Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-pass maximally flat IIR digital differentiator design with arbitrary flatness degree

Digital differentiators are often used to estimate the derivative of measured signal. They are realized as FIR (Finite Impulse Response) or IIR (Infinite Impulse Response) digital filters. Low-pass maximally flat differentiators have smooth frequency response and are useful since it can avoid signal distortion caused by ripple in the passband of the differentiator. However, the conventional design method of low-pass maximally flat IIR digital differentiator is limited for flatness degree. Hence this paper proposes a design method for low-pass maximally flat IIR digital differentiators with arbitrary flatness degree. At first, the proposed method formulates the simultaneous equations to derive the coefficients of denominator. Then, the recurrence formula is formulated to derive the coefficients of numerator. Through design examples, it is shown that the proposed method can treat arbitrary flatness degree.
Head Movement Prediction using FCNN

Viewport adaptive streaming of 360-dgree videos relies on accurate prediction of the viewport, while the user generally suffers from significant quality degradation under long delay settings. To deal with this issue, advanced methods for long-term viewport prediction are highly desired to improve viewport prediction accuracy. To more accurately capture the non-linear relationship between the future and past viewpoints, this paper proposes a Fully Connected Neural Network (FCNN) model to make future predictions, which is light in computation. The input data such as yaw values, pitch values, Estimated Weighted Moving Average (EWMA) of yaw values, and EWMA of pitch values, are transformed into sine and cosine angles before feeding into the encoding layer of the FCNN model by considering the roll angle to zero. After transforming the data input into the proposed FCNN model, a long-term prediction length of up to 4 seconds has been explored, to capture the non-linear and long-term dependent relation between past and future viewport positions more accurately. Experimental results show that the proposed scheme performs well for the large size prediction window.
Analysis of EIRP Measurement Grid for 5G Millimeter Wave User Equipment

For 5G millimeter wave (mm-Wave) telecommunications, the combination of highly integrated phased array antenna modules and system-level dynamic beamforming makes the antenna port inaccessible for physical connections to test equipment. Therefore, radiated over-the-air (OTA) testing becomes mandatory for 5G mm-Wave devices’ validation and performance evaluation. New test cases as well as associated Figure of merits (FoMs) are required. In 3rd generation partnership project (3GPP), the equivalent isotropic radiated power (EIRP) is used as a basic FoM for defining the radiation performance and the uplink spherical coverage of UE is evaluated by cumulative distribution function (CDF) of EIRP. Given the complexity of OTA test system, the test time for 5 G mm-Wave devices have been dramatically increased compare with sub-6 GHz test cases. Therefore, proper approaches to reduce the mm-Wave test time significantly is a key issue to be resolved and measurement grid is an important breakthrough direction. This paper aims to present an in-depth understanding of the impact of different measurement girds on 5G mm-Wave test time and accuracy through experiments. We first briefly review the progress of 3GPP standardization on measurement grid for 5G mm-Wave mobile devices. Then, several experiments are carried out with different angular step size to get different CDF results, their test time and measurement accuracy are also compared and analyzed. The results illustrate the importance of optimized angular step size and a proper choice of measurement grid size. The challenges of balancing test time and measurement accuracy for 5G mm-Wave devices during OTA RF performance evaluation have been elucidated and further discussed.
Multi-Resolution Convolutional Recurrent Networks

In sequential learning tasks, recurrent neural network (RNN) has been successfully developed for many years. RNN has achieved a great success in a variety of applications in presence of audio, video, speech and text data. On the other hand, temporal convolutional network (TCN) has recently drawn high attention in different works. TCN basically achieves comparable performance with RNN, but attractively TCN could work more efficient than RNN due to the parallel computation of one-dimensional convolution. A fundamental issue in sequential learning is to capture the temporal dependencies with different time scales. In this paper, we present a new sequential learning machine called the multi-resolution convolutional recurrent network (MR-CRN), which is a hybrid model of TCN encoder and RNN decoder. Utilizing the representation learned by TCN encoder in different layers with various temporal resolutions, RNN decoder can summarize the contextual information with dif-ferent resolutions and time scales without modifying the original architecture. In the experiments on language modeling and action recognition, the merit of MR-CRN is illustrated for sequential learning and prediction in terms of latent representation, model perplexity and recognition accuracy.
Research on chemical reaction prediction model based on Fairseq

Predicting the products of chemical reactions is a conspicuous difficulty in organic chemistry. The model in this paper is used to predict the compounds produced under known reactants and reagents. The author uses fairseq framework of python to construct a neural machine translation model based on SMILES strings of chemical reactants, reagents and products. The molecular formula of the products of the reactions can be well predicted. After training, the loss and the ppl of the model are 0.435 and 1.35. The final two bleu ontest part are 12.71 and 12.60 respectively. Fairseq performs well in molecular prediction, which shows that the cross domain traditional neural machine translation method has broad prospects in chemistry, and the use of artificial intelligence method to assist traditional research has strong practicality.
RF Energy Harvesting for Low Power Applications

The Radio frequency energy harvesting is attracting a more attention in order to provide power to the electronic devices, including Wireless Sensor Networks. This project focuses on utilizing ambient radio frequency energy available in the nearby environment by using rectifier and an antenna called as rectenna. The main element of radiofrequency harvester is the receiving antenna, as it has to capture the RF energy from the radiating sources. Our work provides, highly efficient ultra-wide band antenna ranging from 1.7 to 10 GHz for energy harvesting is presented. A new UWB Octagon slotted Circular Microstrip Patch antenna is designed to operate at UWB frequencies with increased bandwidth and reduced side lobe levels. At the receiving end, the rectifier circuits convert the received signal to DC supply, from where the power is given to the suitable application. The antenna proposed for the above need was designed using Ansoft HFSS, fabricated and will be tested under ambient conditions and rectifier is also simulated using Multisim, fabricated and tested. The expecting output voltage from this system is from the range 0.1 to 3V.
Cloud resource prediction model based on LSTM and RBF

In order to respond to resource usage "in advance" and to dynamically, timely and accurately schedule and allocate resources based on predicted values, a cloud resource prediction model based on Long Short Term Memory (LSTM) neural network and Radial Basis Function (RBF) neural network is proposed after investigating the current state of research on the cloud container resource prediction problem, and the parameters are trained using back propagation (BP) neural network to find the optimal combination of parameters. The LSTM can avoid the long-term dependency problem, and remembering information from very early moments is the default behavior of the LSTM without the use of a back propagation (BP) neural network. The LSTM can avoid the long-term dependency problem, and remembering information from very early moments is the default behavior of the LSTM without paying a great price for it specifically. The transformation of RBF from input space to hidden space is nonlinear, while the transformation from hidden space to output space is linear, greatly speeding up learning and avoiding the local minima problem . The experimental results show that the average error ratios of the LSTM single prediction model and RBF single prediction model are 16.38% and 19.36%, respectively, while the average error ratio of the proposed model is 4.51%, which shows that the model proposed in this paper has better prediction performance and prediction accuracy compared with other models.
3D-GFE: a Three-Dimensional Geometric-Feature Extractor for Point Cloud Data

In this paper, we propose a novel framework that extracts rotation-invariant features relative to the centroid and the reference point in a local point set. Furthermore, we search different scales of nearest neighbors simultaneously to acquire more rotation-invariant information around certain specific point cloud data. We evaluate our architecture with two point cloud tasks, object classification and part segmentation. Experiment results show that our method generates consistent results on randomly rotated data and achieves state-of-the-art performance without any rotational data augmentation. For classification, when training and testing with arbitrary rotations, our model is able to reach averagely 89.0% and 73.5% accuracy on the ModelNet40 and ScanObjectNN datasets, respectively. On the ShapeNet dataset, which is a part segmentation task, our model can achieve 77.7% mIOU.
Stereo Visual Odometry with Information Enhancement at Feature Points

The visual odometry based on the feature point method is solved by matching spatial points with pixel points using the PnP algorithm when calculating the pose change between the front and back frames. Accurate spatial location of feature points plays an important role in the process of visual odometry calculation. Incorrectly positioned space points greatly affect the performance of the visual odometer. The depth value calculation method based on deep learning can effectively improve the accuracy of spatial point locations, however, this method does not focus on the correctness of depth values at feature points. This paper proposes a way to integrate deep learning and traditional approach to construct a stereo VO system which pays attention on the feature points’ depth to improve the accuracy of the visual odometry indirectly. Specifically, the training process is divided into two phases. The first phase trains a stereo matching network using a binocular dataset to obtain the initial model of the network. In the second stage, a feature extraction network is added to obtain a feature point mask from the extracted feature points, and a loss function is built using the mask, while a reprojection loss function is built using the poses values. The two loss functions are added to the first stage loss function during the second stage training. Finally, the trained stereo matching network is used to generate the depth values, and the matched feature points are obtained using the feature matching network, and the stereo visual odometry is constructed by calculating the relative pose between the former and latter frames through the PnP algorithm. Extensive experiments on the KITTI dataset show the robustness of our system.
Design of a High Isolation and Dual-polarized Antenna for 2G/3G/LTE Base Stations

A novel high isolation and dual-polarized antenna is designed in this paper for 2G/3G/LTE base station application. The antenna composing of two pairs of crossed dipoles achieves dual-polarized at the classic communication band. Each pair of dipole is fed by a microstrip line which is directly connected with a coaxial line. The radiating plate of the antenna is printed on the bottom of the dielectric slab and the material of slab is Rogers RO3003(tm). Microstrip line adopts jumper structure, and they are printed on the bottom and top of the dielectric plate, which are perpendicular to each other. The simulated results show that the antenna has a bandwidth of 51.2% (1.67–2.82GHz) for the return loss of S11 less than -10dB, which achieves the coverage of 2G/3G/LTE frequency band. A high isolation of the antenna beyond 30dB in most of the bandwidth can be achieved, and the gain is about 8.2dBi. Thus, this high isolation and broadband dual-polarized antenna is potential for 2G/3G/LTE base station applications.
Integrated spectral kurtosis analysis

Spectral kurtosis (SK) analysis is a popular signal processing technique to locate harmonics, transients and repetitive impulses in the frequency domain. As a fourth-order frequency-domain technique the SK belongs to the wide range of higher-order spectral analysis and commonly finds application for the pre-processing of band-limited envelope analyses. We propose a novel definition of the SK, whereby SK estimates are fully resolved in time using the technique of filtering-averaging. Hereby, SK estimates are defined as the average of the full time resolution of trispectral estimates, while the latter can be processed to carry out envelope analyses directly on these estimates. Conjoining SK and envelope analysis specifies an integrated SK analysis, providing insight into the generating mechanisms of its estimates. It further differentiates from the popular short-time Fourier transform (STFT) estimator in a facilitated handling, $(\boldsymbol{a})$ requiring less parameters, $(\boldsymbol{b})$ circumventing the quasi-stationary assumption of the STFT, and $(\boldsymbol{c})$ providing the frequency bandwidth with a clear meaning.
Exploring Artifact Rejection for High-pulse Rate Electrically Evoked Auditory Steady State Responses in Cochlear Implants Users

The electrically evoked auditory steady state response (eASSR) is a potential objective measurement for cochlear implant (CI) fitting. Similar to other electroencephalography (EEG) measurements with CI users, artifacts caused by the pulsatile electric stimulation are phase locked to the stimulus and cannot be further reduced via an increasing number of repetitions. This is particularly problematic for high-pulse-rate eASSR, where the artifact temporally overlaps with a large part of the desired brain response. Here, an off-line CI artifact reduction technique, referred to as moving strobe averaging (MSA), is proposed and evaluated using highly-controlled EEG measurement data which deliberately do not contain a brain response: 1) simulated idealized data and 2) two dummy EEG recordings collected with either an electric resistor circuit to mimic scalp impedances (CI-in-a-box) or a saline solution filled tank (CI-in-atank) setup. The MSA algorithm supports identification of the artifact in the time domain and only requires that the artifact duration remains shorter than the inter-stimulus interval. In addition to being computationally efficient, MSA allows for removal of the stimulation artifact with minimal distortion of the desired neural response. Potential limitations of artifact rejection caused by the recording equipment itself are identified.
Factory Operating System (FOS): Vertical Integration Framework for Smart Factories

The vertical integration of shop-floor resources used to build smart factories is among the key factors of the Industrial Internet of Things (IIoT). The primary IIoT enablers are the emerging technologies that have been arising from computer and data science, information and communication technology, and artificial intelligence and machine learning domain. However, existing architectures such as Cyber Physical Production System are rather abstract and with technology preferences. In this paper, a technology-neutral framework called Factory Operating System (FOS) was proposed. The similarity between the FOS and computer Operating System (OS) was discussed first to establish the easy-to-understand background. Then, the main functions and common FOS services were identified and defined. Furthermore, potentially compatible technologies were examined alongside FOS, allowing for technical gaps and overlaps to be detected. The outlook of the FOS application scenarios based on the UniqueCandy prototype and the FOS scalability to support production networks was assessed. Finally, the obtained results validate that FOS can be used both as a practical vision and an applicable framework for the vertical integration of smart factories.
Kronecker Product Adaptive Beamforming for Microphone Arrays

Microphone array adaptive beamforming is needed in a wide range of audio and speech applications for acquiring the acoustic signal of interest while suppressing noise and inter-ference. In the design and application of adaptive beamformers, particular attention has to be paid to the issues of robustness and computational efficiency. One way to deal with these issues is through the use of the recently developed Kronecker prod-uct beamforming framework. However, the existing Kronecker product beamformers were formulated based on special array geometries that can be straightforwardly decomposed into sub-arrays and, as a result, the application of such formulation is limited to a small range of arrays. To generalize the formulation, we introduce in this paper a framework that can be applied to arbitrary array geometries, where the beamformer is represented as a sum of Kronecker products of several subfilters. Based on this new framework, an iterative optimization algorithm is derived for designing the subfilters of the minimum variance distortionless response (MVDR) beamformer. Simulation results demonstrate the advantages of the proposed method in different conditions.
An Optimal Variable-Latency Architecture for Deterministic Approaches to Stochastic Computing with Unary Bit Stream Preserving Properties

Classical Stochastic Computing methods perform operations on bit streams generated using random number generators. Sufficient accuracy requires long bit streams which leads to very high latency and larger area. Deterministic approaches to Stochastic Computing greatly reduce the latency and area, and produce fully accurate results, but still have high latency. While state-of-the-art variable-latency architectures show an up to 70% reduction in latency, they still perform operations that do not contribute to the final result. In this paper we present an optimal variable-latency archi-tecture in terms of minimum required cycles to obtain fully accurate results. We propose an efficient stochastic number gen-erator that uses a down counter and compare-to-zero hardware compared to an up counter and binary comparator. This key contribution means that the latency of our design only depends on the magnitude of input values and is independent of the number of bits or inputs used. This is not the case in state-of-the-art variable-latency architectures. Also, unlike any other architecture, ours preserves the unary bit stream encoding after performing additions and multiplications, which enables more complex designs that require unary bit streams such as sorting. Our architecture thereby further improves the area, latency, and energy efficiency compared to the state-of-the-art.
The fake news propagate with more self-loop in online social networks

In recent years, social networks such as Twitter and Weibo have developed rapidly. At the same time, false and harmful information has spread rapidly in online social networks like a plague and has caused a great negative impact on people who don't know the truth. We found that fake news turns to have different structural features of their propagation networks compared with that of real news. With careful analysis, we reveal that fake news is spread by the self-loop (one user reposts its own re-posting) to some degree. This abnormal spread pattern could be a feature of fake news identification and motivate future studies in this direction.
Imbalanced sample feature enhancement of hyperspectral imagery classification

Due to its easy application, low computation consumption and promising generalization performance, extreme learning machine is widely used for hyperspectral imagery classification. However, most extreme learning machine algorithms ignored the depression of the majorities to minorities. To tackle this task, a model is proposed to improve the performance of imbalanced sample classification for hyperspectral images. Firstly, the spatial and spectral features are combined, in order to enhance the feature of minorities, guided filtering and enhanced neighborhood features are adopted, which will enlarge the samples of minorities and provide diversity for classification task. Secondly, the separating boundary is supposed to be pushed toward the side of minority class under imbalanced situation, which in fact favors the performance of majority class. A random under-sampling bagging extreme learning machine is employed, which will intensively improve the depression effect. Experiments carried on two widely used datasets. The results showed that, the imbalanced sample feature enhancement model proposed in this manuscript takes into account both the feature of enhanced small sample and the suppression of large sample on the classification boundary of small sample by extreme learning machine, which weakens the suppression effect of large sample on small sample, and further improves the classification accuracy of hyperspectral images.
Model-based Microstrip Antenna Design

This paper aims to reduce the design cost and streamline the design processes for microstrip antennas by using a model-based antenna design technique that explains the behavior of the system more simply by describing the behavior of the elements in a system and the interactions between these elements for the process of antenna design phase that begins from the idea stage to the production stage. This technique proposes to create two models called employer and employee models for antenna design processes. The employer model organizes the design process by keeping the limitations and freedoms of the antenna’s parameters, while the employee model uses graph theory and machine learning to define antenna parameters and interactions between parameters by creating a dynamic model. In this paper, this concept is applied to rectangular microstrip antennas. The cavity model performs preliminary design optimization on the rectangular microstrip antenna’s far region electric field radiation patterns (E and H planes) on the width and length parameters. For various rectangular antennas, it is demonstrated that the width and length suggested by the method supply the intended radiation parameters that agree with simulations made with pre-existing simulation software. Thus, the pre-optimization technique that is quick, easy to comprehend, and apply is obtained that supplies the desired radiation pattern. Using this technique aims to minimize the number of design trials by explaining the freedoms and limitations regarding the antenna with the employer model by checking the compatibility of the antenna with the employer model during the design.
Two-step reset method for energy-efficient SAR ADC switching schemes

The impact of the reset energy on the overall energy efficiency of an successive-approximation register (SAR) ADC is examined for some recently reported switching schemes. The reset energy can several times exceed the switching energy drawn from the reference during conversion. The simple method based on the optimal capacitor charging is proposed. By utilising of two-step switching during the reset phase, the total energy consumption of the DAC's capacitive matrix can be decreased by ∼20%.