Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Background EEG Activity after Creative Task in Aged Adults

Cognitive training is considered as an effective intervention for slowing cognitive decline, however single-domain training programs have shown limited transfer to untrained tasks. Complex cognitive training has a potential to yield broader transfer effect. Given that creative thinking involves variety of cognitive subprocesses, creative tasks may be useful as a complex training paradigm. In line with this assumption, creative engagement has a neuroprotective effect in older subjects, improving mental health and well-being. However, neural mechanisms underlying the effect has remained unknown. Evidence suggests that post-task changes in resting-state brain activity may be detected in regions involved during training. The current study was aimed to reveal changes in electroencephalographic (EEG) activity during creative task performance and to identify whether those activity patterns persist in the baseline EEG after the end of a creative session. EEG was registered in 25 elderly ($64\pm 6$ years) high-functioning subjects before, during and after creative problem solving, consisting of 30 verbal and 30 figural tasks. Current source density estimates and subsequent statistical contracts using statistical non-parametrical mapping (SnPM) were calculated via LORETA package. We found a significant increase of spectral density from pre- to post-task interval in theta-gamma frequency bands. The effect was localized in parieto-occipital cortex, overlapping with the region showing synchronization during the tasks performance. Observed changes in resting-state EEG activity may be considered as an indicator of use-dependent neuroplasticity.
Design of Controllers for Higher Order Systems

A method of parametric synthesis of the PID controller of the control system for high-order objects based on the modal method for linear systems with the location of complex-conjugate poles is proposed. The methodology provides for inspection of the controller parameters according to the algebraic criterion of stability of Routh-Hurwitz to change all positive values. On the example of designing a PID controller of the control system for the object of the fourth order, the efficiency of the technique is demonstrated
D&D: A Distributed and Disposable Approach to Privacy Preserving Data Analytics in User-Centric Healthcare

Benefiting from the modern information and communication technologies, user centricity has become a clear evolution trend in healthcare. Unfortunately, given the high sensitivity of health data and the uncertainty in user environments, user-centric healthcare systems inevitably suffer from more frequent privacy threats, not to mention that technologies and business of data exploitation have generally outpaced the current privacy regulations and laws. Although there exist well-defined privacy preserving mechanisms, such as Data Encryption, Data Perturbation, and De-identification, they have been considered inadequate to address the diverse privacy challenges in big healthcare data analytics. Our argument is that, before considering any sophisticated mechanism, practitioners should first try to imitate human memory's forgetting process as an intrinsic privacy preserving strategy in user-centric healthcare. Technically, we implement this strategy by changing traditional data analytics routines into a distributed and disposable manner, so as to naturally exclude the data owners' sensitive information. The technical implementation essentially acts as a concrete How-To solution to satisfying a fundamental principle of privacy law, i.e. data minimization. We have initially applied our work to a smart bed project for sleep quality analytics, and received positive feedback on the effectiveness of privacy preservation in suitable homecare scenarios.
Linear Controller Synthesis for Master System using Extended Physiological Proprioception in Upper-Limb Prosthesis

A Linear Controller is proposed for Master system in Upper-Limb Prosthesis using Extended Physiological Proprioception (EPP). The implemented design aims to provide fair tracking for the Master-Slave system. The control law is designed using a novel, Single-motor Master system using PID for different values of design parameters such as settling time and peak overshoot. A second order of system is used for the control synthesis. Simulation results show that control law works satisfactory, even in presence of non-linear friction due to lead-screw mechanism. A further comparison of simulation results between Single and Double motor Master system shows that Single Master scheme is superior in terms of energy consumption and torque requirement.
Investigation of the Structural, Magnetic, Dielectric, and Optical Properties of Mn and Co-Doped BiFeO3 (Bi1–xCoxFe0.8Mn0.2O3) Nanoparticles

In this article, Bi<sub>1–x</sub>Co<sub>x</sub>Fe<sub>0.8</sub>Mn<sub>0.2</sub>O<sub>3</sub> (BCFMO) nanoparticles with x = 0.0, 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30 were synthesized at 650°C and annealed for 1 h by the sol–gel method. Then, the structural, magnetic, dielectric, and optical properties of BCFMO nanoparticles were investigated with the substitution of cobalt and manganese elements for bismuth and iron under various percentages. X-ray diffraction (XRD), Fourier transform-infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), and ultraviolet (UV)–visible spectrophotometer were used to characterize the prepared samples. By using <italic>LCR</italic> meter, the dielectric constant (<inline-formula> <tex-math notation="LaTeX">$\varepsilon ^{\prime }$ </tex-math></inline-formula>), the dielectric loss factor (tan <inline-formula> <tex-math notation="LaTeX">$\delta$ </tex-math></inline-formula>), the dielectric loss (<inline-formula> <tex-math notation="LaTeX">$\varepsilon ^{\prime \prime }$ </tex-math></inline-formula>), and the electrical conductivity amounts of ac (<inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {ac}}$ </tex-math></inline-formula>) for BCFMO nanoparticles were measured at the annealing temperature of 650°C for 1 h in the frequency range of 100 Hz–5 MHz.
Jitter Suppression Techniques for High-Speed Sample-and-Hold Circuits

In this paper, we discuss the limitations of the existing clock jitter reduction techniques, and introduce <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> sampling, which can reduce the jitter-induced sampling error significantly. We investigate various feedback configurations, and show that NRZ and RZ feedback techniques both improve the jitter shaping properties of the <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> sampler. However, in order to benefit from the superior jitter shaping, a constant feedback pulsewidth is required. We propose a new clocking scheme, called <italic>correlated clocking</italic> to address the feedback pulsewidth variations. This technique utilizes the correlation between the rising and falling edges of the on-chip clocks to eliminate the feedback pulsewidth jitter. We show that a <inline-formula> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula>-order <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> sampler with correlated RZ feedback can achieve the same SJNR as a <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> sampler with switched-capacitor feedback. We then expand our analysis to <inline-formula> <tex-math notation="LaTeX">$2^{nd}$ </tex-math></inline-formula>- and higher-order modulators, and show that contrary to common belief, even with constant-pulsewidth feedback, higher-order modulators are limited by the feedback pulse jitter. We conclude that the maximum benefit of a <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> sampler with a conventional single-loop architecture is independent of the loop order, feedback mechanism, and the oversampling ratio, and it reaches 4.77 dB for OSR≥5.
Utilization of UAV technology for vegetation cover mapping using object based image analysis in restoration area of Gunung Halimun Salak National Park, Indonesia

Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy.
Updating Data-Center Network With Ultra-Low Latency Data Plane

Due to the asynchronous and distributed nature of the data plane, the transition from the initial to final state may result in serious transient congestion in software-defined networking (SDN). Moreover, with the rapid development of ultra-low latency network in data-centers, the control loop between the control and data plane becomes much longer than the ultra-low latency communication in the data plane. In this case, traffic surges significantly in the data plane during the network reconfiguration process, and it becomes harder for the SDN controllers to manipulate the update operations as they expect. In this paper, we consider the traffic variation during the network update and model it as a novel minimum demand violation problem (MDVP). Later, we prove its hardness, and propose a heuristic approximation algorithm to approach the optimal result. Our method brings flexibility for network operators to make a trade-off between the network congestion and update speed. Experiments show that our method can halve the intermediate network states and reduce the demand violation ratio by 36.7 % compared to the state-of-the-art.
Test Transfer Across Mobile Apps Through Semantic Mapping

GUI-based testing has been primarily used to examine the functionality and usability of mobile apps. Despite the numerous GUI-based test input generation techniques proposed in the literature, these techniques are still limited by (1) lack of context-aware text inputs; (2) failing to generate expressive tests; and (3) absence of test oracles. To address these limitations, we propose CraftDroid, a framework that leverages information retrieval, along with static and dynamic analysis techniques, to extract the human knowledge from an existing test suite for one app and transfer the test cases and oracles to be used for testing other apps with the similar functionalities. Evaluation of CraftDroid on real-world commercial Android apps corroborates its effectiveness by achieving 73% precision and 90% recall on average for transferring both the GUI events and oracles. In addition, 75% of the attempted transfers successfully generated valid and feature-based tests for popular features among apps in the same category.
Feature-Matching Speech Denoising GANs via Progressive Training

In the single-channel speech enhancement, generative adversarial networks (GANs) have been proved to be effective in eliminating noise, and improving the intelligibility and quality of speech. However, in low signal noise ratio (SNR) environments, high noise reduces the quality of speech largely, it's challenging to remove the noise directly from the noisy speech. Here, we propose a feature-matching speech denoising GANs method via progressive training for low SNR. Our approach decomposes the challenging task into several simpler tasks by progressively increasing the SNR of data and the depth of the networks simultaneously. Through this training approach, the networks can reconstruct the finer distribution of clean speech. Meanwhile, we combine a feature-matching strategy based on the discriminator and a traditional feature-mapping method to reduce the discrepancy between the distribution of the enhanced features and the clean features. The networks are jointly optimized to minimax the loss between the generator and the discriminator. Evaluated on the AISHELL-2 dataset, the proposed GANs achieve a relative error rate (WER) improvement of 7.50% on the ASR-Clean model trained by clean data, and a relative improvement of 8.03% on the ASR-MTR model trained by the multi-style training. In STOI and PESQ, there are 4.69% and 10.67% relative improvements, respectively.
A New Wireless EV Charging System With Integrated DC–DC Magnetic Element

A dc–dc conversion stage is commonly used for a wireless electric vehicle (EV) battery charging system. The dc–dc stage requires a bulky inductor to charge the battery either in constant voltage (CV) or constant current (CC) modes. In this article, a new magnetic structure for the wireless EV charging system is proposed to integrate the dc–dc inductor with the receiver coil on the vehicle side. The proposed structure utilizes the existing core material in the wireless power circuit pads and provides a more compact design and an efficient wireless charger system for EV applications. The proposed magnetic structure is modeled in 3-D, and the finite-element analysis (FEA) results are presented and compared with measurements. Moreover, the effect of the proposed integration method on the wireless charging system performance is investigated, and the results are presented. A 3.3-kW/85-kHz wireless charger is optimally designed and built to evaluate the proposed configuration. Furthermore, the efficiency and the output voltage of the wireless charging system are also measured and compared with the simulation results. The simulation and experimental results show the performance of the proposed structure.
Real-time Distance Measuring Evaluation for Accelerometer Sensor on Smartphone

Nowadays, technology for smart phones has adopted sensors for detecting various movements such as gyro, accelerometer compass and GPS. The sensors that are used to measure distance are accelerometer and GPS. For measuring distance, accelerometer will provide more details or information than GPS, in the other hand, GPS has some restrictions, such as points, signals, data resolution and sampling rate. For the advantage of the accelerometer is it can work at all times, but the accelerometer can cause errors. Calculating distance by accelerometer must use a process that helps to measure the distance. This paper provides a method for measuring distance using the accelerometer from the smart phone and shows the results of the comparison of distance measurements by various methods.
Generating Block IO Trace Data from a Cloud Site Using Packet Capture and Analyzing the IO Trace Data

To acquire storage workload, I captured whole packets between IA servers and network storage systems in one cloud site, and obtained block IO trace data by analyzing these captured packets. The cloud site consisted of about 100 physical servers and each physical server had some virtual machines (VMs). There were 2,000–3,000 virtual disks attached to the VMs. In this paper, I describe the following: 1. A method for generating block IO trace data from the captured packets from the cloud site using plural packet capture servers. 2. An analysis report from whole IO trace data, comparing this trace data and other trace data in public repositories
Artificial Neural Network Based Load Balancing On Software Defined Networking

Cloud computing is an emerging area where all the network services are provided in the form of pay per use. Utilizing the available resources efficiently is one of the challenge in the data center network. Cloud resources are given as virtual machine to the end users. Sometimes, Virtual Machines (VMs) are overloaded or underloaded. The overloaded VMs degrade the performance of applications and underloaded VMs lead to inefficient resource utilization. To effectively utilize the available resources, we have proposed Dynamic Agent-Based’Load Balancing (DA-LB) on Software-Defined Networking (SDN) using Back Propagation Artificial Neural Network (BPANN). This load balancing algorithm uses the global visibility of SDN to efficiently migrate virtual machines in the data center network. The results show that our SDN load balancing approach improves the overall network efficiency and works well on data migration.The proposed algorithm helps to increase the processing speed and predict the loaded VM in heavy load conditions and optimizes the resource utilization. The migration process results are compared with Multi-Path TCP (MPTCP) and Heuristic algorithm (HA) and it is seen that our proposed algorithm takes less migration time compared to MPTCP and HA.
Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network

Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly contain various types of ground objects and complex backgrounds, most of methods cannot focus on saliency features of scene, which limits the classification performances. To address this issue, we propose a novel Saliency Dual Attention Residual Network (SDAResNet) to extract both cross-channel and spatial saliency information for scene classification of RSI. More specifically, the proposed SDAResNet consists of spatial attention and channel attention, in which spatial attention is embedded in low-level feature to emphasize saliency location information and suppress background information, and channel attention is integrated to high-level features to extract saliency meaningful information. Additionally, several image classification tricks are used to further improve classification accuracy. Finally, Extensive experiments on two challenging benchmark RSI datasets are presented to demonstrate that our methods outperform most of state-of-the-art approaches significantly.
RSU-Assisted Traffic-Aware Routing Based on Reinforcement Learning for Urban Vanets

In urban vehicular ad hoc networks (VANETs), the high mobility of vehicles along street roads poses daunting challenges to routing protocols and has a great impact on network performance. In addition, the frequent network partition caused by an uneven distribution of vehicles in an urban environment further places higher requirements on the routing protocols in VANETs. More importantly, the high vehicle density during the traffic peak hours and a variety of natural obstacles, such as tall buildings, other vehicles and trees, greatly increase the difficulty of protocol design for high quality communications. Considering these issues, in this paper, we introduce a novel routing protocol for urban VANETs called RSU-assisted Q-learning-based Traffic-Aware Routing (QTAR). Combining the advantages of geographic routing with the static road map information, QTAR learns the road segment traffic information based on the Q-learning algorithm. In QTAR, a routing path consists of multiple dynamically selected high reliability connection road segments that enable packets to reach their destination effectively. For packet forwarding within a road segment, distributed V2V Q-learning (Q-learning occurs between vehicles) integrated with QGGF (Q-greedy geographical forwarding) is adopted to reduce delivery delay and the effect of fast vehicle movements on path sensitivity, while distributed R2R Q-learning (Q-learning occurs between RSU units) is designed for packet forwarding at each intermediate intersection. In the case of a local optimum occurring in QGGF, SCF (store-carry-forward) is used to reduce the possibility of packet loss. Detailed simulation experimental results demonstrate that QTAR outperforms the existing traffic-aware routing protocols, in terms of 7.9% and 16.38% higher average packet delivery ratios than those of reliable traffic-aware routing (RTAR) and greedy traffic-aware routing (GyTAR) in high vehicular density scenarios and 30.96% and 46.19% lower average end-to-end delays with respect to RTAR and GyTAR in low vehicular density scenarios, respectively.
Multi-Objective Optimization of Channel Mapping for Fail-Operational Hybrid TDM NoCs

Despite their technical and economic advantages, multi-core processors are hesitantly adopted in safety-critical embedded application domains. A key issue is to provide hard real-time guarantees and fault-tolerance for critical applications as well as isolation between applications of different criticality on shared resources such as the interconnect. To address this, hybrid NoCs that combine configurable Time-Division-Multiplexing (TDM) for critical traffic with packet switching for Best-Effort (BE) traffic have been proposed. More recently, hybrid NoCs with protection switching schemes have been introduced to cope with fail-operational applications. Although they can handle failures happening in a NoC and guarantee real-time requirements by design, the problem of providing optimized mapping of critical tasks while considering the properties of protection switching schemes still remains. In this paper, we propose a multi-objective optimization methodology to tackle this matter. We first propose a model to map TDM channels onto a given NoC topology while considering protection switching schemes. The implemented objectives are defined so as to evaluate the behavior of the TDM mapping while still capturing its impact on the BE traffic, since a complete simulation considering BE would be time consuming (hundreds of thousands of NoC cycles with different injection rates). We then show how beneficial simultaneously optimizing several objectives is when using hybrid NoCs. The proposed methodology can indeed enable design space exploration in order to find feasible mappings for application specific scenarios and broad trade-off analyzes without having to run prohibitively long BE traffic simulations.
reZig: Decompose a Collision via Reference Waveform in ZigBee

With the proliferation of IoTs, massive deployments of ZigBee devices intensify the collision, a fundamental problem in wireless communication. Conventional solutions exploit the protocol based on CSMA to avoid collision, which incurs considerable overhead. Recently, new solutions which focus on concurrent transmission via collision decomposition are proposed instead of collision avoidance. However, existing approaches to collision decomposition in ZigBee suffer the problems of error propagation and scarce-sample error. To make up the weakness of existing solutions, we propose a novel solution, reZig, which leverages the generated reference waveform in the receiver to infer each single waveform in the collision. By inferring the chip independently, reZig is error-propagation-free. As there are multiple segments provided to decode one chip, reZig can select the segment with most raw samples to ensure the decoding reliability. We implement reZig on the USRP N210 testbed. Experiments demonstrate that reZig achieves the 1.46∼ 2.8× throughput compared to mZig, the state-of-the-art collision-decomposition approach to ZigBee.