Miracle-dz/UniFAR-FTUs huggingface dataset:
Markov chain based opportunistic routing protocol to enhance the performance of the MANET

Mobile ad hoc networks (MANETs) have inherently dynamic topologies. Due to node mobility, the links along the paths suffer frequent failure and reactivation in highly dynamic environments. A new routing paradigm, known as Opportunistic Routing (OR) has recently been proposed that finds routes in adaptive manner, based on current channel conditions in dynamic wireless environment. OR is based on the use of broadcast transmissions to expand the potential forwarders that can assist in the retransmission of the data packets. In this paper, we embark upon the basic operations of OR and a framework to support decision making for finding optimal route in the MANET. In later part we propose an adaptive algorithm to deal with OR as a Markov decision problem (MDP). Finally the comparative analysis of the proposed Markov chain based protocol with OR protocol: CORMAN has been carried out on the basis of throughput, average delay and control overhead. The proposed protocol shows the better results as compared to existing one.
Audio Based Handwriting Input for Tiny Mobile Devices

The popularization of tiny mobile devices has raised the problem that it is hard to efficiently input messages via tiny keyboards or touch screens. In this paper, we present TableWrite, an audio-based handwriting input scheme, which allows users to input words to mobile devices by writing on tables with fingers. The key feature is that, once trained by a user, TableWrite does not require any retraining phase before each use. To reduce the impacts of audio signal’s multipath propagation, we design multiple features that maintain consistency even when writing positions keep changing. We apply machine learning and gesture tracking techniques to further improve the accuracy of handwriting recognition. Our prototype system’s experimental results show that the average accuracy of word recognition is around 90%-95% in lab environments, which validates the effectiveness of TableWrite.
Novel approach to robust PI controller design with state derivative feedback for linear uncertain systems

The paper devoted is to the problem of develop robust PI controller design procedure for uncertain linear systems with state derivative feedback. The obtained continuous-time PI robust controller with output/state and state derivative feedback ensure the guaranteed cost and parameter dependent quadratic stability in frame of H2. The novel results are that the designed robust PI controller is used for stabilization of closed loop systems with the state derivative feedback. Due to new auxiliary matrices the design procedure gives less conservativeness results. Numerical example is given to illustrate the properties and effectiveness of the proposed method.
Fast write operations in non-volatile memories using latency masking

Energy consumption is an important issue in designing embedded systems and the emerging Internet of Things (IoT). The use of non-volatile memories instead of SRAM in these systems improves their energy consumption since non-volatile memories consume much less leakage power and provide better capacity given the same die area as SRAM. However, this can impose significant performance overhead because the write operation latency of non-volatile memories is more than that of SRAM. In this paper we presented an NVM-based data memory architecture for embedded systems which improves the performance of the system at the cost of a slight energy consumption overhead. The architecture employs multi-banking techniques to parallelize the write operations and adds a write buffer which masks the latency of some write operations and reduces the average write latency of the memory subsystem. Compared to a system which uses a single-bank NVM, the proposed architecture can improve the performance by 38% at the cost of 9% increase in the energy consumption.
A LSTM-based bidirectional translation model for optimizing rare words and terminologies

Neural translation model has greatly grown in recent years. Many researches have come up with very good solutions to deficiencies in Neural translation model. However, it is difficult to get best effect for rare words and terminologies what are marked as unknown words because of the limit of the dictionary's size. This paper presents a bidirectional translation model what can be used to translate between bilinguals and optimize rare words and terminologies. At first we use word2vec to get a word similarity model. By replacing the rare words to be trained and tested by similarity model, we solve the problems caused by rare words. In addition, all terminologies are treated as a rare word to join this model, so that there is a good performance in translating terminologies. Then, by introducing mutual learning in the symmetric LSTM, the translation accuracy between bilinguals has been improved. As experimental results show, this method achieves expected goal in effectiveness and accuracy.
Intrinsic Formulation of KKT Conditions and Constraint Qualifications on Smooth Manifolds

Karush-Kuhn-Tucker (KKT) conditions for equality and inequality constrained optimization problems on smooth manifolds are formulated. Under the Guignard constraint qualification, local minimizers are shown to admit Lagrange multipliers. The linear independence, Mangasarian-Fromovitz, and Abadie constraint qualifications are also formulated, and the chain "LICQ implies MFCQ implies ACQ implies GCQ" is proved. Moreover, classical connections between these constraint qualifications and the set of Lagrange multipliers are established, which parallel the results in Euclidean space. The constrained Riemannian center of mass on the sphere serves as an illustrating numerical example.
Screening for propagation suitability in vitro of different Cyclamen species

In the present study we examined the possibility of propagating different Cyclamen species ( C. africanum Boiss. and Reut., C. cilicium Boiss. and Heldr., C. coum Mill . , C. hederifolium Ait., C. persicum Mill., C. purpurascens Mill.) including some of their subspecies and cultivars in vitro using explants of adult plants. For this purpose two protocols have been applied to eleven genotypes combined with mostly four explant types (placentas with ovules, leaves, petioles and peduncles). The use of these protocols has given rise to either somatic embryo-like structures and/or adventitious shoots in all genotypes. This way it was possible to propagate each of the examined genotypes in vitro using explants of adult plants in a time less than one year. These results may be used in breeding and propagation of Cyclamen as an ornamental plant and as a medicinal plant.
A 13-mW 64-dB SNDR 280-MS/s Pipelined ADC Using Linearized Integrating Amplifiers

A 12-bit pipelined analog-to-digital converter (ADC) using a new integration-based open-loop residue amplifier topology is presented. The amplifier distortion is cancelled with the help of an analog linearization technique based on a tunable input-driven active degeneration. Amplifier gain and nonlinearity errors are detected in background using split-ADC calibration technique. The mismatch between the two half-ADCs is minimized by sharing the residue amplifier between the two half-ADCs and adding the calibration offset over time. Based on this “split-over-time” calibration architecture, a two-lane prototype ADC was fabricated in 28-nm CMOS that achieves 64-dB signal-to-noise + distortion ratio and 77dB spurious-free dynamic range at Nyquist input after calibration. Operating at 280 MS/s, the ADC consumes 13 mW from 1-V supply, exhibiting a Schreier figure-of-merit of 164.3 dB. By dissipating only 0.4 mW in the residue amplifiers, the linearization technique helps the ADC achieve an improvement of at least 3 dB in Schreier FoM over existing state-of-the-art ADCs with comparable architectures.
Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.
The proposition of a multiagent system for adaptive protection of a distribution system

The increasing consumption demand, the usual structures of the Distribution Systems (DSs) have experienced an expressive insertion of Distributed Generators (DGs). Depending on the resulting topology, these new DSs form microgrids in order to supply the demand demanded by certain loads against specific situations. However, the inclusion of DGs may cause adverse effects to DS, such as in the associated conventional protection system. In this context, this research brings the proposition of a Multiagent System (MAS) applied to the adaptive protection of a microgrid. This proposition was validated via simulations in the Real Time Digital Simulator (RTDS) using RSCAD software. For the research, the 13-bar distribution system of the Institute of Electrical and Electronics Engineers (IEEE) was simulated in the presence of photovoltaic generation connected in one of its bars. On this modified system, several different short-circuit situations have been applied. The results of the simulation and the resulting analyzes indicate that the use of a MAS will be quite effective in the proposed adaptive protection.
Research on torque model based on pretreatment method for switched reluctance motor

Owing to high nonlinearity of the electromagnetic characteristic caused by double-salient structure and high magnetic saturation, it is hard to establish an accurate model using formulas for switched reluctance motor (SRM). This paper establishes a torque model based on good nonlinear mapping ability of BP neural network. And for further optimization, a third input variable reflecting the system nonlinear characteristics is added to establish a BP neural network for SRM using pretreatment method. Comparing to traditional torque model, the proposed model has a small-scale network structure and higher accuracy, which is propitious to achieve real-time online control for SRM driving system.
Next generation firewalls for dynamic coalitions

Firewalls represent a critical security building block for networks as they monitor and control incoming and outgoing network traffic based on the enforcement of predetermined security rules, referred to as firewall rules. Firewalls are constantly being improved to enhance network security. From being a simple filtering device, firewall has been evolved to operate in conjunction in intrusion detection and prevention systems. This paper reviews the existing firewall policies and assesses their application in highly dynamic networks such as coalitions networks. The paper also describe the need for the next-generation firewall policies and how the generative policy model can be leveraged.
The implementation of healthcare information systems: The role of commitment and usage in benefits achievement

There is an increasing interest in the relationship between the use of electronic medical records (EMR) and the production of health data. However, the assimilation of a healthcare information system (HIS) requires organizational changes in the practices of health professionals, which are not easily accepted by them. In a complex organizational context such as health, this information makes the role of the users regarding the adoption of a HIS, an important topic in the field of research of IS. In this sense, it is relevant to investigate how top management's commitment influences the commitment of the professionals in relation to the changes induced by the implementation of a HIS, as well as, in what way the HIS usage enables the achievement of expected benefits from its adoption. A qualitative, interpretative and case-based methodology was used, and the data was collected using semi-structured interviews, documentary analysis and direct observation. Two Portuguese public hospitals were analyzed. It was found that top management's commitment, and that of the Clinical Directorate is a key factor in a more rapid and comprehensive implementation of the HIS, influencing professionals' commitment and the usage of the system, which contributes to the increase of the potential expected benefits.
Smart pump control system for optimal distribution of electricity

The availability of water and by extension, the availability of electricity to operate the water pumps is necessary for farming. However, the total electricity available for use in each electricity grid is limited. Water pumps constitute the majority of the electrical consumption in rural farming regions. Hence, the number of water pumps that can be present in an electricity grid is severely affected due to electricity shortages. This problem is compounded by the fact that all the pumps are generally operated at the same time leading to a huge demand for electricity. This problem can be solved by distributing the electricity supply to the pumps evenly throughout the day. For this, an existing control panel will be modified by adding a GSM module to provide remote functionality. The control panels will then be able to be controlled remotely by the user through a mobile phone as well as the authorized government body through a central server. The system will consider the load profiles of the pumps in the grid and distribute them to minimize peak electricity consumption. Simulations show that the system will allow the government body to monitor all the water pumps in the electricity grid and control their operation to achieve minimum electricity load.
A Balunless Frequency Multiplier With Differential Output by Current Flow Manipulation

A balunless frequency doubler (FD) architecture which can provide differential output without any additional balun required is proposed in this paper. The architecture manipulates the desired second-harmonic currents around the doubler core by a multifunction network to avoid any leakage current path from the output current loop. Therefore, the output currents extracted from the same current loop can have the same amplitude and phase. As the output currents flow into and out of the same loads, respectively, the induced output voltages can be perfectly differential without needing to add a balun. A 60-GHz FD realized in a 90-nm CMOS technology is designed to verify the proposed FD architecture. The measured amplitude and phase imbalances of the differential output are only 0.2 dB and 0.5°, respectively, while providing −5.5-dB conversion gain at an output frequency of 60 GHz. The proposed FD only consumes 15.9 mW from a 1-V supply. The proposed doubler architecture can be theoretically extended to realize a frequency multiplier with a multiplication factor larger than 2.
Face recognition algorithm based on multi-scale CLBP

Digital surveillance systems are extensively being used in our daily lives, and there is an increasingly demand for face recognition technology based on monitoring scenarios. In this paper, we propose a recognition algorithm based on multi-scale completed local binary pattern (CLBP) operators which can be used for face recognition tasks. The CLBP operator which is initially used in texture recognition has achieved great performance. The key idea of CLBP is using three CLPB operators to construct mixed feature representation. On this basis, the concept of multi-scale CLBP mixed-feature construction is proposed, which extracts the same feature on different pixel-scale levels and fused as one optimal feature. The multi-scale CLBP descriptor contains much more information and has great discriminative power. We investigate the performance of our algorithm on public dataset. Extensive experiments results demonstrate that our multi-scale CLBP algorithm outperforms other state-of-art operators.
An iterative decoding scheme for Davey-MacKay construction

In the Davey-MacKay (DM) construction, the inner decoder treats unknown transmitted bits as random independent substitution errors. It limits the synchronization capability of the inner decoder, and thus weakens the error-correcting capability of the DM construction. In order to improve the performance of the DM construction, an iterative decoding scheme is proposed, which iteratively utilizes the more accurate estimates of transmitted codewords. In the proposed scheme, the estimated average bit error rates and the estimated low-density parity-check (LDPC) codewords from the outer decoder are fed back into the inner decoder to update the synchronization process. Simulation results show that the proposed iterative decoding scheme significantly outperforms the traditional DM construction.
Soccer Fans Sentiment through the Eye of Big Data: The UEFA Champions League as a Case Study

The proliferation of online soccer contents generated by fans and teams has been widely leveraged in the area of sentiment analysis research. This work proposes domain-specific approach for understanding sentiment expressed in soccer conversations. We introduce a soccer-specific lexicon that we leverage in building sentiment model trained on soccer dataset. Our results show the effectiveness of the proposed approach in recognizing fans emotion during soccer events.
A service-oriented middleware for cloud of things and fog computing supporting smart city applications

Several cities around the world are seeking to adopt the smart city concept to enhance the living quality of their residents and optimize city resources. Smart cities heavily rely on utilizing a number of technologies to improve the performance and services in healthcare, transportation, energy, education, and many others, while reducing costs and resource consumption. One of the promising technologies to support such efforts is the Cloud of Things (CoT). CoT can provide a platform for linking the cyber parts of the smart city that are executed on the cloud with the physical parts of the smart city including residents, vehicles, power grids, buildings, water networks, hospitals and other physical resources. Another technology that can be utilized for enhancing smart cities services is Fog Computing, which extends the traditional Cloud Computing paradigm to the edge of the network to enable better support for operating enhanced services. However, proper integration and efficient utilization of CoT and Fog Computing for smart cities is not an easy task. The paper discusses how the service-oriented middleware approach can help resolve some of the challenges of developing and operating smart city services using CoT and Fog Computing. In addition, this paper proposes a service-oriented middleware, called SmartCityWare, for effective integration and utilization of CoT and Fog Computing for smart cities.
LncRNAs in genetic basis of glaucoma

Glaucoma is an umbrella term used to designate a heterogeneous group of ocular disorders characterised by progressive excavation of the optic disc, optic atrophy and gradual loss of the visual field caused by the slow death of retinal ganglion cells and their axons. Glaucoma can potentially lead to blindness if left untreated. It usually starts from the periphery and progresses gradually toward the centre of the visual field. Vision loss caused by glaucoma is irreversible and causes a heavy burden on affected families and society, therefore the importance of early diagnosis and prevention should be emphasised. Genetic factors appear to play a role in glaucoma pathogenesis; it has been shown that individuals with a positive family history are at a greater risk because they are more likely predisposed be affected. Notable advances have been recorded in the past decade concerning the genetic and environmental factors likely to contribute or cause glaucoma with the discovery of multiple glaucoma-associated genes and genetic loci. Thorough investigations by a handful of studies on the function of long non-coding RNAs discovered that, although lacking protein-coding potential, lncRNAs can still participate in the regulation of gene expression at various levels, thus their possible implication in different disease aetiologies. In this review, we focus on the lncRNAs characteristics and its regulation, and summarise these results from separate, independent, glaucoma-related studies in addition to discussing possible pathways by which lncRNAs might contribute to glaucoma.
Keyframe extraction and loop closure detection considering robot motion

Appearance based loop closure detection is a crucial technique for robot simultaneous localization and mapping. Extracting appropriate keyframes can reduce computational cost. In this paper, we propose a keyframe extraction and loop closure detection algorithm considering robot motion information. Both linear and angular velocities of the robot are utilized to decide when to extract the query keyframe. Comparative experiments are carried out on a public dataset to verify the proposed algorithm. The results demonstrate that our algorithm can extract appropriate keyframes at key points on robot trajectory and can be used in loop closure detection.
Cross-differential protection for double-circuit lines using current traveling waves

This paper presents a one-terminal traveling wave-based cross-differential protection algorithm for double-circuit lines which uses only current measurements. Assuming that both circuits of the monitored double-circuit line have the same lengths, the algorithm reliably classifies faults as internal or external, assuring the algorithm security and reliability. If the fault is internal, the method operates, detecting the faulted phases of the faulted line, otherwise, the algorithm properly restrains. The traveling wave-based cross-differential protection function extracts current traveling wave information from measurements taken from both current transformers installed at the monitored terminal and uses only the first incident wavefronts to implement the tripping logic. Using the Alternative Transients Program (ATP), several fault simulations were carried out, attesting that the presented protection function is able to reliably detect internal faults in few milliseconds after the disturbance inception.