of lupus.
Modeling Brain Diverse and Complex Hemodynamic Response Patterns via Deep Recurrent Autoencoder

For decades, a variety of task-based functional MRI (tfMRI) data analysis approaches have been developed, including the general linear model (GLM), sparse representations, and independent component analysis (ICA). However, these methods are mainly shallow models and are limited in faithfully modeling the complex, diverse, and concurrent spatial–temporal functional brain activities. Recently, recurrent neural networks (RNNs) have demonstrated great superiority in modeling temporal dependency of signals, while autoencoder models have been proven to be effective in automatically estimating the optimal representations of the original data. These characteristics of RNNs and autoencoders naturally meet the requirement of modeling hemodynamic response patterns in tfMRI data. Thus, we propose a novel unsupervised framework of deep recurrent autoencoder (DRAE) for modeling hemodynamic response patterns in this article. The basic idea of the DRAE model is to combine the deep RNN and the autoencoder to automatically characterize the meaningful functional brain networks and corresponding diverse and complex hemodynamic response patterns simultaneously. The experimental results demonstrate the superiority of the proposed DRAE model in automatically estimating the diverse and complex hemodynamic response patterns.
A Sub-nW 32-kHz Crystal Oscillator Architecture Based on a DC-Only Sustaining Amplifier

This article introduces a novel oscillator architecture that enables sub-nW power consumption in a 32-kHz crystal oscillator (XO) by amplifying the oscillation signal at dc, instead of the oscillation frequency. The impact of circuit nonidealities on the oscillator’s performance is analyzed. Measurement results of 20 different 65-nm standard CMOS dies show an average power consumption of 0.55 nW drawn from a 0.5-V supply at room temperature for a 32-kHz XO. The measured long-term stability of the XO is 14 ppb. The proposed oscillator architecture does not require any calibration schemes or multiple supply domains.
The Hybrid Technique for Image Classification to Detect Gender and Age using ANN and SVM

: These days look into has investigated to separating helper data from different biometric procedures, for example, fingerprints, confront, iris, palm, voice and so forth. This data contains a few highlights like sexual orientation, age, whiskers, mustache, scars, stature, hair, skin shading, glasses, weight, facial imprints, tattoos and so forth. This data contributes increasingly to ID. The real changes that go over face acknowledgment are to discover age and sexual orientation of the individual. This paper contributes a huge overview of different face acknowledgment systems for finding the age and sexual orientation. The current systems are talked about in light of their exhibitions. Especially, the programmed estimation of age and sexual orientation from confront pictures can be helpful in an extensive variety of scientific applications. The portrayals got from various face locales are expected to Support Vector Machine (SVM) classifiers to evaluate the age and sexual orientation of the individual in the picture. Test investigation demonstrate the great after effects of this component based approach, and its extra advantages when confront pictures are influenced by impediments. As of late, profound neural systems have shown magnificent exhibitions in perceiving the age and sexual orientation on human face pictures. We show an examination exploring these distinctive impacts. In detail, our work looks at four prevalent neural system structures, examines the impact of retraining, assesses the vigor of the considered arrangement pre-handling by means of cross-technique test set swapping and instinctively imagines the model's forecast procedures in given preprocessing conditions utilizing the current Layer-wise Relevance Propagation (LRP) calculation. Our assessments on the testing Audience benchmark demonstrate that reasonable parameter introduction prompts a comprehensive impression of the info, repaying workmanship genuine information portrayals. With a mix of straightforward preprocessing steps, we achieve best in class execution in sex acknowledgment.
Content Caching in Industrial IoT: Security and Energy Considerations

In Industrial Internet of Things (IIoT), the volume of generated traffic can be high, causing concerns over issues, such as high energy consumption and longer access delay. Caching IIoT content at the network nodes is a promising solution to address these concerns. However, cached IIoT content at the network nodes is vulnerable to malicious attacks, which can cause significant disruptions in industrial operations. In order to monitor and prevent such malicious activities, trusted systems/nodes with supervisory control can be placed at the network nodes. In this article, we introduce the concept of the trusted caching node (TCN) for IIoT. TCNs cache popular IIoT content and ensure robust security to the connected links. This article finds the optimum location for the TCNs and selects secured-routes for content distribution. We formulate a joint optimization problem and minimize the cost related to probable content breach and energy consumption. We also design a low-complexity heuristic algorithm to solve this problem in real-time. The simulation results demonstrate that our proposed solution achieves significant performance gain and ensures secured IIoT content delivery.
Application of Blended Teaching in Hydraulic Pneumatic Transmission and Control Course Based on MOOC and FanYa Platform

— The hydraulic pneumatic transmission and control course has the characteristics with difficult theory and complicated application in the teaching process. How to change the problems of traditional classroom teaching such as dull, monotonous, boring, relatively fixed teaching time and place, rigid learning methods, low learning efficiency, etc., the MOOC and SMOOC technology were studied. The FanYa platform was adopted to realize blended teaching. The teaching team was organized before class, and the curriculum syllabus was developed according to the talent training program, and the teaching program, teaching plan, teaching PPT, animation production and micro-lesson video shooting were completed. The FanYa platform was used to complete pre-class preview, preview effect tracking, classroom communication and discussion and homework. Through the pilot program in the last three years, the students can make full use of fragmented time and learn at any time and anywhere by adopting blended teaching method. Teachers can greatly improve teaching efficiency. This work summarized the material block diagram and execution process of blended teaching, and realized the four-in-one blended teaching in the traditional hydraulic pneumatic transmission and control course, including the combination of theory and practice, pre-class and after-class, online and offline, traditional classroom and network classroom.
Knowledge, Awareness and Practices (KAP) among patients with hypothyroidism attending endocrine clinics of community hospitals in Chennai

Primary hypothyroidism is an extremely common disorder encountered in every endocrine clinic. We found that patients who have been on treatment and follow up elsewhere for many years lacked a basic understanding of their disorder and held wrong beliefs regarding the same. We felt a study on the knowledge, awareness and practices of patients with hypothyroidism would help the physician to focus on specific issues during patient education, clarify commonly held mis-beliefs during their consultation and thus enhance patient compliance and follow up. So we decided to do a KAP (Knowledge, Awareness and Practices) study on patients with primary hypothyroidism. A search carried out on Pubmed andMedscape did not reveal previous similar studies in medical literature.
Geobia Achievements and Spatial Opportunities in the Era of Big Earth Observation Data

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured CV system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena.
Reliability Aware Energy Optimized Scheduling of Non-Preemptive Periodic Real-Time Tasks on Heterogeneous Multiprocessor System

Higher reliability and lower energy consumption are conflicting, yet among the most important design objectives for the real-time systems. Moreover, in the domain of real-time systems, non-preemptive scheduling is relatively unexplored with objectives such as reliability and energy. Thus we propose an active replication based framework to schedule a set of periodic real-time tasks in the non-preemptive heterogeneous environment such that the given reliability and timing constraints are satisfied whereas the energy consumption is minimized. First, we formulate the problem as a constraint optimization problem that provides an optimal solution; however, it does not scale well. Thus, we also propose heuristics which apply reservation of processors and reallocation of jobs, to compute suboptimal solution efficiently in terms of energy consumption as well as schedulability. Heuristics make use of the interplay of task-level reliability target, reliability of replicas, number of replicas, reliability of tasks, and energy consumption. We perform an experimental study on the test cases generated by extending UUnisort algorithm [1] and observe the effect of various simulation parameters on energy consumption and schedulability.
Optimum Energy Allocation for a Random Access Packet With Message Bits

In this paper, performance is investigated for a random access packet composed of a preamble and message. To minimize the error probability of a packet with channel-adaptive random access, the optimum energy allocation is obtained between the preamble and the message. The performances with the optimum and sub-optimum energy allocations are compared by simulation.
End-to-End Network Slices: From Network Function Profiles to Fine-Grained SLAs

Towards end-to-end network slicing, diverse envisioned 5G services (eg, augmented reality, vehicular communications, IoT) Call for advanced multi-administrative domain service deployments, open challenges from vertical Agreement (SLA) -based orchestration hazards. Through different proposed methodologies and demonstrated prototypes, this work showcases: the automated extraction of network function profiles; the manners to analyze how such profiles compose programmable network slice footprints; and the means to perform fine-grained auditable SLAs for end-to-end network slicing among multiple administrative domains. Sustained on state-of-the-art networking concepts, this work presents contributions by detecting roots on standardization efforts and best-of-breed open source embodiments, each one standing prominent future work topics in shape of its shortcomings.
Low Latency Floating-Point Division and Square Root Unit

Digit-recurrence algorithms are widely used in actual microprocessors to compute floating-point division and square root. These iterative algorithms present a good trade-off in terms of performance, area and power. We present a floating-point division and square root unit, which implements a radix-64 floating-point division and a radix-16 floating-point square root. To have an affordable implementation, each radix-64 division iteration and radix-16 square root iteration are made of simpler radix-4 iterations: 3 radix-4 iterations in division and 2 in square root. Speculation is used between consecutive radix-4 iterations to get a reduced timing. There are three different parts in digit-recurrence implementations: initialization, digit iterations, and rounding. The digit iteration is the iterative part and it uses the same logic for several cycles. Division and square root share partially the initialization and rounding stages, whereas each one has different logic for the digit iterations. The result is a low-latency floating-point divider and square root, requiring 11, 6, and 4 cycles for double, single and half-precision division with normalized operands and result, and 15, 8 and 5 cycles for square root. One or two additional cycles are needed in case of subnormal operand(s) or result.
A Compiler Approach to Map Algebra for Raster Spatial Modeling

Modeling and simulation enables the study of spatial phenomena that are otherwise impossible to reproduce in the physical world. On the one hand, digital models replicate the shape and state of the Earth with bits and bytes that we can store and manipulate. On the other, computer models turn mathematical equations into instructions that computers can run to simulate events of interest. Together, they provide a digital laboratory where to virtually isolate and experiment with spatial phenomena. Recently, advances in remote sensing technologies have brought unprecedented volumes of spatio-temporal data. Simultaneously, computer architectures have developed into parallel and heterogeneous designs. More data enables better models, but also entails more calculations, which in turn requires faster computers. Parallel computers promise higher performance, but their complex programming impacts both productivity and portability. This raises a conflict between the execution speed, ease of development and portability of computer models. However, these three qualities are necessary for an efficient practice of modeling and simulation. In this thesis we propose a compiler approach to map algebra that provides a productive, performant and portable environment where to model spatial phenomena and simulate their dynamics. Modelers write sequential scripts in the map algebra formalism, a language for raster spatial analysis. Then the compiler parses their scripts to generate parallel codes that run efficiently on modern heterogeneous architectures. Moreover, scripts are written once and automatically translated to the target computer architecture, attaining portability. To test our hypothesis, we build a prototype compiler and evaluate typical map algebra workloads. Our experiments produced three clear outcomes: (1) the compiler approach can handle large volumes of data even when they exceed the computer memory, (2) the performance is competitive as it provides large speed-ups over interpreted map algebras, and (3) the compilation process is fully transparent to modelers and therefore user-friendly. In conclusion, a map algebra compiler meets the necessary qualities for a practical modeling and simulation of spatial phenomena with raster data, and as a result it becomes a valuable tool for modelers.
Understanding and Countermeasures against IoT Physical Side Channel Leakage

With the proliferation of cheap bulk SSD storage and better batteries in the last few years we are experiencing an explosion in the number of Internet of Things (IoT) devices flooding the market, smartphone connected point-of-sale devices (e.g. Square), home monitoring devices (e.g. NEST), fitness monitoring devices (e.g. Fitbit), and smartwatches. With new IoT devices come new security threats that have yet to be adequately evaluated. We propose μLeech, a new embedded trusted platform module for next-generation power scavenging devices. Such power scavenging devices are already widely deployed. For instance, the Square point-of-sale reader uses the microphone/speaker interface of a smartphone for communications and as a power supply. Such devices are being used as trusted devices in security-critical applications, without having been adequately evaluated. μLeech can securely store keys and provide cryptographic services to any connected smartphone. Our design also facilitates physical side-channel security analysis by providing interfaces to facilitate the acquisition of power traces and clock manipulation attacks. Thus μLeech empowers security researchers to analyze leakage in next-generation embedded and IoT devices and to evaluate countermeasures before deployment. Even the most secure systems reveal their secrets through secret-dependent computation. Secret-dependent computation is detectable by monitoring a system’s time, power, or outputs. Common defenses to side-channel emanations include adding noise to the channel or making algorithmic changes to mitigate specific side-channels. Unfortunately, existing solutions are not automatic, not comprehensive, or not practical. We propose an isolation-based approach for eliminating power and timing side-channels that is automatic, comprehensive, and practical. Our approach eliminates side-channels by leveraging integrated decoupling capacitors to electrically isolate trusted computation from the adversary. Software has the ability to request a fixed-power/time quantum of