Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation in the Uplink of GEO Satellite Relay System with Dynamic Doppler Shift

In a relay satellite system, low earth orbit (LEO) satellites are the users of a geosynchronous earth orbit (GEO) satellite, and each highly dynamic GEO-LEO link has a different Doppler shift due to the movement of LEO satellites, which causes serious inter-resource-block(RB) interference and leads to significant degradation of capacity. In this paper, to reduce interRB interference caused by different Doppler shift and maximize the uplink capacity, we derive the mathematical formulae to describe the inter-RB interference with a fixed Doppler shift and propose a resource allocation approach. The problem is formulated as a non-convex optimization model and Lagrangian duality method is used to solve it. Finally, we present a subgradient algorithm to get the results. Simulation results show that the proposed resource allocation method compared to the QPSO (quantum-behaved particle swarm optimization) resource allocation algorithm has a better capacity after decreasing interRB interference.
A Flatness Predict Model Based on Deep Belief Network for Steel Rolling Process

The flatness deviation of the cold-rolled strip steel sheet reflects the product quality of strip steel, and it is a vital quality index to monitor in the cold rolling strip process. Given the problem of delayed detection of strip rolling at low speed, a practical and effective flatness predict model is established by a data-driven method, which integrates Mutual Information (MI) with Deep Belief Network (DBN) in this thesis. In the process of feature selection, MI technology is applied to the model, which can describe the quantitative relationship between flatness variables and process variables. Furthermore, it is the criterion for selecting the variables as the best input. Then, DBN is presented as the flatness predictor, it can reinforce the ability to extract features from raw data which with the character of high dimension and nonlinear. Firstly, the Contrastive Divergence learning (CD) is applied to pretraining the model by training the Restricted Boltzmann Machine (RBM) which is the basic unit of DBN. Then, a global back-propagation (BP) algorithm is used in the whole model’s fine-tuning, and the whole model parameters were optimized by Adaptive moment estimation (Adam) finally. As a result, the model’s performance, mean square error (MSE) is about matching 0.0009, which is better than the prediction model formed by BPNN or Stacked Auto Encoder (SAE). (Abstract)
Secure File Storage for Android Devices on Public Clouds

We propose MCOFS a secure cloud storage system for Android that combines the use of multiple public cloud providers with cryptography and redundancy mechanisms to ensure the confidentiality and availability of files even if one of the providers becomes hostile or suffers catastrophic data loss. Experimental results show that, while there is a performance penalty in comparison to the plain use of a single provider, it is small enough and a fair price to pay for the added guarantees.
Raw red onion intake and insulin resistance markers in overweight or obese patients with polycystic ovary syndrome: a randomized controlled-clinical trial

Background: Insulin resistance (IR) plays a pivotal role in the development of polycystic ovary syndrome (PCOS). Though hypoglycemic and hypolipidemic effects of onion have been suggested in type 2 diabetes mellitus, still, lack of data exist to investigate its hypoglycemic effect in PCOS. Objective: to assess the effects of raw red onion consumption on IR markers in PCOS. Methods: In this randomized controlled-clinical trial, 53 overweight or obese non-diabetic patients with PCOS were randomly allocated to either group of high-onion (HO; raw red onions: 2×40-50 g/day for overweight and 2×50-60 g/day for obese patients) or low-onion (LO; raw red onions: 2×10-15 g/day) for an 8 weeks. Fasting and 2-hour blood sugar, insulin, total testosterone, and progesterone, as well as homeostasis model of insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) were measured at baseline and after treatment. IR was defined as HOMA-IR≥3.8 in PCOS. Anthropometric indices and dietary intake were also evaluated. Results: After 8 weeks, no differences were observed in the IR markers including HOMA-IR neither within nor between the HO (from 3.53±1.22 to 4.08±1.70; P=0.090) and LO groups (from 3.58±1.75 to 3.32±1.36; P=0.338). Anthropometric and dietary parameters did not differ between the two groups (P>0.050). However, a non-significant increase was observed in the rate of menses occurrence in HO (17%) compared to LO group (6%) after 8 weeks. Conclusions: Although raw red onion intake could not significantly improve IR markers in non-diabetic patients with PCOS, it could increase the chance of menses occurrence. Further investigations are warranted to determine the effects of onion in these patients.
Numerical Computation Method for Human Exposure to Non-uniform Magnetic Field

Low-frequency dosimetry is commonly assessed by evaluating the electric field in the human body using the scalar potential finite difference method. In this paper, incomplete LU factorization preconditioned biconjugate gradients stabilized method is used to solve electric scalar potential finite-difference equations. Induced E-field by this in-house numerical solver is validated using two other numerical tools. The developed solver is able to run with a personal computer and a satisfactory efficiency is attained.
Optimal Residential Load Scheduling in Dynamic Tariff Environment

The optimal scheduling of appliances under real time pricing can potentially provide advantages to consumer as well as to utility. Demand Side Management (DSM) is a key challenging area in smart grid. The main responsibility of a DSM system is to schedule the appliances in an optimal way to reduce the electricity cost while considering the consumer requirements. Optimal scheduling of appliances will reduce the peak demand and improve the power system efficiency. This paper studies the DSM strategies for residential consumers, where the customer provides the desired operation for each shiftable electricity appliance which is accompanied with time limits. The use of Intelligent Decision Support Systems has become vital for load scheduling which supports consumers to respond to dynamic tariff environment. The paper discusses how the Exact method, Genetic algorithm and the Mixed integer linear programming are applicable in load scheduling. Next, we proposed two novel approaches to optimize the load scheduling, namely Vacant Power Method and Prior Ranking Method. Furthermore, a comparative analysis of these DSM strategies were performed considering the electricity cost and the processing time.
Inter-Personal Relation Extraction Model Based on Bidirectional GRU and Attention Mechanism

Inter-Personal Relationship Extraction is an important part of knowledge extraction and is also the fundamental work of constructing the knowledge graph of people’s relationships. Compared with the traditional pattern recognition methods, the deep learning methods are more prominent in the relation extraction (RE) tasks. At present, the research of Chinese relation extraction technology is mainly based on the method of kernel function and Distant Supervision. In this paper, we propose a Chinese relation extraction model based on Bidirectional GRU network and Attention mechanism. Combining with the structural characteristics of the Chinese language, the input vector is input in the form of word vectors. Aiming at the problem of context memory, a Bidirectional GRU neural network is used to fuse the input vectors. The feature information of the word level is extracted from a sentence, and the sentence feature is extracted through the Attention mechanism of the word level. To verify the feasibility of this method, we use the distant supervision method to extract data from websites and compare it with existing relationship extraction methods. The experimental results show that Bi-directional GRU with Attention mechanism model can make full use of all the feature information of sentences, and the accuracy of Bi-directional GRU model is significantly higher than that of other neural network models without Attention mechanism.
Rate Analysis of Full-Pilot Zero-Forcing in Cell-Free Massive MIMO with Low- Resolution ADCs

In contrast to classical zero-forcing (ZF) receiver, a new one called full-pilot zero-forcing (fpZF) receiver is exploited in our work. Based on this, we investigate the achievable uplink rate of a cell-free massive multi-input multi-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) at the access points (APs). In order to carry out the tractable analysis on multiple quantization bits, we advocate the additive quantization noise model (AQNM). Quantization technology is a very effective method to lower the cost of transmission in the case where high data fidelity is not needed. After each AP receiving the payload data transmitted from all users, the quantization procedure is implemented in a distributed manner. Then a closed-form expression of achievable uplink rate is derived, which helps us to investigate system performance. The results in our simulation experiments indicate the fpZF receiver performs much better than the conjugate beamforming (CB) receiver when high-bit ADCs are used to process these received signals. However, when few-bit ADCs are adopted, its performance will decrease a lot and is dramatically inferior to the CB receiver, which is not as well as we expect. This observation demonstrates the fpZF receiver is more sensitive to the quantization operation than the CB receiver.
26.8 A Trimodal Wireless Implantable Neural Interface System-on-Chip

Implantable biomedical devices (IMDs) capable of injecting a designated current into target neural tissue to modulate neural activity have been proven therapeutically effective. The next generation of IMDs is expected to sense changes in the evoked neural activities, allowing recorded data to be used as feedback mechanism to adjust stimulation parameters/patterns, making these systems even more effective while reducing power consumption [1]. Recently, optogenetic neuromodulation with distinct advantages of cell-type specificity, high temporal precision, and rapid reversibility has been added to researchers' arsenal and resulted in the development of IMDs with optical stimulation capability [2], [3]. An IMD with both optical and electrical stimulation capabilities, plus neural recording, is expected to offer ultimate flexibility to users for executing advanced neuromodulation paradigms that are not possible with today's technology. This may also give users the ability to lower optical stimulation threshold by priming the neural tissue with sub-threshold current. In [4], al 13 functions are presented; however, optical stimulation is not integrated, channel counts are limited, and the power source is a battery. A key feature that brings an IMD closer to clinical use is wireless power delivery and data communication. Here we present a fully integrated, wireless, and high-channel-count trimodal neural interface SoC.
Learning Automata-Based Scalable PCE for Load-Balancing in Multi-carrier Domain Sequences

The prior selection of a domain sequence is a key issue for an optimal establishing of inter-domain paths. The architecture based on the path computation element (PCE) for selecting the domain sequence was proposed. Inter-domain PCEs were established that could have TE information about the link status of the multi-domain network. Confidentiality fails in the case of multi-carrier domain sequences because the inter-domain PCEs are not controlled by certain network operators. The inter-domain PCEs are also exposed to overload due to the calculation of the domain sequences. Confidentiality and scalability problems are avoided by proposing the per-domain technique based on PCE, where the PCE of the source domain has learning automata (LA) for the selection of multi-carrier domain sequences. The selection of the inter-domain path is made with a low complexity from a set of paths belonging to different multi-carrier disjoint-domain sequences. The incorporation of an LA-PCE in the source domain of the connection between domains allows decreasing the blocking probability with respect to BGP. Scenarios have been used for uniform traffic load between pairs of domains as well as links exposed to congestion.
Visual Perception and Modeling for Autonomous Apple Harvesting

Visual perception and modelling are essential tasks in many vision-dependent robotic tasks. This work presents a robotic vision system to perform fruit recognition, modelling, and environment modelling for autonomous apple harvesting. The fruit recognition applies a deep-learning model Dasnet to perform detection and segmentation on fruits, and segmentation on branches. Fruit modelling localises the centre and computes the grasp pose of each fruit based on Hough Transform. Environment modelling adopts Octrees to represent the occupied space within the working environment of the robot. The robot control computes the path and guide manipulator to pick the fruits based on the computed 3D model of the crop. The developed method is tested in both laboratory and orchard environments. Test results show that fruit recognition and modelling algorithm can accurately localise the fruits and compute the grasp pose in various situations. The Dasnet achieves 0.871 on F1 score of the fruit detection. Fruit modelling achieves 0.955 and 0.923 on the accuracy of the fruit centre estimation and grasp orientation, respectively. To illustrate the efficiency of the vision system in autonomous harvesting, a robotic harvesting experiment by using industry robotic arm in a controlled environment is conducted. Experimental results show that the proposed visual perception and modelling can efficiently guide the robotic arm to perform the detachment and success rate of harvesting is improved compared to the method which does not compute the grasp pose of fruits.
Accelerated Expanding-Window FEC Using RS-Code for Real-Time Video Streaming

Real-time video streaming is taking off in recent years. Video is predicted to account for 82% of Internet traffic by 2022, a growing share of which will take the form of live streaming [1]. Forward Error Correction (FEC) [2] is commonly applied to protect transmitted data in real-time video streaming, by sending redundant parity packets to recover the lost packets on the spot.
Attention-Based User Temporal Model for Recommendation

For the traditional recommendation system, the user model is represented by the user’s historical behaviors, but the user’s interest is not static, and the user’s interest drift affects the final recommendation effect of the recommendation system. However, most of the work to solve the drift of interest is to design long-term and short-term models for users, Using RNNbased models with different time spans to learn an overall embedding of user history sequences. It is not possible to dynamically monitor user interest drift. In this work, we propose a user model named Attention-Based User Temporal Model (AUTM). The key design of our work is to use the attention model to dynamically assign importance weights to user embedding of RNN network outputs at different times, and then combine the static user embedding. Compared to a set of baselines in real-word dataset, our model shows better performance in prediction precision and Area Under Curve (AUC) score.
16.4 A Calibration-Free 71.7dB SNDR 100MS/s 0.7mW Weighted-Averaging Correlated Level Shifting Pipelined SAR ADC with Speed-Enhancement Scheme

With the increasing demand for next-generation communication, the trend of developing wide-bandwidth, high-resolution ADCs has emerged, where pipelined SAR (PIPE-SAR) ADCs [1], [2] have become popular owing to their excellent power-efficiency due to the fewer number of opamps. However, the finite opamp gain error limits the resolution of PIPE-SAR ADCs. Without calibration, this work proposes the weighted-averaging correlated level shifting (WACLS) technique for PIPE-SAR ADCs to theoretically eliminate the finite opamp gain error and operate with a speed-enhancement scheme to overcome the speed-degradation drawback of the prior two-phase CLS-based residue amplification [3], [4]. In addition, a duty-cycle-controlled ring amplifier (ringamp) with quick start-up is proposed for power saving.
N-Ray Ground Interference: Extending the Two-Ray Interference Model for 3D Terrain Shapes

The two-ray path loss model is a well-known model in wireless communications and has also found wide-spread use in vehicular network simulation. However, its applicability in three-dimensional scenarios is questionable as it assumes a planar ground surface between sender and receiver. In contrast, a terrain shape of varying inclination can lead to an arbitrary number of reflected rays, which cannot be handled by the two-ray model. Therefore, we present the derivation and impementation of an n-ray ground interference model to estimate the corresponding path loss. Using the new model, two sample scenarios are analyzed to compare the path loss resulting from 2-ray and n-ray ground interference. The results indicate considerable differences, emphasizing the need for the n-ray ground interference model in three-dimensional V2X scenarios.
Application of Regularized GRU-LSTM Model in Stock Price Prediction

The stock market is a highly complex nonlinear movement system, and its fluctuation law is affected by many factors, so the prediction of the stock price index is a very challenging task. There are many examples showing that Neural Network algorithms are well suited for such time series predictions and often achieve satisfactory results. In this paper, based on the existing models, we proposed a Regularized GRULSTM neural network model and applied it to the short-term forecast of closing price of the two stocks. The experimental results show that our proposed model is superior to the existing GRU and LSTM network models in stock time series prediction.
Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural Network

Neural network has shown promising performance on coreference resolution systems that uses mention pair method. With deep neural network, it can learn hidden and deep relations between two mentions. However, there is no work on coreference resolution for Indonesian text that uses this learning technique. The state-of-the-art system for Indonesian text only states the use of lexical and syntactic features can improve the existing coreference resolution system. In this paper, we propose a new coreference resolution system for Indonesian text with mention pair method that uses deep neural network to learn the relations of the two mentions. In addition to lexical and syntactic features, in order to learn the representation of the mentions' words and context, we use word embeddings and feed them to Convolutional Neural Network (CNN). Furthermore, we do singleton exclusion using singleton classifier component to prevent singleton mentions enter any entity clusters at the end. Achieving 67.37% without singleton exclusion, 63.27% with trained singleton classifier, and 75.95% with gold singleton classifier on CoNLL average F1 score, our proposed system outperforms the state-of-the-art system.
Ultra-Low Power Relaxation Oscillators survey: Design Trends and Challenges

The Relaxation oscillators (ROSCs) offer a better trade-off between power consumption, physical area and accuracy. This paper presents a study of recently published ultra-low-power relaxation oscillators with low-temperature coefficient, identifying its characteristics, performance parameters, and main challenges. A brief review show as state-of-the-art circuits handle with PVT (Process, voltage, and temperature) variations. By the end of this analysis, some trends in relaxation oscillators design are discussed.
Comparison of Antenna Parameters for Different Substrate Materials at Terahertz Frequency Region

Terahertz band communication is considered to be one of the most potential aspects of networks beyond 5G. However, it has been observed that antennas designed for this region still have very high losses. Dielectric substrate plays a key role in determining the antenna parameters like Sparameter, VSWR, 3D radiation pattern, directivity and gain. This paper provides a comprehensive solid background of the previous research works done on microstrip patch antenna in the terahertz region. Furthermore, a design for circular microstrip patch antenna consisting of a copper patch primarily focused on terahertz band communication has also been proposed. Simulation of the proposed antenna is carried out using an electromagnetic field solver software called Computer Simulation Technology (CST). The antenna’s performance based on different types of substrates such as Polyimide, Teflon, Quartz and Silicon Dioxide is analyzed between frequencies ranging between 1.5 to 1.7 terahertz (THz). The minimum return loss achieved for the fore mentioned resonant frequency range is-22.336dB for Teflon and the corresponding gain is 4.71 dB, which represents the optimum results among the four substrates
ADPM: An Alzheimer’s Disease Prediction Model for Time Series Neuroimage Analysis

Alzheimer’s Disease (AD) is a form of dementia which causes memory, thinking, and behavior disorders in humans. Effective early diagnosis and treatment of AD is of fundamental importance as it can reduce disease progression, allow more effective management of symptoms, facilitate timely patient access to advice and support, and lower associated costs of health care. Given that Alzheimer’s typically progresses in stages over an extended period of time, we propose that automated analysis of time sequential data may enhance disease prediction. We present a novel time-series Alzheimer’s Disease Prediction Model (ADPM) comprising Random Forest (RF) region of interest (ROI) selection and Gated Recurrent Units (GRU) prediction. Experiments show that our methodology achieves higher classification accuracy in comparison to existing algorithms, and can facilitate prediction of early onset AD. Furthermore, testing demonstrates that random forest ROI selection can identify disease-relative brain regions across different image modalities (MRI, PET, DTI).
Using Structured event to represent complaints of patients: a medical assistant for doctors

Extracting relations between entities from complaints of patients is a significant but challenging problem in intelligent medical diagnose. It can help doctors to record the main information from the complaints of patients. As the development of technologies, Doctors need a more effective and convenient way to capture entire information from patients’ complaints and build Electronic Health Records (EHRs). This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture. The event generation model adopts an open Chinese Information Extraction (open Chinese IE) and build a part-of-speech tagging to do dependency grammar analysis. Two kinds of evaluations are taken. One is metrics recall-oriented understanding for gusting evaluation (ROUGE). It measures the fitness of the generated events with the standard reference from doctors. The other are accuracy and Matthews Correlation Coefficient (MCC). They test the performance of grammar analysis. The results show our model have an excellent and robust performance.