Miracle-dz/UniFAR-FTUs huggingface dataset:
Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems.
Configuring Oracle Server for VLDB

This paper will help the reader configure a very large Oracle Server database (VLDB) for high performance and high availability with low maintenance. It describes decisions about database block size, RAID technology and raw devices, redo log files, tablespace partitioning, storage parameters, and rollback segments. This paper explores the technologies and trade-off constraints associated with these issues and presents technically detailed methods for optimizing a configuration within these trade-offs.
CFD modelling of magnetic nanoparticle suspension in microfluidics

In this paper a CFD model is presented to model the magnetic nanoparticle (MNP) suspension aggregation in a microfluidic device. The nanoparticles are injected to the microfluidic chip, which consists of serially connected microchambers. The device with the nanoparticle suspension is used to investigate enzyme-substrate reactions. The current work presents a dedicated two-phase solver for the nanoparticle suspension filling procedure of the chambers. The model relies on our measurements and a magnetorheological model, and it is created with the open source CFD software OpenFOAM.
A Flywheel Energy Storage System in a Microgrid for Powering Small Villages in Remote Islands in the South Pacific

In most Pacific Island Countries (PICs), the populations reside in isolated communities where electricity generation is hard to access and therefore these communities are heavily dependent on fossil fuel based generation plants. One way to reduce the dependence on these fuels is to use the abundant, albeit intermittent, Renewable Energy Sources (RES). At present most RES based microgrids (MGs) use lead acid battery batteries as the only way to store energy, which can be detrimental for the environment. This paper proposes to minimize the ecological impact support them by using Flywheel Energy Storage Systems (FESS) so as to reduce the size of battery strings required or increase the storage performance of the MG. A case study on data for a small village in Taveuni (Fiji) is presented and the advantages of using a FESS as a back-up storage system are highlighted and discussed.
Design of a LP, RHCP and LHCP Polarization-Reconfigurable Holographic Antenna

In this paper, the feasibility of utilizing the microwave hologram to realize the high-gain radiation with polarization agility performance is investigated, and an effective method to design the polarization-reconfigurable holographic antenna is presented. By utilizing a polarization-insensitive hologram as the leaky-wave radiating structure and an appropriately designed, embedded, polarization-reconfigurable feeding launcher to control the polarization state of the radiating leaky waves, a polarization-reconfigurable holographic antenna with broadside radiation and linearly polarized (LP), right-hand circularly polarized (RHCP), and left-hand circularly polarized (LHCP) polarization diversity is successfully achieved. The simulation results demonstrate that the proposed antenna exhibits pencil beams with aperture efficiency up to 28.3% for the three polarization states. Meanwhile, the stable gain performance with peak gain above 17.5 dBi and gain fluctuation less than 1.7 dB is obtained for the three reconfigurable polarization states. For verification, a prototype was fabricated and tested to validate the proposed design concept. Moreover, the working mechanism is investigated.
Thermal and Optical Study on the Frequency Dependence of an Atmospheric Microwave Argon Plasma Jet

This paper presents the frequency- and power-dependent thermal and spectral properties of a microwave argon plasma jet in the gigahertz range. These properties are determined at 1.3 GHz, 2.4 GHz, and 3.5 GHz for input powers from 2 W to 15 W and gas-flows from 1 to 4 Lm−1, including the influence of an argon/nitrogen mixture. These investigations are performed to determine the specific influence of the excitation frequency on the spectral properties of the plasma. The gas temperature is determined via the hydroxyl radical (OH) rotational band around 310 nm by fitting the simulated spectrum to the measurements. It is shown that high temperatures above 1350 K are generated inside the plasma for an input power of 10 W. At the same time, selective discharge 2-D mapping is realized as a result of charge-coupled device (CCD) imaging through optical bandpass filters, corresponding to the OH, N2, Ar, and H spectral emission lines. It is found that all the investigated species change their size proportional to the size of the plasma. Influence of nitrogen is investigated by generating an argon/nitrogen gas mixture. The nitrogen admixtures up to 2% are examined. These investigations are based on measurements of the N2-band around 370 nm. A strong dependence of the discharge appearance on the nitrogen content is found.
Preamble Design for UAV Communications Over Cellular Networks

Unmanned aerial vehicle (UAV) communications over cellular networks are currently gaining increased interest because it can enable real-time control of UAVs and wide-area coverage. We discuss various challenges for future UAV cellular networks, where cellular networks with down-tilted antennas optimized for terrestrial users can extend terrestrial cellular services to aerial users. To overcome challenges such as large channel variation and UAV power consumption in UAV cellular networks, we propose a technique of preamble design for these networks using scalable sequences. Specifically, three types of scalable sequences are proposed for efficient use of battery power in UAVs, namely, maximum-length scalable sequence, Zadoff–Chu scalable sequence, and linear frequency modulated (LFM) scalable sequence. The properties of these scalable sequences are analyzed and compared through simulations in different channel environments.
Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis

Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments.
Finite impulse response neural networks with applications in time series prediction

Traditional feedforward neural networks are static structures which simply map input to output. Motivated from biological considerations, a dynamic network is proposed which uses Finite Impulse Response (FIR) linear filters to model the processes of axonal transport, synaptic modulation, and membrane charge dissipation. Effectively all weights in the static feedforward network are replaced by adaptive FIR filters. A training algorithm based on gradient descent is derived for the FIR structure. The algorithm, termed temporal backpropagation, is shown to be a direct temporal and vectorial extension of the popular backpropagation algorithm. Various properties including computational complexity and learning characteristics are explored. The FIR network can be viewed as an adaptive nonlinear filter with applications encompassing those of traditional adaptive filters and systems. In this dissertation, we concentrate on the FIR network for use in nonlinear time series prediction. Various examples including laboratory data, chaotic time series, and financial data are studied. Iterated predictions and reconstruction of underlying chaotic attractors are used to demonstrate the capabilities of the network and methodology. The theoretical motivations for using networks in prediction are also addressed. In looking for a more direct method to derive temporal backpropagation, we introduce and prove a unifying principle called Network Reciprocity. The method, based on simple rules of block diagram manipulation, allows for an almost effortless formulation of neural network algorithms. The approach is illustrated by deriving a variety of algorithms including standard and temporal backpropagation, backpropagation-through-time for recurrent networks and control structures, an efficient method for training cascaded nonlinear filters, and algorithms for networks composed of Infinite Impulse Response (IIR) and lattice filters.
Life detection system using UWB Radar During Disaster

Ultra Wide Band radar renders a salient role in rescuing living beings during disasters or calamities. UWB radar is a kind of sensor that senses the human beings who are buried in the collapsed environment. In the existing system, keying the vital signs (heartbeat and breathing) in complex environment is challenging due to low Signal to Noise Ratio (SNR). The primary tool used for monitoring heartbeat is the voltage-derived Electro Cardio Gram (ECG). An Electrical signal causes the heart muscle to contract and affects the victim. The lack of first aid and medication facilities in the nearby region results in loss of many life. In the proposed system, by using Advanced Signal Processing approaches can identify the victims in complex environments. This can be done by using Data mining and Discrete Wavelet Transform (DWT) is in which a signal is split up in to a set of basic functions and it is required for clinical decision support. This paper presents an advanced signal mining which is the combination of signal processing and data mining. The proposed system is done by MATLAB. In this system the information will be collected and given it to first aid assistance team. The time consume will be reduced using UWB radar along with data mining.
Modulation Strategies for Indirect Matrix Converter: Complexity, Quality and Performance

In general, there are two main classifications in matrix converters. The most common known type is conventional matrix converter (CMC) or direct matrix converter (DMC). The other type is indirect matrix converter (IMC). A brief review for modulation strategies are provided in this work for modulation strategies in IMC. There are several popular modulation methods for IMC such as carrier-based modulation and space vector modulation (SVM). A sinusoidal current waveform is produced on the input and output sides to implement the modulation method. In the conclusion the modulation methods will compared based on performance, theoretical complexity, and some other parameters.
Data Driven Model for Performance Evaluation and Anomaly Detection in Integrated Air Source Heat Pump Operation

In this paper we focus on a practical application of data driven process monitoring for performance evaluation and anomaly detection in the operation of air source heat pumps (ASHPs). At our test site, which is a hospital building in Singapore, we install sensors to monitor the operation of a hot water heating system (10 years old) comprising of 5 ASHPs operating according to a pre-defined switching algorithm to balance their workload. We collect data for 6 consecutive months. With the help of the data and site specific conditions, we first develop a Coefficient of Performance (CoP) based technique to visualize performance degradation regarding to ASHPs and to identify patterns for early fault detection. In our monitored data, three pumps malfunctioned with easily identifiable patterns, such as, consistent under-performance or over-performance followed by longer periods of under-performance. Then, using one month data, we also train a linear regression model for each ASHP performance. This model can also be used to predict the outlet temperature as well as the performance degradation in the operation of individual ASHPs relative to the performance in the training period. The results of the linear model are consistent with our CoP technique. The proposed data driven methods can yield huge economic benefits for the hot water system operator.
Missing Data Recovery in Large-Scale, Sparse Datacenter Traces: An Alibaba Case Study

The trace analysis for datacenter holds a prominent importance for the datacenter performance optimization. However, due to the error and low execution priority of trace collection tasks, modern datacenter traces suffer from the serious data missing problem. Previous works handle the trace data recovery via the statistical imputation methods. However, such methods either recover the missing data with fixed values or require users to decide the relationship model among trace attributes, which are not feasible or accurate when dealing with the two missing data trends in datacenter traces: the data sparsity and the complex correlations among trace attributes. To this end, we focus on a trace released by Alibaba and propose a tensor-based trace data recovery model to facilitate the efficient and accurate data recovery for large-scale, sparse datacenter traces. The proposed model consists of two main phases. First, the data discretization and attribute selection methods work together to select the trace attributes with strong correlations with the value-missing attribute. Then, a tensor is constructed and the missing values are recovered by employing the CANDECOMP/PARAFAC decomposition-based tensor completion method. The experimental results demonstrate that our model achieves higher accuracy than six statistical or machine learning-based methods.
A Modeling Methodology for Resistive RAM Based on Stanford-PKU Model With Extended Multilevel Capability

Modeling of resistive RAMs (RRAMs) is a herculean task due to its non-linearity. While the exigent need for a model has motivated research groups to formulate realistic models, the diversity in RRAMs’ characteristics has created a gap between model developers and model users. This paper bridges the gap by proposing an algorithm by which the parameters of a model are tuned to specific RRAMs. To this end, a physics-based compact model was chosen due to its flexibility, and the proposed algorithm was used to exactly fit the model to different RRAMs, which differed greatly in their material composition and switching behavior. Furthermore, the model was extended to simulate multiple low resistance states (LRS), which is a vital focus of research to increase memory density in RRAMs. The ability of the model to simulate the switching from a high resistance state to multiple LRS was verified by measurements on 1T-1R cells.
Cognitive Competence Improvement for Autonomous Vehicles: A Lane Change Identification Model for Distant Preceding Vehicles

The motion status recognition of the preceding vehicle in a long-distance region is a requirement for autonomous vehicles to make appropriate decisions and increase their comprehension of the environment. At present, the lane change behavior of the leading vehicle at a short distance is detected using stereo cameras and LiDAR. However, the short detection distance (about 100 m) does not meet the requirements of high-speed driving of autonomous vehicles on expressways; this is a fundamental problem limiting the development of autonomous vehicles exhibiting human-like behavior. In this paper, a comprehensive model consisting of a back-propagation (BP) neural network model optimized by a particle swarm optimization (PSO) algorithm, and a continuous identification model is developed based on the results of naturalistic on-road experiments using millimeter-wave radar data. By considering different time-to-lane crossings (TLCs), the PSO-BP neural network model is trained using real vehicle lane change data and implemented when the TLC of the leading vehicle is longer than 1.0 s. In contrast, when the TLC is less than 1 s, the continuous recognition model of the TLC is used. By comparison with the BP neural network model, the recognition accuracy rate of the proposed model is increased from 80% to 87% after the PSO optimization for a time window of 1.0 s; these results meet the recognition requirements of the autonomous driving systems for distant targets. The findings of this paper improve the cognitive competence and safety of autonomous driving systems.
Color Features and KNN in Classification of Raw Arecanut images

Arecanut is one of the important cash crops of Southern India. Classification of raw arecanut is one of the major tasks in grading, which is a vital part of crop management. In this work we proposed a model which classifies the raw arecanut. We used color histogram and color moments as features with K-NN classifier. Experiment is conducted on a dataset of 800 images of four classes using two color features and four distance measures with K-NN. A classification accuracy of 98.13% is achieved for 20% training with K value of 3 and Euclidean distance measure for color histogram features.
Constrained blind equalizer using adaptive nonlinear filter

In this paper, a new adaptive equalizer that adjusts the nonlinear filter without training signals is proposed and its performance is evaluated. In the proposed equalizer, the nonlinear filter has constraints, while learning is carried out by means of the output energy minimization algorithm. Since the proposed equalizer has nonlinearity, it has a better bit error rate than the linear equalizer. Further, since no training signal is needed, it is to be expected that the transmission efficiency is superior to that in an equalizer with training signals. In order to evaluate the performance limitations of the proposed equalizer, an optimum equalizer with constrained filter input is derived. By means of simulations, a performance evaluation is carried out and the effectiveness of the proposed equalizer is demonstrated. Further, in order to improve the performance of the proposed equalizer for a linear communication channel, an intersymbol interference canceler and an algorithm for correcting the error are proposed and their effectiveness is demonstrated. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(3): 17–29, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10145
Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed. Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images. In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE). Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality. In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously. In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE. The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding. The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE. The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches.
Indoor INS / UWB-based human localization with missing data utilizing predictive UFIR filtering

A combined algorithm for the loosely fused ultra wide band ( UWB ) and inertial navigation system ( INS ) -based measurements is designed under the indoor human navigation conditions with missing data. The scheme proposed fuses the INS- and UWB-derived positions via a data fusion filter. Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability, we also consider the missing data problem in UWB measurements. To overcome this problem, the loosely-coupled INS / UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response ( UFIR ) fusion filter. We show experimentally that, the standard UFIR fusion filter has higher robustness than the Kalman filter. It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.
Monolithically Integrated E/D InAlN/GaN HEMT And Inverters On Sapphire Substrate

We have fabricated and characterized direct-coupled field-effect transistor (FET) logic (DCFL) Inverters based on monolithically integrated enhancement/depletion-mode (E/D-mode) InAlN/GaN high electron mobility transistors (HEMTs). The Enhancement-mode InAlN/GaN HEMT was realized by utilizing low damage BCl3-based plasma gate-recess etching, which exhibits a peak transconductance (Gm) of 435mS/mm with a threshold voltage of 0.74V. When the supply voltage (VDD) is biased at 2.5V, the E/D InAlN/GaN HEMTs inverter exhibits an output logic swing of 2.19V, a logic-low noise margin of 0.54V and a logichigh noise margin of 1.38V.