Miracle-dz/UniFAR-FTUs huggingface dataset:
Range and natural history of point-tailed palmcreepers (Aves: Furnariidae)

The point-tailed palmcreeper Berlepschia rikeri is a distinctive Furnariidae species, very shy and poorly known. In this paper we present natural history notes on this species as well as a detailed review of its range. This paper summarizes anecdotal observations gathered during 10 years of ornithological investigation of the central Brazilian Cerrado, as well as data obtained in 13 Brazilian and overseas collections and from online databases. We found that B. rikeri is widely distributed across north and central South America, where it is strictly associated with palm groves, especially those of Mauritia spp. and Orbignya phalerata. Despite being a habitat specialist, this species can be found in a wide spectrum of vegetation formations, from lowland rainforests to high plateau grasslands. This is an insectivorous species, which constructs a stick nest concealed within the fronds of Mauritia palms. Its juvenile plumage is similar to that of the adult.
Virtual Relay Selection in LTE-V: A Deep Reinforcement Learning Approach to Heterogeneous Data

The development of Long Term Evolution (LTE) enables wireless communication with high transmission rate, low latency, and wide coverage area. These outstanding features of LTE support the next generation of vehicle-to-everything (V2X) communication, which is named LTE-V. Among the various technologies in LTE-V, placing relay nodes on vehicles is a promising approach to save power and energy, and extend the transmission range. In this paper, we consider the virtual relay node selection problem. In the problem, a base station transmits data to a vehicle relay (also known as helper) who will further disseminate the received data to the vehicular subscribers nearby. The selection of the vehicle relay node is a challenging issue since the utility of the selection can only be known after this action has been made. Another challenge of this problem is that the traditional pure optimization is inapplicable due to the imperfect information available. Motivated by the recent success of Alpha Go Zero, we employ deep reinforcement learning (DRL) as a powerful tool for facing the above challenges and solving the problem without global information. We build a bridge between the traffic information and decision of relay node selection based on the reality that the utility of vehicle relay is highly correlated with the traffic density. In our work, deep convolutional neural networks are first applied to extract traffic patterns and then learn the traffic and network topology. Deep learning (DL) acts as a role to map features inside traffic and communication topology to the decisions. Then Q-Learning dynamically updates the utilities of decisions by trials in the environment. Finally, the overall rewards can be calculated to measure these decisions, and the Q function is also updated. Simulation results based on real traffic data validate that our proposed approach can achieve high utility performance.
Addressing Resource Contention and Timing Predictability for Multi-Core Architectures with Shared Memory Interconnects

Multi-core architectures are increasingly being used in real-time embedded systems. In general, such systems have more processors than the shared memory modules, potentially causing severe interference over memory accesses. This resource contention could lead to substantial variation on memory access latencies, and thus wide fluctuation in the overall system performance, which is highly undesirable especially for the time-critical applications. In this paper, we address resource contention and timing predictability for multi-core architectures with distributed memory interconnects. We focus on the locally arbitrated interconnect constructed by pipelined multiplexing stages with local arbitration, while the globally arbitrated interconnect employing global scheduling to the same architecture potentially suffers synchronisation issue and requires strict coordination. Our contributions are mainly threefold: (i) We analyse the resource contention across the memory access data path, and report the accurate calculational method to bound the worst-case behaviour. (ii) We compare the average-case behaviour of the locally arbitrated and the globally arbitrated architectures with experiments, demonstrating varying memory latencies caused by the resource sharing issue. (iii) We propose an architectural modification to smooth resource sharing. Evaluations on simulators and FPGA implementations with synthetic memory workload show that the latency variation is significantly reduced, contributing towards timing predictability of multi-core systems.
4x4 Multiplier Implementation using Gate Diffusion Input

Addition is a basic component for any operation commonly used in many VLSI systems, such as implementation-specific digital signal processing (DSP) and integrated circuit architecture. Another two important criteria for the design of complete adders would be power consumption and rpm. Full adder architecture with low power consumption and fast latency of more transmission is of great concern in the implementation of modern digital systems. To achieve maximum architecture trade-offs, power delay device or energy consumption per process was added. Digital circuit performance can be optimized through proper logic style selection. In this paper, we design an extended multiplier low power consumption adders to minimize complexity and reduce power consumption compared to other designs. This can increase the speed and reduce the dissipation of power. A newer GDI (Gate Diffusion Input) transmission gate was used to implement the adder configuration. This technology allows reducing the power consumption, delay and area of low-power digital circuits thus dealing with low logic design difficulties.
Comparative analysis of machine learning algorithms for identification of BOT attack on the Internet of Vehicles (IoV)

In this digital era, technology is upgrading day by day and becoming more agile and intelligent. Smart devices and gadgets are now being used to find solutions to complex problems in various domains such as health care, industries, entertainment, education, etc. The Transport system, which is the biggest challenge for any governing authority of a state, is also not untouched with this development. There are numerous challenges and issues with the existing transport system, which can be addressed by developing intelligent and autonomous vehicles. The existing vehicles can be upgraded to use sensors and the latest communication techniques. The advancements in the Internet of Things (IoT) have the potential to completely transform the existing transport system to a more advanced and intelligent transport system that is the Internet of Vehicles (IoV). Due to the connectivity with the Internet, the Internet of Vehicles (IoV) is exposed to various security threats. Security is the primary issue, which requires to be addressed for success and adoption of the IoV. In this paper, the applicability of machine learning based solutions to address the security issue of IoV is analyzed. The performance of six machine-learning algorithms to detect Bot threats is validated by the k-fold cross-validation method in python.
Subglacial Lakes, Drainage Pathways and Basins Beneath the Antarctic Ice Sheet Based on the Remote Sensing Big Data

Antarctic subglacial lakes which form an important component of the subglacial hydrological system have the potential to study the history of the ice sheet and the global climate change. In order to detect the potential subglacial lakes beneath the Antarctic ice sheet. A dynamic threshold filtering algorithm has been applied on the CryoSat-2 satellite altimetry remote sensing data to derive the Antarctic potential subglacial lakes including the quantities, areas as well as drainage pathways. And we have compared the difference of the experimental results obtained by CryoSat-2 and Bedmap2 datasets. Our results show the superiority by using the CryoSat-2 data, which have filled the gap in the central area of the Antarctic. Besides CryoSat-2 Altimetry data can generate a rather accurate DEM of the Antarctic ice sheet by this algorithm. As a result, we have detected many more lakes covering about 3% of the total bed of Antarctic than were observed before. There are some direct hydrological connections between a large number of subglacial lakes beneath the Antarctic ice sheet.
The Oenocytes of Tenebrio

While studying the odoriferous glands of the adult Tenebrio molitor L. and those of T. obscurus Fab. , interesting groups of cells were found (in both sexes) in close association with the fat bodies and tracheal system. From their structure and position they are undoubtedly oenocytes. The author feels that, because of their unusual size and number, they should be brought to the attention of other workers.
Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages–estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).
A Multi-Frequency Patch Antenna With Double Sided Parallel Strip Line Periodic Structure

A multi-frequency patch antenna with double-sided parallel-strip line (DSPSL) periodic offset structure is proposed. The designed antenna consists of a simple rectangular patch as well as an embedded one-dimensional DSPSL periodic offset structure, which is employed to replace the traditional rectangular microstrip patch antenna. The periodic DSPSL structure is characterized leaky wave structure. After the periodic DSPSL structure is embedded, the resonance frequency of rectangular patch is offset, and the leaky wave characteristics can determine the number and position of new operating frequencies. As an application, a tri-band antenna is manufactured and these three operating frequencies can be calculated by the derived formulas studied in this communication. Experiment results suggest that the antenna can work at 4.28, 4.78, and 6.42 GHz, respectively. The results of simulated, measured and calculated show high consistency.
Illegal Angling Harvest of Walleyes Protected by Length Limits in Alberta

Abstract The sport fishery for walleyes Stizostedion vitreum in Alberta, Canada, has declined severely because of high angling pressure relative to the low productivity of northern fisheries. Following extensive public consultation, highly restrictive length and bag limits were implemented on all walleye fisheries, clearly increasing the opportunities and temptations for anglers to illegally harvest fish. An estimate of illegal harvest was derived from data gathered at 29 sport fisheries in Alberta from 1991 to 1998. The technique involved counting protected-length fish in anglers' creels and comparing these numbers to the numbers of protected-length fish released. I avoided anglers' exaggeration of released walleye numbers by comparing the ratios of the lengths of fish caught by test angling to those of fish retained and reported by anglers, as recorded by creel clerks. The average illegal harvest level was high; 18.4% (range = 0.2–68.8%) of protected-length walleyes caught were kept. Illegal harvest was.
On the Extinction–Free Stabilization of Predator-Prey Dynamics

Scientists have long been attracted to mechanisms surrounding the predator–prey system. The Lotka–Volterra (LV) model is the most popular formalism used to investigate the dynamics of this system. LV equations present non-linear dynamics that exhibit periodic oscillations in both prey and predator populations. In practical situations, it is useful to stabilise the system asymptotically to a desired set point (population) wherein the two species coexist by fashioning specific control actions. This control strategy can be beneficial for problems that can arise when there is a risk of extinction of one of the species and human intervention must be planned. One natural and well-established theory for describing systems obeying energy balance laws is the port-Hamiltonian modeling, an extension of classical Hamiltonian mechanics to systems endowed with control and observation. The LV model can be formally represented as a non-linear mechanical oscillator employing the canonical equations of Hamilton. This special mathematical structure aids planning and designing efficient control actions. The proposed strategy employs a systematic procedure to efficiently plan biological control actions and bypass species extinction through asymptotic stabilisation of populations.
ElasticWISP: Energy-Proportional WISP Networks

The provision of rural broadband infrastructure is a challenge for network operators across the globe, irrespective of their size. Wireless Internet Service Providers (WISPs) have shown that the small scale deployment of wireless broadband infrastructure is a viable alternative to relying on cellular network providers for remote coverage. However, out of concern for excessive energy consumption WISPs must often resort to using off-grid renewable energy sources such as solar energy for powering network sites, often resulting in undesirable, low-performance backhaul radios being used between sites. To encourage the development of high-performance, sustainable WISP networks, we present ElasticWISP, a backhaul optimisation architecture. ElasticWISP dynamically controls the configuration of backhaul radios based on bandwidth demands and the network-wide energy consumption of these radios. Through simulations driven by real WISP topology and data traffic, we show that ElasticWISP can offer energy savings of approximately 65% when following our design methodology.
Frequency Reconfigurable Microstrip Patch Antenna for IRNSS Applications

This paper presents a compact frequency reconfigurable antenna for IRNSS applications. The proposed antenna can switch the frequency between L5-band and S-band. The reconfigurability for the designed antenna is achieved by incorporating RF PIN (three) diode between the radiating element and resonance slot. The proposed antenna is resonated at 1.1753 GHz (1.16 GHz to 1.19 GHz) and 2.4895 GHz (2.47 GHZ to 2.51 GHz) with ON and OFF condition of PIN diodes, respectively. It is compact and low profile structure with maximum gain of 8.51 dBi with low cross polarization (CP) of approximate < −30 dB.
Robust Dynamic Hand Gesture Interaction using LTE Terminals

Device-free hand gesture is one of the most natural ways to interact with everyday objects. However, existing WiFi-based gesture recognition solutions are typically restricted to indoor environments due to limited outdoor coverage. Furthermore, to achieve high sampling rates, they may interfere with normal data transmissions. In this paper, we aim to develop a robust dynamic gesture interaction system that can be ubiquitously deployed using Long-term Evolution (LTE) mobile terminals. Through both empirical studies and in-depth analysis using the Fresnel zone model, we reveal the key factors that contribute to the repeatability and discernibility of gestures. We show that the optimal location and orientation to perform gestures indeed exist and can be identified without prior knowledge of the position of LTE base stations (BSs) relative to a terminal. Guided by the design principles derived from Fresnel zone characteristics around a 4G terminal, we design highly repeatable and discernible gestures with salient received signal profiles. A gesture interaction system has been developed and implemented to achieve robust recognition with this careful design. Extensive experiments have been conducted in both indoor and outdoor environments, for different relative placements of mobile terminal and BS, and with different users. The proposed system can automatically identify the direction of BSs with a median error of less than 15 degrees and achieve gesture recognition accuracy as high as 98% in all scenarios without the need to acquire any training data.
Effect of Shifting Gate Pulse on Average Currents in Coupled SIDO Boost Converter

The coupled inductor single input dual output (CI-SIDO) boost converter can supply two outputs from a single input. Compared to two single input single output (SISO) converters, CI-SIDO boost converters have reduced component count, reduced losses, and increased efficiency. These converters have applications in hybrid energy systems, electric vehicles, portable electronic devices like mobiles, cameras, etc. The average currents of these converters are an important factor to be considered. The average value of the current decides the copper loss associated with the device. If the average value increases, the peak currents may increase, thus increasing the current ratings of the MOSFETs and diodes of the converter. In the CI-SIDO boost, the shifting of gate pulses is done to reduce the ripples in the inductor current. However, the shifting of gate pulses changes the inductor current waveforms. The change in waveforms changes the average values of inductor currents, input current, diode currents, and MOSFET currents. The effect of shifting gate pulses on average values of currents has not been investigated. This paper finds the effect of shifting gate pulses on the average values of currents in CI-SIDO boost converters. The analysis is verified by simulations in MATLAB/Simulink.
Sparse Spatial and Temporal Estimation for Multipath Mitigation in GNSS

The multipath signals will degrade the tracking performance and increase the positioning errors of the Global Navigation Satellite System (GNSS). Superior multipath mitigation can be obtained by jointly estimating the angles of arrival and delays of both the line of sight signal and the multipath signals. In to do so, this paper proposes the use of the multiple Bayesian learning (MSBL) method together with the joint angle and delay estimation technique in GNSS multipath scenarios. Moreover, to further enhance the resolution, off-grid estimation is adopted to delay while on-grid estimation is kept for angle to reduce the complexity. Simulation results are presented to evaluate the performance of the proposed joint on-grid angle and off-grid delay estimation based on MSBL algorithm under several multipath scenarios and it is shown to outperform existing methods even in the most difficult cases of spatially correlated multipath signals and low carrier-to-noise ratio.
A Novel and Adaptive Transient Fault-Tolerant Algorithm Considering Timing Constraint on Heterogeneous Systems

Due to high performance and low power consumption, heterogeneous processors are widely used in many real-time systems. In these systems, if tasks are not completed before deadline, it will cause disastrous consequences, and thus it is important to provide fault-tolerance. This paper proposes a novel, adaptive and transient fault-tolerant scheduling algorithm to solve the fault-tolerant problem in heterogeneous real-time systems, aiming to improve system reliability within a given deadline. Since task replication is efficient in improving system reliability, the proposed algorithm supports multiple replicas for each primary task and allows the primary tasks and their replicas to be scheduled on the same processor to increase reliability and lower latency. Also, the algorithm can dynamically adjust the number of replicas for each task to accommodate the deadline and ensure higher reliability. Simulated results show that the proposed algorithm can achieve higher reliability in comparison with existing and related fault-tolerant algorithms. To be specific, the proposed algorithm can obtain the reliability of 89.37% whereas the two existing algorithms DB-FTSA and FTSA obtain the reliability of 47.05% and 84.75% for the benchmark of sixty tasks, respectively, to be detailed in Fig. 4 in experiment.
Evaluation of Virtual Reality Tracking Performance for Indoor Navigation

HTC Vive VR system was originally designed for entertainment. It is capable of high-accuracy indoor tracking and is relatively inexpensive compared to other professional indoor tracking systems. Recently, there is an increasing trend to use HTC Vive tracking system towards scientific research and educational applications. However, the tracking accuracy of position and orientation of a target can be significantly affected by the configuration of lighthouses (base stations). In this work, we proposed a simplified error model of the target position and orientation, which takes into consideration the lighthouse configuration, the relative location of the target, and the accuracy of the sensors. It can be used to optimize or evaluate the geometric distribution of lighthouses and receivers for specific applications.
An assessment of MPEG-7 visual descriptors for images of maize plagues and diseases

Feature description is a fundamental process in the analysis of images for content-based retrieval and classification, among other tasks in which the image feature descriptor should have enough discriminative power to differentiate similar from dissimilar images according with a distance measure. Although several descriptors have been proposed for a variety of images, the challenge is their suitability to solve these tasks efficiently. One proposal is the set of standard MPEG-7 visual descriptors. We address their suitability to efficiently describe plagues and diseases in images of maize plants. The importance of this crop is its worldwide relevance for human and animal consumption. Experiments for similarity search queries using a set of distance measures, show that the Color Structure Descriptor with the Bray-Curtis distance is the most efficient and provides around 68% precision in most cases.
Paddy: An Event Log Parsing Approach using Dynamic Dictionary

Large enterprise systems often produce a large volume of event logs, and event log parsing is an important log management task. The goal of log parsing is to construct log templates from log messages and convert raw log messages into structured log messages. A log parser can help engineers monitor their systems and detect anomalous behaviors and errors. Most existing log parsing methods focus on offline methods, which require all log data to be available before parsing. In addition, the massive volume of log messages makes the process complex and time-consuming. In this paper, we propose Paddy, an online event log parsing method. Paddy uses a dynamic dictionary structure to build an inverted index, which can search the template candidates efficiently with a high rate of recall. The use of Jaccard similarity and length feature to rank candidates can improve parsing precision. We evaluated our proposed method on 16 real log datasets from various sources including distributed systems, supercomputers, operating systems, mobile systems, and standalone software. Our experimental results demonstrate that Paddy achieves the highest accuracy on eight data sets out of sixteen datasets compared to other baseline methods. We also evaluated the robustness and runtime efficiency of the methods and the experimental results show that our method Paddy achieves superior stableness and is scalable with a large volume of log messages.