Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing the Shape of Faster-Than-Nyquist (FTN) Signals With the Constraint on Energy Concentration in the Occupied Frequency Bandwidth

The achievement of the boundary values of spectral efficiency is associated with the development of methods for generating and receiving signals with a compact spectrum. The reason is a constant increase in the capacity of existing communication channels caused by an increase in the volume of transmitted information. Moreover, the allocated frequency bandwidths have natural limitations, and they are almost all reached. The effective way to approach the boundary values of spectral efficiency is the application of spectrally efficient signals, such as FTN (Faster-Than-Nyquist) signals. This article proposes to synthesize optimal FTN signals that are more compact in the spectrum than RRC (root raised cosine) pulses-based signals. The criterion of maximum energy concentration in the occupied frequency bandwidth and the constraint on the cross-correlation coefficient are used to solve the optimization problem. The contribution of this work is the optimization of FTN signal shape. The obtained optimal FTN signals provide a 24% increase in spectral efficiency compared to the RRC pulses-based signals. At the same time, the energy loss stays almost unchanged. To the best of authors’ knowledge, this is the first case when the frequency bandwidth reduction is achieved at practically no energy loss. The simulation modeling in channels with additive white Gaussian noise and fading channels is done with regard to the FTN-SC-FDE (Faster-Than-Nyquist - Single Carrier system with Frequency Domain Equalization) structure. The optimal FTN signals presented in this work can be used to increase the spectral efficiency of satellite broadcasting systems, such as DVB-S2/S2X.
Sensory Outcomes in Digital Nerve Repair Techniques: An Updated Meta-analysis and Systematic Review

Background: Injuries to digital nerves are common with trauma to the hand, often requiring surgery. Surgical management of these injuries can be performed using several techniques: direct repair (neurorrhaphy), autograft, allograft, and conduit repair. In light of increasing the availability and use of various digital nerve repair techniques, a new systematic review and meta-analysis was undertaken to comparatively review the available evidence to determine any differences in outcomes to better guide treatment in cases with digital nerve gaps. Methods: Current literature on sensory outcomes of various digital nerve repair techniques was reviewed using static 2-point discrimination (S2PD), moving 2-point discrimination (M2PD), Semmes-Weinstein monofilament testing (SWMF), and complication rates as outcomes of interest. After inclusion and exclusion criteria were applied, 15 articles were reviewed and 625 nerve repairs were analyzed. Results: The average gap length for allograft repair, autograft repair, and conduit repair was 15.4, 24.7, and 13.4 mm, respectively. For S2PD outcomes, autograft repair was statistically superior to all other forms of repair. Allograft trended higher than neurorrhaphy and conduit repair, but results were not statistically significant. For SWMF outcomes, autograft repair was statistically superior to conduit repair and neurorrhaphy; it was statistically comparable with allograft repair. Allograft performed statistically superior to conduit repair relative to M2PD. Conclusions: Based on the current updated meta-analysis using newer data and techniques, we found that all available techniques have reasonable outcomes. Yet when managing a digital nerve injury with a gap, thereby excluding direct neurorrhaphy, both autograft and allograft performed comparably and were superior to conduit repair.
INFECÇÃO POR Ehrlichia canis E Anaplasma platys EM CADELAS E NEONATOS DE CUIABÁ, MATO GROSSO

Os canideos podem ser infectados por diversos agentes pertencentes a Familia Anaplasmataceae dentre os mais comumente relatados encontram-se Ehrlichia canis e Anaplasma platys, causando a ehrlichiose monocitica canina e a trombocitopenia infecciosa ciclica canina, respectivamente. O diagnostico pode ser feito pela deteccao de morulas em esfregaco sanguineo, sorologicamente, pelo isolamento, com excecao de A. platys e pelos metodos moleculares, como a PCR. O objetivo desta pesquisa foi avaliar a presenca de infeccao por E. canis e A. platys em caes neonatos e suas respectivas maes. Durante o periodo de junho a dezembro de 2006, foram avaliados clinicamente 21 femeas higidas e 40 filhotes de suas respectivas ninhadas com idade entre cinco a 29 dias de idade. Para a avaliacao da infeccao por E. canis , realizaram-se analise sorologica com o ensaio SNAP 3DX, observacao de morulas em esfregaco sanguineo e nested PCR e para A. platys, a nested PCR. Dos 61 animais avaliados, 36 (59%) apresentaram-se positivos para E. canis e 16 (26,2%) para A. platys . Infeccao isolada por A. platys e co-infeccao com E. canis foi observada em cinco (13,8%) e 11 (30,5%) caes, respectivamente, nao havendo diferenca estatisticamente significativa (P=0,63). Com os dados obtidos pela tecnica da n PCR conseguiu-se detectar infeccao por E. canis e A. platys em neonatos e suas respectivas maes, levantando a possibilidade de transmissao transplacentaria, entretanto estudos mais aprofundados devem ser realizados para se confirmar tal aspecto, principalmente em areas endemicas para a doenca.
Comparison of Video Codecs Performance for Real-Time Transmission

Nowadays, the need for more efficient video compression algorithms is growing, due to the increasing consumption of video content in higher resolutions. Thus, it is obvious that video compression technologies play a key role in the distribution of video content in broadcasting. In this paper, the video compression comparisons take into account the encoding or decoding time which is needed for each compression standard, in order to provide evidence about their adaptation under real-time streaming conditions. The video test sequences used in our experiments were retrieved from miscellaneous sources and their common characteristic is that they have High or Ultra High Definition resolution. Video codecs' performance was tested by using commercial coding libraries and reference software of the coding standards.
Prediction Diabetes Mellitus Using Decision Tree Models

Health is an essential thing in human life, but Indonesian people are still far from the word healthy lifestyle. One disease that can be caused by an unhealthy lifestyle is diabetes mellitus that can cause many deaths. So far, there are many data in the hospital, but the data can not be maximized well even if it can be used to predict diabetes. The need for accuracy to produce better results. In conducting this test, the results obtained using C 4.5 only algorithm is 72.08%.
A novel approach for high-velocity big geo-data handling using iterative and feature learning algorithms

Geospatial data were exclusively generated by official agencies. However, following the technological revolution in data collection and production, various sources have emerged for the massive production of geospatial data, resulting the phenomenon of big geo-data. Therefore, dealing with large amounts of these data sets, results in a high velocity as they change very quickly, is a challenging task. Hence, analysis become more complex and computation become prohibitively expensive. As a result, spatial computing technologies become limited in front of these complex data and operations. Accordingly, we aimed to refine complexity with simplicity by replacing traditional geospatial models with referring to the simplest intelligent and minimum resource requirement algorithms that can be applied against these constraints, while ensuring the criteria of performance and scalability. In this work, we focus on the high-velocity of this big geo-data through the use of an iterative approach applied to a feature learning algorithms to decrease the memory consumption and the time complexity of traditional machine learning algorithms. According to our knowledge, although they were widely applied in the 19th century as a solution to overcome the problems of limitation of memory and computing resources. Iterative methods were still not used for the big geo-data analytics and generally for the big data domain. Thus, this approach could be beneficial especially for real time applications such as the anomaly monitoring and detection.
Challenges Affecting the Effective Communication of PHI to Non-Medical Users: A Contextual Inquiry.

Non-medical users, those without any medical training background, have difficulties in accessing, reading, locating, understanding and interpreting their personal health information (PHI). Additionally, the medical user, those with a medical training background, use medical terminologies and language which makes it even more difficult for the non-medical users to understand their PHI. The aim of this study was to investigate the challenges experienced in communicating PHI effectively to non-medical users. A contextual inquiry was carried out and the results showed that limited access to PHI, lack of understanding the content in the PHI reports, lack of proper communication, lack of uniformity in the implementation of the EMR system, are some of the challenges preventing effective communication of PHI to non-medical users.
Wireless Network Evolution Towards Service Continuity in 5G enabled Mobile Edge Computing

One of the main centrepieces in the next generation of cellular wireless communication networks is to always provide seamless network connectivity on the top of high-speed data rate and network availability. The emerging fifth-generation (5G) is expected to drastically improve the market demand in terms of higher data rates, excellent user coverage, superb end-to-end connection with very low-latency and ultra-reliability. Even though existing mechanisms such as handover and roaming procedures have already introduced possible solutions in previous generations, mobile users still suffer from delays during network switching in cross border situations. This paper summarizes the research being done on service continuity cross border for different generations of cellular wireless communication networks and demonstrates the benefits that 5G brings to service continuity use cases in the context of a European project. Opening challenges remain highlighted and guidelines are provided to modify the current schemes to address these limitations.
Music4All: A New Music Database and Its Applications

One of the goals of the music information retrieval (MIR) community is to research new methods and create new systems that can efficiently and effectively retrieve and recommend songs from large databases of music content. Despite the volume of research in the area, there is a lack of music databases to support these works, i.e. databases that comply with some quite desirable requirements for the development of researches, such as: a huge amount of music pieces, the audio signal availability and a great diversity of audio attributes. In order to contribute to the MIR community, we present Music4All, a new music database which contains metadata, tags, genre information, 30-seconds audio clips, lyrics, and so on. Additionally, we also exemplify some MIR tasks that may benefit from our database and compare it with other databases proposed in the literature. To request the Music4All database, send an e-mail to contact4music4all@gmail.com.
Towards Creating Transparency in Loyalty Payments in Music Streaming Platforms

In the music industry, the blockchain could provide indisputable record of song listens, i.e., the users listening patterns, duration of listening, location where the song was streamed, the radio/TV channel that played the song, etc., that could assist in transparent calculation of artists’ royalties. This way, the music industry does not only benefit the middlemen, who introduce a lot of bureaucracy in the distribution and streaming of music, but the core music industry contributors - the artists. Thus, to ensure fairness and transparency, and to make musicians key players in loyalty calculations and payment, this paper proposes a blockchained based music streaming platform where the music lovers pay for what they listen to while creating transparency in payment of royalties to the music owners by the music streaming providers. The proposed mechanism is built on permissioned blockchain where the music streaming service provider, the music lovers and the music owners are members and each can create and commit to given records in the platform.
Prediction of expected Angle of knee joint of human lower limbs based on leg interaction

In order to convert the leg interaction force between the exoskeleton and human body into the expected angle of the movement of the human hip and knee at the next moment, and realize the recognition of human motion intention, the mapping relationship between the joint angular velocity and the active joint torque generated by the leg interaction force was studied. Firstly, by collecting joint angle data from the gait experiment, the movement characteristics of lower limb swing phase and support phase in normal daily gait were analyzed, and the joint angle prediction strategies for different phases of human body were determined. Then, by establishing the dynamic model of exoskeleton under human-machine coupling, the formula of human body gravity compensation was obtained. Then, regarding the leg swing phase, the nonlinear mapping relationship between the joint angular velocity of human body and the joint torque generated by the interaction force of the leg after gravity compensation was established through the admittance control model. The desired Angle was obtained by multiplying the angular velocity of the control period plus the current joint angle. Finally, for the prediction of the expected joint Angle of the leg support phase, the hip joint should be controlled by position to keep the back of the exoskeleton upright, and the knee joint should adopt the same algorithm as the swing to obtain the desired joint angle.
Blockchain-Based Wi-Fi Offloading Platform for 5G

The advent of 5G has sparked interest in Wi-Fi offloading techniques that enable efficient resource sharing and congestion management of wireless communication spectrum. However, offloading data between multiple networks (i.e. service providers) requires costly inter-provider communication which has a substantial overhead as well as high offloading latency. Moreover, involvement of the profit-oriented decision making of service providers has an inherent weakness of unfair scheduling among users and networks. To overcome those problems, this research work proposes a holistic framework similar to an online data market place where existing infrastructure can be used to set up Wi-Fi zones that everyone can use from their own data plan irrespective of the network operators they belong to. First, our proposed architecture improves the efficacy of offloading by using decentralized nature of the emerging Software-Defined Networking (SDN) to set up an operator-assisted data offloading platform, resulting in efficient inter-provider communication. Second, our proposal strengthens the fair scheduling of offloading resources by using blockchain technology to initiate unbiased and independent decision making. The resulting service is a rating system for the sellers to make reliable transactions for payments.
Study of motor imagery for multiclass brain system interface with a special focus in the same limb movement

The presented study focuses on simple upper limb movements; lifting and clenching for use in Motor Imagery (MI) based Brain System Interface (BSI). Furthermore, we analyzed the same limb movement imagery and its validity in developing multi-class BSI. 15 subject datasets were analyzed using feature extraction and classification methods from regularized common spatial pattern toolbox. The results for offline analysis are presented in this study. The multiclass analysis was done using One-Versus-One (OVO) and One-Versus-Rest (OVR) approaches using pre-selected feature extraction and classification. OVO and OVR binary classifiers were converted into 4 class classifiers using a novel score based method. Neither lifting nor clenching actions showed any substantial advantage over the other for binary classification. Furthermore, within the same limb, two kinesthetically close actions (lifting vs. clenching) show good differentiability even with the same shared neural pathway. However, further analysis of the performance of the same limb actions in the 4 class environment is required. The same limb movements, if successfully incorporated, shows potential in increasing the number of differentiable classes in a multiclass BSI.
On closed-loop stability of model predictive controllers with learning costs

Model predictive controllers are commonly associated with a fixed running and/or terminal cost function. Recently, some possibilities of cost function adaptation inspired by reinforcement learning were investigated. The current study analyzes closed-loop stability of such controllers in a general way. It is shown what constraints on learned running and terminal cost are required for this sake. A particular feature of the suggested control scheme is that, unlike in some common model predictive controllers, an assumed local Lyapunov function does not have to satisfy a decay function not less than the running cost. Relation of the considered control scheme to a baseline model predictive controller and adaptive dynamic programming is discussed. In a case study, it is shown how different cost function adaptation schemes lead to different performance with respect to the infinite-horizon cost.
Modelling False Data Injection Attacks Against Non-linear State Estimation in AC Power Systems

False data injection (FDI) attacks can disrupt the operation of the smart grid by manipulating the state estimation process without being detected. To deal with these kinds of attacks on smart grid networks, vulnerability analysis should carefully be developed under realistic conditions. Existing research efforts on FDI attacks have not performed the vulnerability analysis of the smart grid using the AC state estimation without incurring significant computational complexity. In this paper, we develop a low-complexity system to model the least-effort FDI attacks in the AC power grid. We do that by using a reduced row echelon (RRE) form-based greedy method on the AC state estimation process to compute the minimum number of measurements an attacker needs to compromise to launch the undetectable low-cost FDI attack with more efficiency. Simulation results obtained for various IEEE standard test systems show the efficient performance and enhanced accuracy of our proposed approach for modeling least-effort attacks.
Predator-recognition training enhances survival of brook trout: evidence from laboratory and field-enclosure studies

In this study we tested whether brook trout (Salvelinus fontinalis) can learn to recognise predators through releaser-induced recognition learning and whether this learning enhances survival of trout during encounters with a predator. In our initial experiment, we exposed hatchery-reared predator-naive brook trout to chemical stimuli from predatory chain pickerel (Esox niger) paired with alarm signals released by damaged trout, disturbance signals, or distilled water. In subsequent tests 24 h later, when only pickerel odour was presented, trout conditioned with damage-released alarm signals exhibited antipredator behaviour (i.e., decreased movement and altered foraging patterns), in contrast to the other treatments, thereby demonstrating learned recognition of the predator. In our second experiment we showed that trout retained the ability to recognise the predator for at least 10 days. In the next series of experiments we explicitly tested whether training trout to recognise predators confers a survival .
An aphid lineage maintains a bark‐feeding niche while switching to and diversifying on conifers

Lachnine aphids are unusual among phytophagous insects because they feed on both leafy and woody parts of both angiosperm and conifer hosts. Despite being piercing‐sucking phloem‐feeders, these aphids are most speciose on woody parts of coniferous hosts. To evaluate the significance of this unusual biology on their evolution, we reconstructed the ancestral host and feeding site of the lachnine aphids and estimated important host shifts during their evolution. We sampled 78 species representing 14 of the 18 genera of Lachninae from Asia and North America. We performed parsimony, Bayesian and likelihood phylogenetic analyses of combined mitochondrial Cox1, Cox2, CytB and nuclear EF1a1 DNA sequences. We dated the resulting phylogram's important nodes using Bayesian methods and multiple fossil and secondary calibrations. Finally, we used parsimony and Bayesian ancestral state reconstruction to evaluate ancestral feeding ecology. Our results suggest the lachnine common ancestor fed on a woody part of an angiosperm host in the mid‐Cretaceous. A shift to conifer hosts in the Late Cretaceous is correlated with a subsequent increased diversification in the Palaeogene, but a switch to leafy host tissues did not engender a similar burst of diversification. Extant lachnine lineages exhibit the full range of historical association with their hosts: some appeared before, some concomitant with and some after the appearance of their hosts. We conclude our study by placing all the lachnine genera in five tribes.
Three-Dimensional Aerial Cell Partitioning Based on Optimal Transport Theory

In this paper, we investigate a three-dimensions (3D) cellular network that incorporates both unmanned aerial vehicle-base stations (UAV-BSs) and UAV-users (UAV-UEs). In the considered model, UAV-BSs hover in 3D space to serve UAV-UEs that are distributed according to an arbitrary spatial distribution function. We propose an optimal 3D cell partitioning method based on fair resource allocation scheme for which the total amount of data service is maximized. Utilizing the mathematical tool of optimal transport theory, the cell partitioning problem can be modeled as a semi-discrete optimal transport problem which is easy to be solved. To this end, we apply steepest-ascent search algorithm to solve the 3D cell partition optimization problem simplified by the optimal transport theory. The simulation results show that fairness of the proposed cell partitioning algorithm is higher than max-SINR algorithm, and it also guarantees the amount of system data service.
A Machine Learning Radar Scheduling Method Based on the EST Algorithm

A machine learning radar scheduling method is proposed based on the earliest start time (EST) algorithm. In this method, the EST algorithm is used to find an initial schedule, and a reinforcement learning approach is conducted to reduce the cost of the initial schedule. In search for a better starting point, the start time of all the tasks are randomly shifted within their allowed time ranges, the shifted tasks are scheduled with the EST again. Then the gradient descent algorithm is applied to further shift the tasks' start times, in order to find an enhanced solution. The procedure is repeated several times. The schedule with the minimal cost is the final solution. The performance of the proposed method is evaluated numerically, showing 1.3 to 10.5 times less cost than the EST, depending on the scenario. In addition, a full cycle of scheduling takes a few tens of milliseconds thus the method could be considered in real radar systems.