Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating image convolution filtering algorithms on integrated CPU–GPU architectures

Abstract. Convolution filtering is one of the most important algorithms in image processing. It is data-intensive, especially when dealing with high-definition images. Most previous studies on accelerating convolution computation in parallel focus on the use of graphics processing units (GPUs), whereas the central processing units (CPUs) always play the role of host to manage the data buffer and control flow. However, recent CPU architectures have seen significant modifications to parallel data computing capabilities, and the trend of integrating the CPU and GPU on a single chip is on a rise. We propose an approach to accelerate convolution filtering on the heterogeneous architecture of integrated CPU–GPU. We exploit the parallel processing power of vector instructions on a CPU and make it collaboratively function with the on-chip GPU. Two task assignment methods, static and dynamic task partitioning, are proposed for CPU–GPU collaboration. We evaluate our approach with images and filters of different sizes. The experimental results demonstrate that we can achieve 146 GFLOP/s at best using a quad-core CPU and the performance is 2.5 to 4.8 times faster than that of the single-GPU version of the OpenCV library. We also obtain 90 times speedup over the single-threaded CPU version. The results demonstrate that the proposed algorithm is efficient.
Optical Sensor Based Gestures Inference Using Recurrent Neural Network in Mobile Conditions

In this paper the implementation of recurrent neural network models for hand gesture recognition on edge devices was performed. The models were trained with 27 hand gestures recorded with the use of a linear optical sensor consisting of 8 photodiodes and 4 LEDs. Different models, trained off-line, were tested in terms of inference time on several mobile devices. The impact of different network topologies (different number of neurons and layers) and different effective sampling frequency of a recorded gesture on the inference time were evaluated. Inference time tests performed on the most effective of available platforms, Samsung Galaxy S8 smartphone, present that recurrent neural networks trained on unprocessed (raw) data from the gesture sensor (1-layer model) execute faster than models trained on differently processed features (2 and 3-layer models): 374ms vs 765ms and 1048ms for data recorded with 100Hz. Also, the reduction of effective sampling frequency allows to shorten the inference time, e.g., for raw data from 374ms at 100Hz to 115ms at 25Hz. Presented results confirm the usability of low complexity interfaces like linear gesture sensor in mobile devices with limited computational capabilities, which allows to consider such sensors in wearable electronic devices e.g. smart glasses.
Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records

This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records. We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning. We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately. Due to a reduced dimension of the subspaces, the overlearning is suppressed, and muscular artifacts are better separated. Once the muscular artifacts are removed, the signals in the individual subspaces are combined to provide an artifact free EEG record. We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal. The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.
T-PriDO: A Tree-based Privacy-Preserving and Contextual Collaborative Online Big Data Processing System

The emerging of big data era has urged the development of online big data processing systems. However, existing works seldom take the issue of privacy into account. In addition, the impacts of network structures among service providers are usually ignored. In this paper, we propose a cloud-based big data processing framework, where service providers are modeled as distributed cooperative learners predicting users’ preferences of items based on users’ contexts, while adapting the decision-making strategy based on users’ reward. We establish an item-cluster tree from top to the bottom to handle big data analysis. Service providers share information over a social network to complete collaborative learning. Considering the structure of the social networks among service providers, we also propose an adaptive algorithm to reduce the performance loss. Theoretical analysis shows that our proposal achieves sublinear regret and differential privacy of both network service providers and users. Experiments results validate that our proposed algorithms support increasing big datasets while strike a balance between privacy-preserving level and prediction accuracy.
A High-Bandwidth and Strong Robust Current Control Strategy for PMLSM Drives

As an ideal current control method, the superiority of the predictive current control (PCC) is seriously weakened by the one-step control delay and parameter mismatch issues. To acquire the high dynamic and static characteristics, this paper studies an improved PCC scheme for the permanent magnet linear synchronous motor (PMLSM) drives. First, the discrete-time model of PMLSM considering the parameter variation is established. Second, the formation mechanisms of these two issues are analyzed in detail, respectively. Meanwhile, through replanning the timing sequence logic, the one-step control delay issue is solved. Furthermore, based on the super-twisting algorithm, the super-twisting sliding-mode observer (STSMO) is constructed to compensate for the parameter disturbance. To further improve the dynamic property and lower the chattering, a third-order STSMO is presented in comparison with the second-order STSMO. In the meantime, the estimated currents can be utilized in the solution of one-step delay issue. At last, on the PMLSM testing platform based on the aerostatic guide, the corresponding experimental results are shown to verify the effectiveness and correctness of the proposed method.
Mycobacterial Cell Wall Synthesis Inhibitors Cause Lethal ATP Burst

Mycobacterial cell wall inhibitors interfere with targets involved in synthesis of mycolic acids, arabinogalactan and peptidoglycan. These antibiotics corrupt structural integrity of the cell envelope and this is believed to be the cause of drug mediated cell death. Here, we show that treatment of Mycobacterium bovis BCG with these mechanistically different classes of cell wall inhibitors at MIC caused a 4 to 5-fold increase in intrabacterial ATP concentration. This effect on ATP homeostasis was specific to inhibitors of cell wall synthesis and not observed for other anti-tuberculosis drugs. Treating M. bovis BCG with sub-MIC concentrations of the ATP synthase inhibitor bedaquiline or the uncoupler carbonyl cyanide 3-chlorophenylhydrazone suppressed drug induced ATP surge, suggesting that the increase in ATP concentration was due to increased oxidative phosphorylation. Pharmacological suppression of the ATP burst attenuated bactericidal activity of the cell wall-targeting drugs up to 100-fold, suggesting that increased ATP levels are associated with the lethal effect of these antibiotics. Interestingly, inhibition of the ATP burst also suppressed induction of the promoter of the cell envelope stress response operon iniBAC by cell wall inhibitors suggesting a link between ATP surge and iniBAC expression. In conclusion, we show that treatment of M. bovis BCG with inhibitors of cell wall synthesis causes a burst of intrabacterial ATP by increasing oxidative phosphorylation. This ATP surge appears to be required for induction of the iniBAC cell envelope stress response operon and to contribute to drug induced cell death. Hence, this work revealed links between inhibition of cell wall synthesis, oxidative phosphorylation, iniBAC induction and cell death. The identification of the molecular mechanisms linking these processes may reveal novel targets for the discovery of bactericidal antibiotics.
Improved Sliding Mode Observer for Position Sensorless Control of Permanent Magnet Synchronous Motor

This paper proposes an improved sliding-mode observer (SMO) for the sensorless control of permanent-magnet synchronous motor (PMSM). In the conventional SMO, a lowpass filter (LPF) is used to reduce the chattering caused by the signum function. That will bring the phase lag and affect the precision of the observer. The improved SMO adopts the self-adapting complex-coefficient filter (SACCF) to replace the LPF. The SACCF has no phase lag and amplitude attenuation at the mid-frequency. Thus, it can extract the back electromotive force (EMF) without distortions while suppressing the chattering. Then the improved SMO use the normalized phase-locked loop (PLL) to track the rotor position and speed accurately. Finally, the simulations and experiments are carried out with a 3kW PMSM, and the results verify the effectiveness of the proposed method.
Synchrotron-Based X-Ray Fluorescence Microscopy as a Technique for Imaging of Elements in Plants1[OPEN]

Synchrotron-based X-ray fluorescence microscopy (XFM) is a powerful elemental imaging technique now enabling a wide range of studies within plant sciences. Understanding the distribution of elements within plant tissues is important across a range of fields in plant science. In this review, we examine synchrotron-based x-ray fluorescence microscopy (XFM) as an elemental imaging technique in plant sciences, considering both its historical and current uses as well as discussing emerging approaches. XFM offers several unique capabilities of interest to plant scientists, including in vivo analyses at room temperature and pressure, good detection limits (approximately 1–100 mg kg−1), and excellent resolution (down to 50 nm). This has permitted its use in a range of studies, including for functional characterization in molecular biology, examining the distribution of nutrients in food products, understanding the movement of foliar fertilizers, investigating the behavior of engineered nanoparticles, elucidating the toxic effects of metal(loid)s in agronomic plant species, and studying the unique properties of hyperaccumulating plants. We anticipate that continuing technological advances at XFM beamlines also will provide new opportunities moving into the future, such as for high-throughput screening in molecular biology, the use of exotic metal tags for protein localization, and enabling time-resolved, in vivo analyses of living plants. By examining current and potential future applications, we hope to encourage further XFM studies in plant sciences by highlighting the versatility of this approach.
Novel Lattice Reduction Algorithms: Precoder Reduction and Vector Perturbation Tradeoffs

Lattice reduction (LR) has been extensively studied for data detection in uplink transmission. In this letter, we present a novel class of iterative LR algorithms in precoded downlink transmission. A unified framework is developed for this purpose, to reduce the precoder maximum row norm by iteratively minimizing the weighted sum of the precoder row-norms. We show that the resulting objective function can be minimized using the rounding-off technique or the sphere encoding (SE), traditionally used to determine the vector perturbation (VP) in downstream transmission. Different combinations of LR and VP techniques are investigated to assess their performance tradeoffs. Simulation results reveal that the proposed iterative SE-based LR outperforms the state-of-the-art Lenstra–Lenstra–Lovász and Seysen algorithms. In addition, the proposed algorithm shifts the computational complexity from the costly per-symbol VP computation to the per-channel-realization precoder reduction, which is beneficial in slowly varying channels.
Fast Energy Storage Systems Comparison in Terms of Energy Efficiency for a Specific Application

One of the key parameters to properly and accurately assess an energy storage system is the energy efficiency, which has a direct impact on the system performance and an indirect impact in its cost. In this paper, a methodology for comparing double-layer capacitors (EDLC) and kinetic energy storage systems (KESS) in terms of energy efficiency is proposed. This methodology, based on accurate loss models, takes real operating cycles into account, so a realistic result is obtained for each particular case. An accurate study of the losses of both technologies is accomplished during the paper, getting a complete model for EDLCs and KESSs, and obtaining efficiency maps for the whole range of operation points. Some conclusions about the convenience to use one or the other alternative and the best way to operate them are presented. In addition, a specific study case related to a wave energy power generation plant has been carried out in this paper, defining a methodology to select the energy storage requirements, calculating the round-trip efficiency and getting some recommendations related to the most appropriate operation strategy to take the most advantage of the energy storage system.
Wireless Stimulus-on-Device Design for Novel P300 Hybrid Brain-Computer Interface Applications

Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).
Levels of Resistance to Pyrethroid among Distinct kdr Alleles in Aedes aegypti Laboratory Lines and Frequency of kdr Alleles in 27 Natural Populations from Rio de Janeiro, Brazil

Background Several mutations in voltage gated sodium channel (NaV) have been identified in Aedes aegypti populations worldwide. However, only few are related to knockdown resistance to pyrethroids, most of which with variations in the 1016 and 1534 NaV sites. In Brazil, at least two NaV alleles are known: NaVR1, with a substitution in the 1534 (1016 Val+ + 1534 Ilekdr) and NaVR2, with substitutions in both 1016 and sites (1016Ilekdr + 1534Cyskdr). There is also the duplication in the NaV gene, with one copy carrying the substitution Ile1011Met, although its effects on pyrethroid resistance remain to be clarified. Our goals in this study were (1) to determine the role of each kdr NaV allele and the duplication on pyrethroid resistance and (2) to screen the frequency of the kdr alleles in 27 several natural Ae. aegypti populations from the metropolitan region of Rio de Janeiro. Methods Pyrethroid resistance was evaluated by a knockdown time (KdT) assay, an adaptation of the WHO test tubes with paper impregnated with deltamethrin. We used laboratory-selected Ae. aegypti lineages: R1R1 and R2R2 (homozygous for the kdr NaVR1 and NaVR2 alleles, respectively), Dup (with duplication in the NaV gene), Rockefeller (the susceptibility reference control), and F1 hybrids among them. Genotyping of both 1016 and 1534 NaV sites was performed in 811 Ae. aegypti sampled from 27 localities from Rio de Janeiro (17), Niterói (6) and Nova Iguaçu (4) cities, Rio de Janeiro State, Brazil, with a TaqMan real time PCR approach. Results The laboratory lineages R1R1, R2R2, and R1R2 were the only ones that needed more than 60 minutes to knock down all the insects exposed to the pyrethroid, being the KdT R2R2 > R1R2 > R1R1, corroborating the recessive nature of the kdr mutations. Frequency of kdr alleles NaVR1 and NaVR2 in field-caught mosquitoes varied from 0 to 52% and 43 to 86%, respectively, evidencing high levels of “resistant genotypes” (R1R1, R1R2, and R2R2), which together summed 60 to 100% in Ae. aegypti populations from Rio de Janeiro. Conclusions The NaVR1 and NaVR2 kdr alleles confer resistance to the pyrethroid deltamethrin in homozygotes and R1R2 heterozygotes, being the R2R2 most resistant genotype. The allele containing duplication in the NaV gene, with a mutation in the 1011 site, did not confer resistance under the tested conditions. The frequencies of the “resistant genotypes” are elevated in Ae. aegypti natural populations from Rio de Janeiro.
Smart Device based Initial Movement Detection of Cyclists using Convolutional Neuronal Networks

For future traffic scenarios, we envision interconnected traffic participants, who exchange information about their current state, e.g., position, their predicted intentions, allowing to act in a cooperative manner. Vulnerable road users (VRUs), e.g., pedestrians and cyclists, will be equipped with smart device that can be used to detect their intentions and transmit these detected intention to approaching cars such that their drivers can be warned. In this article, we focus on detecting the initial movement of cyclist using smart devices. Smart devices provide the necessary sensors, namely accelerometer and gyroscope, and therefore pose an excellent instrument to detect movement transitions (e.g., waiting to moving) fast. Convolutional Neural Networks prove to be the state-of-the-art solution for many problems with an ever increasing range of applications. Therefore, we model the initial movement detection as a classification problem. In terms of Organic Computing (OC) it be seen as a step towards self-awareness and self-adaptation. We apply residual network architectures to the task of detecting the initial starting movement of cyclists.
Hardware-Assisted Secure Communication in Embedded and Multi-Core Computing Systems

With the sharp rise of functionalities and connectivities in multi-core embedded systems, these systems have become notably vulnerable to security attacks. Conventional software security mechanisms fail to deliver full safety and also affect the system performance significantly. In this paper, a hardware-based security procedure is proposed to handle critical information in real-time through comprehensive separation without needing any help from the software. To evaluate the proposed system, an authentication system based on an image procession solution has been implemented on a reconfigurable device. In addition, the proposed security mechanism is evaluated for the Networks-on-chips, where minimal area, power consumption and performance overheads are achieved.
Compact S Band Antenna for CubeSat

In this work, a compact antenna design that can be used in space communication is presented. This antenna element has a height of less than 6 mm and can be used for space application due to its excellent axial ratio in a half-sphere radiation space and good bandwidth performance. The element is compact occupying just 28 × 28 mm and has a dual band operation for the duplex S band satellite to earth communications. Each band has 80–90 Mhz matching bandwidth and covers the communications channels allocated for the low earth orbit satellites in S band. The antenna has an efficiency of around −1 dB across the two bands and it is easy to manufacture with lightweight materials. In addition, this design simplifies the front end architecture and minimizes loses. Compared with literature is the most compact element for the given bandwidth.
Optimal Analysis and Design of DC-DC Converter to Achieve High Voltage Conversion Gain and High Efficiency for Renewable Energy Systems

High conversion gain of voltage is generally required to interface various renewable energy sources, such as PV modules. This paper focuses on the optimal analysis and design of non-isolated DC-DC converters to meet the high-step-up gain requirement and achieve high efficiency. The proposed topology utilizes the coupled inductor technique to achieve high step-up voltage conversion ratio. A power loss model is developed to identify losses in each component for efficiency enhancement. The switch has relatively low voltage stress since leakage energy is directly transferred to the output to avoid voltage spikes across it. In addition, the coupled inductor alleviated the reverse recovery issue of the diode. The circuit operation and steady-state analysis of the proposed converter are presented in detail. A prototype circuit is built and tested to prove the circuit analysis and optimal design.
A Test Path Generation Method Based on SSM Models

The traditional finite state machine (FSM) cannot express complicated systems due to its flattened sequential features. A safe state machine (SSM) is an extension of FSMs with capability of expressing hierarchical structure, parallel structure and historical mechanism. Therefore, it can accurately express complicated functions of a system, and SSMs have strict formal semantics. This paper provides a method for generating test paths based on SSMs. A method of creating the reachability graph (RG) of an SSM is presented, which can eliminate the hierarchical and parallel structure, and mark the history states. Finally, test paths are generated based on certain test coverage criteria of the RG mode.
The efficacy of oral acyclovir during early course of pityriasis rosea: a systematic review and meta-analysis

Abstract Background: Acyclovir has been reported as a potential therapy for pityriasis rosea (PR) in several clinical trials on the basis of evidence of the involvement of human herpes viruses 6 and 7. Objective: We evaluated the efficacy of acyclovir for abating PR skin lesions within a fixed period. Methods: We searched 4 databases for clinical trials that used oral acyclovir to treat PR and performed systematic review and meta-analysis to determine oral acyclovir’s effect on skin lesions on the 14th day after commencing treatment. Results: Five clinical trials including four randomized controlled trials were identified that compared the effects of oral acyclovir (n = 133) and nonacyclovir (n = 140) in patients with PR. Oral acyclovir significantly reduced erythema (odds ratio [OR] 11.30; 95% CI = 5.70–22.41; p < .01) and limited lesion formation (OR 8.67; 95% CI = 3.29–22.81; p < .01) compared with nonacyclovir treatment on the 14th day. These results were in agreement with the results of subgroup analysis of only high-dose oral acyclovir treatment and randomized controlled trials. Conclusion: Oral acyclovir may be a relatively safe and effective treatment in the early course of PR, and patients with PR may achieve faster symptoms control with acyclovir.