Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Identification Method for Signal-Level Wireless Protocol

Electromagnetic spectrum surveillance is the basis of space environment monitoring and management, while protocol identification is one of the most important methods. Protocol identification has been applied in many fields, such as target recognition, anomaly detection, security management and information countermeasure. In the past decade, a lot of deep learning based protocol identification methods have been proposed, based on the data-level protocol. However, the data needs to be demodulated and decoded, depending on the prior information of the target system. In this paper, we focus on signal-level protocol, and introduce the deep learning based identification method. We first develop a multi-dimensional convolutional neural network based method for feature extraction of signal-level protocol. Then, a spatial pooling convolutional neural network is introduced for variable length protocol identification. At last, an unsupervised training network is proposed for feature extraction of unlabelled signal-level protocol. The proposed methods are evaluated by using the practical wireless protocols, and the effectiveness is verified by simulation results.
Forecasting of GDP (Gross Domestic Product) Per Capita Using (ARIMA) Data-Driven Intelligent Time Series Predicting Approach

Gross Domestic Product is the aggregate value of all final services and products generated by the country during the measurement period, including private inventories, paid-in capital expenditures, government purchases, personal consumption, and the balance of international commerce. During the Pandemic period of the last two years, the COVID-19 outbreak has caused chaos in the worldwide economy. Sickness outbreaks, supply-chain disruptions, and, more recently, inflation have made policymaking exceedingly difficult. This research aims to forecast GDP (Gross Domestic Product) per capita for the coming years while also examining historical and present trends in India. This study's objective is to forecast India's future GDP per capita over ten years, from 2021 to 2030, using ARIMA. According to a study, India's GDP per capita has been growing during the last 10 years, and this movement is likely to last over the following ten years.
A Review on Recent Machine Learning Applications for Addiction Disorders

Constant contributions of Machine Learning (ML) technology in health sciences has extended to solve addiction disorders problems, whether to detect symptoms or predict risks and treatment outcomes. This article presents an updated review related to the application of ML techniques for addiction disorders, the selected works covered substance addiction (N=18 studies) and non-substance addiction (N=3 studies), and were divided into three categories prognosis, diagnosis, and predicting treatment success. To provide strong evidence about the potential of ML methods to accelerate early prevention and intervention, ultimately aiming to pave the way for further applications of ML approaches in this field.
WeBLE: A BLE-based system compliant with WoT architecture

The W3C organization has recently continued to propose recommendation for WoT (Web of Things). In order for BLE, which has a high market share in IoT application, to comply with the WoT recommendation, this paper propose a proprietary application-level gateway and WeBLE (Web of BLE) device that allow clients to treat BLE devices as WoT node and increase the interoperability of BLE devices.
CoDiPy: Performance Evaluation of Vehicular Cooperative Downloading in Python

With the increase of vehicular software, the need for efficient and cost-effective communication arises. As cellular communication alone can introduce large monetary costs, alternative communication schemes, like cooperative downloading, have been proposed. Thereby, vehicles can use direct communication technologies to collect a full set of messages. Existing network simulators can be used to evaluate the performance of such systems, however, their accurate transmission model impedes the performance. To circumvent the performance gap, we introduce Cooperative Downloading in Python (CoDiPy) to increase the simulation performance and enable large-scale and long simulation runs. We evaluate the accuracy of our results against a state-of-the-art network simulator with the same transmission parameters. CoDiPy's improves the execution time in our experiments by a factor of up to 36 when compared to a network simulator, without inducing inaccuracies.
A Hardware Accelerator for Long Sequence Alignment with the Bit-Vector Scoring Scheme and Divide-and-Conquer Traceback

Sequence alignment plays an important role in bioinformatics. In this paper, we propose an application-specific integrated circuit (ASIC) design for the pairwise sequence alignment using Levenshtein distance based on the Myers bit-vector algorithm. We also design an accelerator for the divide-and-conquer-based Hirschberg’s algorithm to reduce memory usage, so that the on-chip traceback function can be supported. The hardware is implemented with TSMC 40 nm technology and the maximum input sequence size of the aligner is 10,240 × 5,242,880 bps. Our hardware accelerator can achieve 3,992 GCUPS, which speeds up the pairwise alignment by 147× when compared with the software counterpart for the sequences of 90% similarity.
Measuring the Cosmological Geometry from the Lyα Forest along Parallel Lines of Sight

The possibility of measuring the cosmological geometry using the redshift space correlation function of the Lyα forest in multiple lines of sight as a function of angular and velocity separation is discussed. The geometric parameter to be measured is f(z)≡c-1H(z)DA(z), where H(z) is the Hubble constant and DA(z) the angular diameter distance at redshift z. The correlation function is computed in linear theory, assuming that the Lyα forest is a result of gravitational instability in a photoionized intergalactic medium. We describe a method to measure the correlation from observations with the Gaussianization procedure of Croft et al. to map the observed Lyα forest transmitted flux to an approximation of the linear density field. The effect of peculiar velocities on the shape of the recovered power spectrum is pointed out. We estimate the error in recovering the f(z) factor from observations due to the variance in the Lyα absorbers. We show that at least ~25 pairs of quasars (separations <3') are needed to distinguish a flat Ω0=1 universe from a universe with Ω0=0.2, ΩΛ=0.8. A second parameter that is obtained from the correlation function of the Lyα forest is β≃Ω(z)0.6/b (affecting the magnitude of the peculiar velocities), where b is a linear theory bias of the Lyα forest. In the theory of the Lyα forest assumed here, the parameter β can be predicted from numerical simulations; once β is known, the number of quasar pairs needed to constrain f is reduced to about six. On small scales, where the correlation function is higher, f(z) should be measurable with fewer quasars, but nonlinear effects must then be taken into account. The anisotropy of the nonlinear redshift space correlation function as a function of scale should also provide a precise quantitative test of the gravitational instability theory of the Lyα forest.
A Wireless Headstage Prototype Based on a Neurorecorder IC

In this work, we present a prototype for a wireless headstage intended for in-vivo recording of neural signals. It allows 1.4 Mbit/s wireless recording from a previously reported 32 channel neurorecorder IC in the LFP (local field potential) and AP (action potential) band. The prototype is currently configured for simultaneous recording and transmission of 8 LFP channels, allowing to connect to tiny chronically implanted electrode connectors in mice. The small weight of 3.2 g and an area of 23 x 20 mm2 makes the headstage suitable for small rodents. The low system power consumption of 12.6 mW allows a sufficiently long battery life of almost 6 hours from a standard LIR1254 button cell battery. To allow the examination of a correlation between neural activity and external stimuli, the system enables to synchronize the time base of external signals and recorded neural activity.
Determining the mass of the accreting white dwarf in magnetic cataclysmic variables using RXTE data

We have extracted spectra of 20 magnetic cataclysmic variables (mCVs) from the RXTE archive and best fitted them using the X-ray continuum method of Cropper et al. to determine the mass of the accreting white dwarf in each system. We find evidence that the mass distribution of these mCVs is significantly different from that of non-magnetic isolated white dwarfs, with the white dwarfs in mCVs being biased towards higher masses. It is unclear if this effect is a result of selection or whether this reflects a real difference in the parent populations.
A Flexible End-to-End Dual ASIC Transceiver for OFDM Ultrasound In-Body Communication

Implanted medical devices need a reliable, low-energy and secure in-body communication link. Ultrasound (US) wave propagation is promising over other techniques due to its lower body attenuation, inherent security and well-studied physiological impact. While US communication systems have been proposed, they either neglect realistic channel conditions or fail to be integrated into small-scale, energy-scarce systems. This paper presents a highly flexible end-to-end ASIC transceiver with OFDM modulation tailored to robust intra-body US communication. This transceiver comprises a dual chip solution: i) a RISC-V microcontroller extended with two custom passband modulation accelerators implemented in 65nm CMOS; and ii) an analog front-end containing a 20V class-D power amplifier and low-power receive chain implemented in 180nm BCD. Under typical 10cm in-body channel attenuation, OFDM-16QAM modulation and 470kbps datarate, the transceiver achieves both in Tx and Rx mode a BER below 1e-4 consuming 64/20.9nJ/bit Tx/Rx.
Potential superhard osmium dinitride with fluorite and pyrite structure: First-principles calculations

We have performed systematic first-principles calculations on dicarbide, -nitride, -oxide, and -boride of platinum and osmium with the fluorite structure. It is found that only $\mathrm{Pt}{\mathrm{N}}_{2}$, $\mathrm{Os}{\mathrm{N}}_{2}$, and $\mathrm{Os}{\mathrm{O}}_{2}$ are mechanically stable. In particular, $\mathrm{Os}{\mathrm{N}}_{2}$ has the highest bulk modulus of $360.7\phantom{\rule{0.3em}{0ex}}\mathrm{GPa}$. Both the band structure and density of states show that the new phase of $\mathrm{Os}{\mathrm{N}}_{2}$ is metallic. The high bulk modulus is owing to the strong covalent bonding between Os $5d$ and N $2p$ states and the dense packed fluorite structure. In addition, the total-energy calculation for pyrite structure has also been performed, which indicates its mechanical and energetic stability but much lower bulk modulus compared to the fluorite structure.
Time and Frequency Domain Feature Selection Using Mutual Information for EEG-based Emotion Recognition

Emotion Recognition using EEG signals remains a challenging task. Usually, feature extraction and channel selection are determined based on neuro-scientific assumptions. Too many features during the EEG-based human emotion recognition will lead to reduced classification accuracy and consume high computational costs. This study analyzes time and frequency domain features such as Mean, Mean Absolute Value, Standard Deviation, and Power Spectral Density. In this study, an EEG Recording session involved 25 subjects consisting of 12 males and 13 females. Video with two emotions, happy and sad, were stimulated to the subjects. The electrodes were placed in channels F7, F8, FP1, and FP2 based on the 10/20 EEG system. The EEG pre-processing, such as signal filtering, Automatic Artifact Removal EOG, Artifact Subspace Reconstruction, and Independent Component Analysis, were done using MATLAB Toolbox, followed by Infinite Impulse Response with Butterworth was applied to separate the EEG signal into alpha, beta, and gamma sub-band. Therefore, 48 numbers of features were extracted to perform emotion recognition. Mutual Information is used for calculating the degree of importance of each feature. Then, the features were ranked to eliminate features with a minimal contribution. We implemented a Random Forest algorithm to classify human emotions based on the EEG signal. The experimental results show that reducing the number of utilized features from 48 to 12 can increase the accuracy score from 82.61 % to 95.65 %.
Research on Spectrum Needs Prediction Method for HAPS as IMT Base Station

The High-Altitude Platform Station (HAPS) is an integral component in Non-Terrestrial Network (NTN) which is potential 6G technology. The HAPS as IMT Base Stations (HIBS) in the mobile service is an emerging and promising approach to achieve vertical heterogeneous network. Therefore, some efforts have been made in order to harmonize the worldwide usage of HIBS, including investigations of the spectrum needs, usage, and technical and operational characteristics of HIBS, among which the spectrum needs has drawn particular attention because it should be determined in advance prior to the commercial deployment of HIBS. However, there are very few investigations devoted to the prediction of the spectrum needs for future HIBS deployments currently due to some tough difficulties. Firstly, there has no actual deployment precedent of HIBS so that the future capacity demands are not clear. Secondly, both the networking and its performance of HIBS deployment are questions to be answered. Therefore, taking into account the study under WRC-23 Agenda Item 1.4 HIBS-CHARACTERISTICS, this paper proposes an evaluation method of the spectrum needs prediction for HIBS deployed as a supplementary for grounded base station, and presents the results of spectrum needs for HIBS in certain frequency bands identified for IMT. The capacity demand of HIBS in 2025 is predicted and the spectral efficiency of HIBS system is obtained by simulation, and the spectrum needs is defined as Capacity Demand/Spectral Efficiency. The main contributions of this paper are as follows: Firstly, to obtain the spectral efficiency of HIBS systems, we set up a simulation model with 7-cell networking in accordance with configuration parameters of WRC-23 AI 1.4, which equipped with beamforming antenna to improve spectral efficiency. And calculating the CDF curve of spectral efficiency by using Monte Carlo method allows the simulation result to more closely match the actual deployment. Secondly, in order to solve the problem of no actual HIBS traffic data, we propose a method based on the methodology provided in Recommendation ITU-R M.1768-1 to simulate the capacity demand, and then to predict the capacity demand of HIBS in 2025 by polynomial fitting. Finally, we have predicted the spectrum needs by calculating Capacity Demand/Spectral Efficiency. The spectrum demand range of HIBS for China rural area in 2025 predicted by the proposed method is from 80.30 MHz to 122.97 MHz. This method can effectively predict the spectrum needs of HIBS for various deployment scenarios, which plays an important role in driving the commercial deployment of HIBS.
The Polarization of the Planet-Hosting WASP-18 System

We report observations of the linear polarization of the WASP-18 system, which harbors a massive (∼10 MJ) planet orbiting close to its star with an orbital period of 0.94 days. We find that the WASP-18 system is polarized at ∼200 parts-per-million (ppm), likely from the interstellar medium predominantly, with no strong evidence for phase-dependent modulation from reflected light from the planet. We set an upper limit of 40 ppm (99% confidence level) on the amplitude of a reflected polarized light planetary signal. We compare the results with models for a number of processes that may produce polarized light in a planetary system to determine whether we can rule out any phenomena with this limit. Models of reflected light from thick clouds can approach or exceed this limit, but such clouds are unlikely at the high temperature of the WASP-18b atmosphere. Additionally, we model the expected polarization resulting from the transit of the planet across the star and find that this has an amplitude of ∼1.6 ppm, which is well below our detection limits. We also model the polarization due to the tidal distortion of the star by the massive planet and find that this is also too small to be measured currently.
Improvement of Bandwidth Fairness between TCP BBRv2 and CUBIC

Google proposed Bottleneck Bandwidth Round-trip propagation time version2 (BBRv2) congestion control algorithm to solve the bandwidth fairness problem of the BBRv1. The bandwidth fairness of BBRv2 depends on the Bandwidth Delay Product (BDP). When the bottleneck buffer is less than 2BDP, BBRv2 improves bandwidth unfairness in bandwidth sharing with loss-based congestion control algorithm such as CUBIC over BBRv1. However, when the bottleneck buffer exceeds 2BDP, BBRv2 has bandwidth fairness issues. In this paper, we proposed F-BBRv2 to solve the bandwidth fairness problem of BBRv2 by using the min_RTT value and adaptive pacing_gain method.
Tiny Neuron Network System based on RISC-V Processor: A Decentralized Approach for IoT Applications

The idea of Artificial Intelligence of Things (AIoT), a combination of Artificial Intelligence, especially Deep Learning, with edge devices in IoT networks, has recently emerged to reduce the communication cost, and server workloads and improve user experiences. This work presents our current research on a tiny neural network accelerator in a RISC-V System-on-Chip (SoC) to accelerate AI in IoT applications. This accelerator implements a variable-bit-precision MAC or a stochastic MAC to reduce hardware area and power consumption. The tiny AI accelerator has been successfully integrated into a low-power IoT SoC. The design has been implemented on FPGA technology using Arty A7 100T development kit with the operating frequency of 50MHz and the hardware resource of 12K slices. For the MNIST dataset, the accelerator with 8-bit precision can perform Convolutional Neural Network with an accuracy of 98.55%.
A Wideband High Gain Circularly Polarized Antenna Based on Nut-Shape Metasurface

This paper proposes a wideband high gain circularly polarized (CP) antenna based on a novel metasurface (MS) at 5.8 GHz. The struture of the antenna consists of two parts are stacked on top of each other. The bottom part is a square patch antenna with a diagonal slot at the center and four parasitic elements added to the sides of the driven patch to improve reflection coefficient and axial ratio of the proposed antenna. The top part is a novel MS, formed by $4\times 4$ nut-shape unit cells for improvement gain, matching impandance and CP bandwidth expansion. The antenna has a high gain of 9.35 $\boldsymbol{\text{dBi}}$ and compact size of $\boldsymbol{31\times 31\times 6.7\text{mm}^{3}}$. Wide bandwidth is another advantage of the proposed antenna: impedance bandwidth (S11 $\boldsymbol{ < -10\text{dB}}$ BW) of 1.8 GHz (29.66%) and axial ratio bandwidth (AR BW) of 1.34 GHz (21.42%). The anenna has left hand circularly polarized (LHCP) is suitable for electronic toll collection (ETC) application.
Telehealth Data-derived Visual Analytics for Health Informatics Applications in Coordinated Care of Patients with Multiple Comorbidities

We describe telehealth data-derived visual analytics (VA) approaches aiming to deliver better healthcare outcomes to our telehealth service users with multiple comorbidities through enhanced real-time and real-world clinical decisions. Our telehealth center provides telehealth services and integrates telehealth datasets with electronic medical record (EMR)-derived information. In addition to the continuous vital sign data acquired from years of telemonitoring, the telehealth datasets also contain large amounts of unstructured service data including intervention notes and lifestyle-related information. These data are analyzed by our natural language processing (NLP) program for extracting coded data and subsequent machine learning. We develop a Gantt chart-based approach to interactively visualizing these complex analytics results in order to perform insightful examinations of users’ health status. The interactive Gantt chart presentations provide clear and quick comprehension of users’ disease status and progression, medication management, and treatment outcomes. Our VA approach potentially can optimize clinical decision making, facilitate efficient care coordination, ensure care continuum, and provide cognitive supports for reducing workload burdens. To our knowledge, this is the first case of using Gantt chart model for analyzing telehealth datasets and the integrated health information of both providers- and users-generated sources.
Fast Warning System for Driver of Distraction with Traffic Object Recognition by Image Processing

Along with socio-economic development, the number of cars is increasing, which means more and more potential hazards on the road. These dangers come not only from the subjective behavior of the driver but also from objective incidents. Building an automated system to detect incidents in time plays an important role in reducing traffic accidents. This study proposes a fast image processing method to detect driver distractions to give timely warnings. In addition, the objects on the road that are at risk of causing an accident are also recognized and the system warns the driver in real time. Selected study results are also provided to verify the effectiveness of the method.
Pot Detection System Using YOLO

The ABU Robocon 2021 contest is a contest of throwing arrows into a pot using a robot. The highlight of this game is how the robot can throw arrows into the pot so that it gets a score within a predetermined time limit. In this study, a pot detection system is needed to know the pot to be detected and the estimated distance from the robot to the pot. At this time Deep Learning is often used for research and finding new methods such as YOLO (You Only Look Once). YOLO architecture requires a sophisticated computer architecture to run, so the solution is YOLOv3-tiny-prn. YOLOv3-tiny-prn is a more compact architecture than YOLOv3 and YOLOv3-tiny because the depth of convolutional layers is reduced, so the detection process is faster. So the method used in this study for pot detection is the YOLOv3-tiny-prn method. The error value obtained from the results of the distance estimation test using the YOLOv3-tiny-prn method is below 5%, but in the Scoring Spot 4 distance test there is an error of 100% because the pot is covered by the pot in front of it so it cannot be detected.