Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Defined Wireless Sensor Networks Mangement and Security Challenges: A Review

Software defined networking (SDN) is a paradigm developed to cope with inherent limitations posed by the lack of flexibility in the traditional networking architecture like the Wireless Sensor Network (WSN). The application of SDN in WSN has been advantageous with respect to network management and configuration leading to a new network paradigm called SDWSN. Despite the benefits, SDWSN is faced with several challenges dominated by network management, security, and scalability. These have prompted several research activities among industrial and academic researchers worldwide. Though several solutions have been proposed or developed, most of the challenges still exist and more research works are needed to address them. Therefore, this paper presents a review of the challenges of SDWSN in the aspects of network management, security and its application on Internet of Things (IoT). The essence is to comprehend the existing challenges in an effort to find effective and efficient solutions to ensure more secure, dependable and energy efficient SDWSN. We reviewed several literature on WSN, SDN and SDWSN and presented the findings in the form of challenges and solutions. The analysis shows that SDWSN challenges originates from SDN, WSN and the technology is still at its early stage, though is developing.
Analysis of Cognitive Load using Entropy of Cortical Potential

Cognitive load is defined as the amount of mental workload due to the usage of working memory during a task. The analysis of cognitive load during a reading task is of immense importance in the area of education. It involves visual input, interpretation of the information, assimilation of the same and finally response to a given task. This complex mechanism is a coordinated effort of multiple regions of brain and is reflected in the electrical potentials on the cortical surface. These potentials are captured using Electroencephalogram (EEG) sensors. In this paper we use a 14 lead commercially available EEG sensor to derive the cognitive load during a reading task. Two types of tasks are defined namely, counting number of words with two characters, termed as low load task and counting number of words with two and three characters, termed as high load task. The transition of the peak potentials from one brain region to another reflects the dynamism in the brain during a given task. Initially, we establish that the entropy computed from the transition probabilities of peak potentials, reflect the level of cognitive load using a standard Stroop test. Later this entropy is used to estimate the amount of cognitive load experienced by individuals during the word counting task. Experimental results indicate that proposed methodology can well analyze and calibrate the cognitive load imposed by any task with respect to a standard task.
Real-Time Handwritten Letters Recognition on an Embedded Computer Using ConvNets

This paper describes the design and implementation of a convolutional neural network for 26 handwritten letters recognition on a regular embedded computer. Recognition task is carried out using a customized convolutional neural network, designed to work with low computational resources. Furthermore, training was conducted on the recently published dataset EMNIST. The experimental results show that the proposed neural network achieves an outstanding accuracy rate compared to similar architectures, also, inference shows a fast response time on a Raspberry Pi 3 board.
Etiopatogenia de la halitosis. Revisión

espanolLa halitosis o mal aliento es un problema importante dado que puede comprometer la vida de relacion de las personas. Es frecuente que se atribuya el origen al estomago o a problemas nasales o pulmonares. No obstante, sabemos que hasta en el 90% de los casos, el origen se encuentra en la cavidad oral, y de forma preponderante debido al acumulo de placa en el dorso lingual "cubierta lingual". La enfermedad periodontal tambien esta directamente relacionada con el mal aliento, de manera que el acumulo de placa, bolsas y/o sangrado son aspectos directamente relacionados con el problema. Desde el punto de vista patogenico, las proteinas de la dieta, las de las bacterias y las de los propios tejidos de la boca, son degradadas por la accion de las proteasas bacterianas hasta aminoacidos. Los aminoacidos ricos en azufre son los maximos responsables del mal aliento. De hecho los tres productos mas directamente relacionados son el sulfuro de hidrogeno, el metil mercaptano y el dimetil sulfuro. En la presente revision repasamos todos estos aspectos y las causas mas directamente relacionadas con el problema, revisando igualmente el concepto de halitofobia y pseudohalitosis. EnglishHalitosis, or bad breath is a major problem because it can compromise the people relationship life. Often origin is attributed to stomach or nasal or lung problems. However, we know that up to 90 % of cases, the source is in the oral cavity, and so dominant due to the accumulation of plaque on the dorsum of the tongue "cover tongue". Periodontal disease is also directly related to bad breath, so that the accumulation of plaque, periodontal pockets and/or bleeding are aspects directly related to the problem. From the pathogenic point of view, the dietary and bacteria proteins, and the own oral tissues proteins, are degraded by the action of bacterial proteases to amino acids. Rich in sulfur amino acids are the maximum responsible of bad breath. In fact the three most closely related products are hydrogen sulfide, methyl mercaptan and dimethyl sulphide. In this update, we review all these aspects and causes more directly related to the problem, also reviewing the pseudohalitosis and halitophobia concepts.
An Event Driven Wireless Sensors Network for Monitoring of Plants Health and Larva Activities

The aim of this project is to develop an efficient event driven wireless sensors network for an effective and power efficient monitoring of plants health and larva population in a remote crop field. In this framework, an event-driven wireless sensors network is proposed to detect larva and measure other system parameters like Acoustic Complexity Index (ACI), temperature, humidity and soil moisture. The sensors' data is collected by the front end sensing node, developed with a STM32F407VG board, via a serial port. The STM32F407VG board is based on the ARM processor. In contrast to the clock driven classical sensing nodes, the devised sensing nodes only acquire and transmits the sensors data in the case of a significant change. It significantly reduces the power consuming data acquisition and transmission activity as compared to the classical solutions. It improves the proposed solution power efficiency and autonomy as compared to the counter classical ones. The data from the node is transmitted to a base station by using a wireless ZigBee interface. The base station collects data from a group of event-driven sensing nodes. This data is transmitted to the Central Processing Unit (CPU) via the USB liaison between the base station and the CPU. On CPU this data is analyzed via the MATLAB based specifically developed application. The findings are displayed and logged on the CPU. It allows the terminal user to access this and to achieve a timely interaction and cure of the intended crop field. The system parameters are adjusted in order to achieve the effective modules integration and performance. The proposed system operation is tested with an experimental setup. Results have confirmed a proper system functionality.
Unknown Input Method Based Observer Synthesis and Actuator Fault Estimation for Polynomial T-S Fuzzy Systems

This paper focuses on estimating the unmeasurable states and unknown actuator fault of Polynomial Takagi-Sugeno (T-S) fuzzy system. A new approach based on the unknown input method for observer synthesis is proposed in which the unmeasurable states and the actuator faults are asymptotically estimated, simultaneously. The proposed method allows eliminating completely the effects of faults during the observer synthesis. On the basis of Lyapunov theory, the conditions for designing observer which are expressed in term of the Sum-Of Square (SOS) framework are derived. Finally, a numerical example is presented to prove the effectiveness of the proposed method.
Fixed Switching Frequency Direct Model Predictive Control Based on Output Current Gradients

In this paper we present a direct model predictive control (MPC) scheme, which combines control and modulation in one computational stage. The MPC problem is formulated as a current control problem. By utilizing a fixed modulation cycle akin to pulse width modulation (PWM) that allows for one switching transition per phase and half-cycle a constant switching frequency and a discrete current harmonic spectrum result. Thus, the issues that relate to the variable switching frequency and current interharmonics of direct MPC schemes are resolved. To verify the effectiveness of the proposed method a variable speed drive system, which consists of a two-level voltage source inverter and a medium-voltage induction machine, serves as a case study.
A Plug - in Electric Vehicle (PEV) with Compact Bidirectional CuK Converter and Sturdier Induction Motor Drive

This paper proposes a CuK converter based integrated battery charger for plug-in electric vehicles (PEV) incorporating an induction motor (IM) drive in the propulsion system. IM drive is controlled with an advanced feed-forward field-oriented controlled (FFOC) in order to reduce the protuberance caused during load disturbance. Proposed bidirectional DC/DC converter is capable of performing buck/boost function during all modes of vehicle operation. It operates as a power factor correction (PFC) converter during plug-in charging mode, and as conventional single switch inverting buck/boost converter in driving and regenerative braking modes. In addition, size, weight and cost of the charger are also reduced, as it involves the minimum number of components compared to existing buck/boost converters used in chargers. Simulations are performed on MATLAB/Simulink environment and a laboratory prototype of the aforementioned converter has been built to validate its feasibility.
Dynamics of Urbanization: The Empirical Validation of the Replacement Hypothesis

A model of urbanization based on the ‘replacement hypothesis’ is presented. The model is empirically validated from cross-sectional time-series data from eleven countries and the fits are found to be extremely good. Intercountry variations in the growth of urbanization are explained on the basis of the structure of the economy as reflected in the sectoral composition of the national output. The phenomenon of urbanization exhibits remarkable resilience in its growth trajectory. The model is also used to make projections of urbanization for the countries studied.
Hardware for Accelerating Anonymization Transparent to Network

With the increase in the number of Internet of Things (IoT) devices, many applications using the data accumulated from IoT devices have been proposed. Consequently, privacy violation due to data disclosure is becoming a problem. Data anonymization is a technique for publishing data while preserving data privacy and is an effective method for solving the problem of privacy violation. There are several requirements for implementing anonymization systems suitable for IoT devices, where the amount of hardware and software resources, function extensibility, and processing throughput are limited. To satisfy these requirements, anonymization hardware for anonymizing data packets from IoT devices is proposed. The proposed hardware is allocated on the intermediate location of network and anonymizes the data packet stream from IoT devices transparently. The proposed anonymization process does not affect data communication or routing. This transparency allows an additional anonymization function to be installed on all types of IoT devices without modifying the devices. The proposed hardware allows IoT devices to anonymize the data packet stream. To reduce the latency and achieve transparent anonymization at a low cost, the proposed anonymization hardware was implemented using a field-programmable gate array. The throughput and latency of the testbed of the proposed hardware were 1,286 Mbps and 330 ns, respectively. The power consumption of the proposed hardware was lower than that for a software implementation.
Formal Analysis of a Proof-of-Stake Blockchain

Blockchain technology relies on consensus algorithms to resolve conflicts in Byzantine environments. New blockchain algorithms are rapidly designed and implemented without a properly conducted formal analysis and verification. In this paper, we conducted a study on Tendermint which is a proof-of-stake consensus algorithm. We verified that the consensus protocol is deadlock-free and is able to reach consensus when at least 2/3 of the network is in agreement. We also proved that a minority set of nodes that compose more than 1/3 of the network is enough to censor the majority of the network and prevent the network from reaching consensus and conclude that the algorithm has some shortcomings on availability.
A 1-V 90.3-dB DR 100-kHz BW 4th-Order Single Bit Sigma-Delta Modulator in 40-nm CMOS Technology

A 1-V 4th-order discrete-time single bit sigma-delta modulator over a signal bandwidth of 100 kHz is presented in this paper. The feed forward topology is selected to reduce the voltage swings of the integrators. In addition, a novel two-stage inverter-based amplifier is proposed to improve the DC gain in low voltage environment. To save power consumption, a pure dynamic comparator is employed. Designed and implemented in 40-nm CMOS technology, the modulator achieves 88.5 dB peak SNDR (signal-to-noise and distortion ratio) and 90.3 dB DR (dynamic range). The core area of the chip is 0.133 mm2 and the total power consumption is 1 mW.
Semi-Automatic Registration of a Robotic Total Station and a CAD Model Without Control Points

The accurate registration of a robotic total station with respect to a given CAD model is a crucial task in the construction industry. Common registration techniques rely on a reference network of control points in the CAD model. One must establish correspondences between control points in the CAD model and measured points in the field. Usually physical markers or natural points of interest are selected as control points. We present a user-guided algorithm for simple and efficient registration of a robotic total station with a CAD model in indoor environments without the need for control points. The user interaction is reduced to selecting a local Manhattan-like corner structure for initial model alignment; accurate registration of the device is carried out automatically. Our algorithm relies on angle and distance measurements only and, therefore, is not limited to vision based robotic total stations. In particular, we propose a new algorithm for robust Manhattan corner extraction.
Quantification of Abrupt Driving Maneuver Utilizing ETC 2.0 Probe Data: A Case Study in Japan

As of the end of October 2017, the utilization of ETC 2.0 in Japan is about 15%, which is growing at a faster rate than its predecessor ETC1.0. ETC 2.0 probe data is designed to store new data fields, or features, such as position information history of the vehicle and abrupt driving maneuver history. Up to the present, the use of these data has not been well spreading, and the government of Japan supports the utilization of ETC 2.0 by the administrative and private enterprises. In this study, we aim to explore a new, effective use of ETC 2.0 probe data. The probe data is sizable, accumulating about 400 GB for one month. Analysis is conducted focusing on the number of abrupt driving maneuver in ten most popular tourist areas in the Greater Tokyo Area (“Kanto region”) from April 2015 to March 2016. Using the ETC 2.0 probe data, we propose a methodology to quantify abrupt driving maneuver of individual drivers and to evaluate their driving behaviors with a view towards business applications in mind.
Neural Sentence-Level Sentiment Classification with Heterogeneous Supervision

Sentence-level sentiment classification aims to mine fine-grained sentiment information from texts. Existing methods for this task are usually based on supervised learning and rely on massive labeled sentences for model training. However, annotating sufficient sentences is expensive and time-consuming. In this paper, we propose a neural sentence-level sentiment classification approach which can exploit heterogeneous sentiment supervision and reduce the dependence on labeled sentences. Besides the sentence-level supervision from labeled sentences, our approach can also incorporate the word-level supervision extracted from sentiment lexicons, document-level supervision extracted from labeled documents and sentiment relations between sentences extracted from unlabeled documents. A unified neural framework is proposed to fuse heterogeneous sentiment supervision to train sentence-level sentiment classification model. Experiments on benchmark datasets validate the effectiveness of our approach.
Automatic Note Recognition and Generation of MDL and MML using FFT

Increase in the number of online released music tracks means there is a need to improve the performance of existing Music Information Retrieval (MIR) systems, e.g., music search engine, and to develop a music version of Turn-it-in tool, a plagiarism checking system that evaluates the similarity between two pieces of music tracks. In our previous papers, we have proposed a symbolic coding scheme, named Music Definition Language (MDL) and Music Manipulation Language (MML), to describe music pieces; and designed corresponding algorithms to compute the similarity scores. However, the potential of MML/MDL won't be released if existing music pieces have to be manually transformed to their MML/MDL representations. This paper is focused on the automatic transformation of audio tracks, either analogue or digital signal, into MDL/MML representations. Following the case study approach, we first use MatLab to generate an MP3 file from a predefined MDL and MML representation, which correspond to the Net-based online music. Next, we adopt Fast Fourier Transform (FFT) to convert the MP3 file back to the MDL and MML file and check how accurate we can obtain via the similarity score. To achieve this purpose, the following features need to be extracted: frequencies, amplitude, time the note has been played and the duration. The experiment shows 93.63% contour melody similarity score and 100% for the rhythm have been achieved compared to the original file.
An Active Assistant Robotic System Based on High-Speed Vision and Haptic Feedback for Human-Robot Collaboration

Human-robot collaboration taking both human and robot's advantages becomes very promising in recent years. In this study, we propose a new active assistant robotic system for human-robot collaboration with the aim of optimally combining the cognitive capabilities of human and accurate motion control capabilities of robot. Under our method, human operator is provided with pneumatic haptic feedback to guide the system for coarse global-motion. Fine local-motion in an active manner is realized by a dynamic compensation robotic module with high-speed visual feedback. Since the active assist behavior here is confined to local motion and is accordingly safe to human operator. Experiments of preliminary investigation for set-point positioning and contour tracing under human-robot collaboration were implemented. Experimental results verified the effectiveness of the proposed method.
A Novel Five-Level Semi-Bridgeless Power Factor Correction Topology

This paper proposes a novel topology of a multilevel converter for power factor correction (PFC) applications. The proposed topology, with a split dc-link, can produce five distinct voltage levels with a reduced number of power devices. Inherently, the proposed topology operates with unitary power factor, sinusoidal grid current, and controlled dc-link voltage. The functional principle of the proposed topology is described in detail to show its benefits compared to the classical PFC topologies. The current control methodology adopts the discrete-time nature of the topology to define the distinct states throughout a control period. A laboratorial prototype was developed to validate the proposed topology under real conditions of operation, and the hardware structure and the implemented control algorithm are presented in this paper. The experimental validation shows a grid current with reduced harmonic distortion, a controlled voltage in the split dc-link, and a unitary power factor operation.
Compact, Broadband, and Polarization-insensitive 3 dB power splitter on silicon

An ultra-compact, ultra-broadband and polarization-insensitive 2×2 3 dB power splitter on silicon is proposed and demonstrated with a coupling region as short as 4.6 μm. The fabricated 3dB power splitter works very well for both TE and TM polarizations in a broad band from 1520 nm to 1610 nm.
Sequential Pattern Sampling with Norm Constraints

In recent years, the field of pattern mining has shifted to user-centered methods. In such a context, it is necessary to have a tight coupling between the system and the user where mining techniques provide results at any time or within a short response time of only few seconds. Pattern sampling is a non-exhaustive method for instantly discovering relevant patterns that ensures a good interactivity while providing strong statistical guarantees due to its random nature. Curiously, such an approach investigated for itemsets and subgraphs has not yet been applied to sequential patterns, which are useful for a wide range of mining tasks and application fields. In this paper, we propose the first method for sequential pattern sampling. In addition to address sequential data, the originality of our approach is to introduce a constraint on the norm to control the length of the drawn patterns and to avoid the pitfall of the "long tail" where the rarest patterns flood the user. We propose a new constrained two-step random procedure, named CSSampling, that randomly draws sequential patterns according to frequency with an interval constraint on the norm. We demonstrate that this method performs an exact sampling. Moreover, despite the use of rejection sampling, the experimental study shows that CSSampling remains efficient and the constraint helps to draw general patterns of the "head". We also illustrate how to benefit from these sampled patterns to instantly build an associative classifier dedicated to sequences. This classification approach rivals state of the art proposals showing the interest of constrained sequential pattern sampling.