Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of 5G End-to-End Network Slicing in Transportation Field

Due to the characteristics of low delay and high reliability, 5G network slice has been widely concerned in the vertical industry, and has been successfully applied in smart electricity grid, live media, medical and other fields. However, it is still in the initial stage in the field of transportation. The development and evolution of various traffic scenes requires 5G network to provide end-to-end flexible customized services with large bandwidth, low delay and high reliability. Starting with 5G's flexible network customization ability, this paper analyzes the performance requirements of relevant business scenarios on the network, discusses the 5G network slicing implementation process of transportation business, explores the end-to-end slicing solution, and verifies that the end-to-end slicing technology can provide differentiated service level SLA guarantee for the transportation industry through test.
Research on Model and Simulation of Vehicle Network Based on LTE Technology

Vehicle Ad Hoc Network (VANET) is one of the important means to realize intelligent transportation system, which can improve road safety and traffic efficiency. At present, two key technologies of vehicle communication include DSRC (Dedicated Short-Range Communication) and LTE (Long Term Evolution). After 20 years of in-depth research, DSRC has reached sufficient technical maturity, but its performance improvement space is relatively limited. LTE is a relatively new 3GPP-based standard and is considered the strongest competitor to IEEE 802.11p. There are many mature analysis models in DSRC reliability analysis, however as LTE is a newly emerging vehicle networking technology, the existing analysis models have some problems that do not conform to actual traffic scenarios and incomplete analysis of beacon transmission errors. Although technological innovations allow LTE to have many features over DSRC, studies have found that DSRC outperforms in some respects.Given the existing shortcomings of analysis models, this paper proposes an LTE broadcast performance analysis model based on the Poisson distribution traffic model. This model uses the Markov State Transition Process to evaluate the probability of packet reception for LTE combined with four transmission errors. The numerical results of the analysis model is cross-validated through the network simulation results obtained by the MATLAB simulator, which proves the correct-ness of the model.
Negative-Bias-Stress-Induced Current Instability in Quasi-2D Tellurium Field-Effect-Transistors

Quasi-2D Tellurium (Te), holds great promise for high performance p-type field-effect-transistor (p-FET) in the future thanks to its high mobility and good stability. To pursue a practical application, its electrical stability of Te FET is of great concern. In this work, p-FETs based on quasi-2D Te synthesized by chemical vapor deposition (CVD) have been fabricated and corresponding bias induced instability has also been investigated under different negative bias stress voltage and stress time. Taking into consideration of the coupling effect of water/oxygen ionization, hydrogen ion and internal traps, three common models are proposed to understand the possible underlying mechanisms for the current instability raised by negative bias stress.
Throughput Optimization for Wireless Powered Interference Channels

This paper studies a general multi-user wireless powered interference channel (IFC) under the harvest-then-transmit protocol, where the communication in channel coherence time consists of two phases, namely wireless energy transfer (WET) and wireless information transfer (WIT). In the first phase, all energy transmitters (ETs) transmit energy signals to information transmitters (ITs) via the collaborative waveform design, while in the second phase, each IT transmits an information signal to its intended ET using the harvested energy in the previous phase. The aim is to jointly design the WET-WIT time allocation, the (deterministic) transmit signal at the first phase, and the transmit power of ITs in the second phase to optimize the network throughput. The design problems are non-convex and hence difficult to solve globally. To deal with them, we propose efficient iterative algorithms based on alternating projections; then, the majorization-minimization technique is used to tackle the non-convex sub-problems in each iteration. We also extend the devised design methodology by considering imperfect channel state information and non-linearity in energy harvesting circuit. The proposed algorithms are locally convergent and can provide high-quality suboptimal solutions to the design problems. The simulation results show the effectiveness of the proposed algorithms under various setups.
Dilated Convolution Network for Road Damage Detection

Road damage resulting from various issues poses a significant risk to traffic safety, which underscores the importance of timely and accurate detection and repair of road defects. Deep learning methods have made significant advancements in road damage detection due to their ability to automatically learn and adapt. The majority of them, however, rely on conventional convolution networks that do not provide precise location information during feature extraction, leading to inaccurate detections. Therefore, we propose a multi-scale convolution network for detecting road damage. After multiple layers of convolution, target location information obtained by depth features becomes sparse. We use the last three stages of multi-scale features for fusion and perform joint up sampling after regular convolution. The fusion of multi-scale features retains rich semantic and location information, while combining depth-separated convolution to reduce model computational complexity. For the attention calculation stage, a combination of channel attention and spatial attention is employed in order to eliminate irrelevant background factors and determine the edge contours of the damaged area accurately. We compare seven mainstream deep learning methods using the CPRID dataset. According to visualization comparisons and quantitative metrics, our network outperforms several current state-of-the-art methods.
A Dual-Cryptographic Coprocessor for Authenticating WBAN Applications

Wireless body area networks (WBANs) transmit highly sensitive human physiological data, which requires robust security measures to prevent data leakage. However, due to the small size and low power consumption limitations of WBAN devices, the hardware computational resources and speed may not meet the traditional cryptographic computation requirements. Therefore, the need for low-power data encryption circuits is becoming increasingly urgent. This paper proposes a dual-cryptographic coprocessor that accelerates both RSA and advanced encryption standard (AES) cryptographic algorithms for authentication and data encryption in WBAN applications. The coprocessor is integrated with a RISC-V processor to construct a programmable WBAN system for supporting authentication and data transmission between smart WBAN chips. The proposed design was implemented in a 55nm CMOS technology. The overall WBAN system operates at a clock frequency of 100 MHz with a power consumption of approximately 11 mW, fulfilling the authentication and data encryption requirements.
Internal and international migration: bridging the theoretical divide

The interdisciplinary field of migration studies is riven with binaries, one of the most fundamental of which is its split into internal and international migration, characterised by different literatures, concepts, methods and policy agendas. Most migration scholars nowadays are researching international migration, even though, quantitatively, internal migration is more important. Yet the distinction between internal and international moves becomes increasingly blurred, not only because of geopolitical events and the changing nature and configuration of borders, but also because migrants' journeys are becoming increasingly multiple, complex and fragmented. Nevertheless, there remain both many similarities and many differences between these two 'migration traditions'. The paper is in three main sections. First we present a schematic model which sets out 10 migration pathways which combine internal and international migration, and return migration, in various sequenced relationships. Second, we survey the limited literature which attempts to compare and integrate internal and international migration within the same theoretical framework both general models and some case-study literature from Mexico. We consider three approaches where theoretical transfer seems to hold potential systems analysis, studies of migrant integration, and the migration-development nexus. The final part of the paper looks in more detail at the case of Albania where since 1990 there has been contemporaneous mass emigration and internal migration. We deploy both quantitative and qualitative methods to examine the links between the two forms of migration in the Albanian context, demonstrating how closely they are entwined both in the macro-dynamics of regional population change and in individual and family biographies of mobility. In conclusion, we argue that there is considerable potential for integrating the study of internal and international migration, both at the theoretical and the empirical level. Too often one is studied without reference to the other, yielding a partial analysis. However, we baulk at attempting any 'grand theory' of migration which incorporates all types of migration, in all places and at all times.
Research on Self-Calibration and Adaptive Algorithm of Positioning Base Station Based on UWB

UWB (Ultra-WideBand) is a communication technology that has gradually emerged in recent years. It is widely used in the field of indoor positioning due to its low power consumption and strong anti-interference ability[1]. This paper mainly studies a method for autonomous calibration of coordinates between UWB base stations. The traditional base station calibration method requires the use of expensive measuring equipment and a lot of labor and time costs[2]. This method can enable the base stations to realie the self-calibration of the position according to the algorithm, thereby reducing costs and workload. In this paper, the self-calibration algorithm of UWB base station is firstly proposed. This algorithm calculates the relative position coordinates of each base station by mathematical method through the communication between base stations. Finally, after conversion, the absolute position information of each base station can be obtained quickly. Then the experiments show that the self-calibration method based on UWB communication can effectively locate the base station, and has high precision and reliability. In practical applications, this method can help users quickly complete base stations deployment and calibration, thereby reducing the cost of investment.In general, the research results of this paper provide important support and reference for the application of UWB technology in the field of indoor navigation and positioning. Future work will further refine the practicality and scalability of the base station self-calibration method.
Speech and Text-Based Motion Generation and Matching System

Speech and text-based motion generation is an emerging research area in both academia and industry. However, there is a problem that related studies have defined the generated motion structure differently so far. To solve this problem, we propose a novel system for matching the generated motion to SMPL-X. In this study we focused on using joint information from a pre-trained model and matched this model to another model which has different joint structure. Since this system runs on Unity, it can be applied to various industry fields. Detailed motion videos and codes are available in https://github.com/kinsingo/Speech-and-Text-based-Motion-Generation-and-Matching-System
Analysis of Vth Distribution Based on Physical Parameter Variation for 3-D TLC NAND Flash Memory

In this paper, the abnormal threshold voltage distribution (Vth) was analyzed considering physical variation in 3-D NAND flash memory cells. The neutral Vth distribution is extracted by technology computer-aided design (T-CAD) simulation and the erase and the program Vth distribution is simulated through the Monte-Carlo (M.C) method.
The Design of a High-Accuracy Sigma-Delta Modulator with 2-2 Mash Structure

A 2-2 mash Sigma-Delta modulator is presented in this paper. This 2-2 mash Sigma-Delta modulator consists of two 2-order cascade topology. Special parameters in the novel transfer function is calculated, that enables the modulator to process the signal ranging from supply tail to ground without losing linear performance. The overall circuit is designed with 0.35um standard mixed signal technology. At 1.92MHz sampling rate and 64 oversampling ratio, the measured signal-to-noise ratio for the overall circuit is more than 110dB around 1kHz.
A cloud-native framework for globally distributed capture and analysis of Internet Background Radiation

Among the existing methods for analysing internet traffic, one focuses on unsolicited and often harmful packets, referred to as Internet Background Radiation (IBR). IBR data can be captured by using cloud-based computing instances to listen for incoming packets and recording the received headers and contents in files using the PCAP format. No services are run on these hosts so all traffic can be regarded as non-legitimate. This work presents the research plan for capturing a year-long sample of the IBR arriving to up to 1500 geographically distributed sensors across a major service cloud provider. In combination with collection, a data analysis pipeline will be constructed to enable the querying of most relevant quantitative and qualitative aspects of the resulting dataset. The ultimate goal is to answer “how is the IBR characterised when captured within the context of cloud computing? ”. Results will include packet distribution according to the properties of the network, transport, and application layers, together with an evaluation of traffic linking to botnet activity such as Mirai and Moobot.
A Computational Approach to Assess Working Memory in Individuals with Aphasia

Aphasia is a disorder related to deficits in language expression and comprehension. requiring continuous efforts for treatment and rehabilitation. The most used methods to evaluate the progress of an aphasic patient require much time to be applied and interpreted and few of them are available in Portuguese. This paper describes a new method to assess the working memory usage of aphasic individuals based on a biomarker associated with the severity of the disorder. The study deals with the process of estimating a working memory score based on the Neupsilin neuropsychological test and the analysis of electroencephalographic signals collected while the user performs an n-back task. The study was applied to 14 volunteers, divided into two groups, one with aphasia and the other one without aphasia. As expected, the analysis of the n-back task showed a lower performance in aphasic individuals. The results also indicate that the Neupsilin test can be used to separate the two classes of individuals, with an average accuracy of 78.57 %, using three subtests related to the working memory. The scores can be used to build a statistical model to estimate the working memory usage based on the collected electroencephalographic signals.
Speech Enhancement Algorithm Based on Wave-U-Net

The traditional speech enhancement algorithms have the problems of overfitting, low generalization and performance degradation in non-stationary noise environments. This paper investigates a speech enhancement algorithm based on the Wave-U-Net architecture. The algorithm operates directly in the time domain using an end-to-end learning approach, allowing for integrated modeling of phase information and repeatedly resampling feature maps to calculate and combine features at different time scales. Experiments show that the enhanced speech signal of Wave-U-Net network model outperforms the deep neural network (DNN) model and the traditional Wiener filtering algorithm overall in terms of perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) speech evaluation metrics, and the algorithm has better robustness.
A Link Level Study on LDM for Mixed Broadcast-Broadband Service Delivery in 5G

This paper addresses the use of Layered Division Multiplexing (LDM) as a method to deliver multiple services in 5G New Radio (NR). LDM is a technique that allows multiple services to be delivered over the same time-frequency resource by allocating transmission power between different layers. A two-layered LDM system can be implemented in 5G NR to deliver both Single Frequency Network (SFN) broadcast service and broadband service over a single time-frequency resource. The integration of 5G beamforming into LDM allows for the creation of sufficiently wide beams for SFN broadcast service and narrow beams for broadband service. Theoretical analysis and link level simulation demonstrate that this approach can provide both services with more spectral efficiency and energy saving.
Efficient Video SoftCast For Intelligent Reflective Surfaces Assisted Symbiotic Radio Network

Intelligent reflective surfaces (IRS)-assisted symbiotic radio (SR) network provides significant communication performance in terms of high energy efficiency (EE) and spectral efficiency (SE), which has been envisioned as a promising solution for the next sixth generation (6G) wireless communication network. On the other hand, SoftCast is proposed as an alternative solution for wireless video transmission. In this paper, the SoftCast scheme is integrated with the IRS-assisted network, and a more efficient video SoftCast scheme is proposed for IRS-assisted SR. Specifically, the reciprocal symbiosis property of IRS-assisted SR is exploited, and the secondary transmitter enabled by the IRS is referred as the effective enhancement of the transmission video signal. The simulation results demonstrated the effectiveness of the proposed method.
Eye Diseases Classification Using Transfer Learning of Residual Neural Network

Eye diseases have long been a serious problem for people, including glaucoma, diabetic retinopathy, cataract and so on. Fortunately, their images in the fundus are distinguishable, which makes it possible to distinguish eye diseases by deep learning. In this paper, we propose an eye disease classification method based on transfer learning, in which ResN et model is used. After the fundus retinal image data is processed and the model structure is optimized, the accuracy on the test set reach 0.92908, and the accuracy on the training set is close to 1. We then calculate some indicators to evaluate our model. Meanwhile, we also use other pretraining models to replace ResN et for experiments, and finally find that our model has excellent performance in distinguishing different eye diseases.
Stereo matching based on census transformation of image gradients

Although multiple-view matching provides certain significant advantages regarding accuracy, occlusion handling and radiometric fidelity, stereo-matching remains indispensable for a variety of applications; these involve cases when image acquisition requires fixed geometry and limited number of images or speed. Such instances include robotics, autonomous navigation, reconstruction from a limited number of aerial/satellite images, industrial inspection and augmented reality through smart-phones. As a consequence, stereo-matching is a continuously evolving research field with growing variety of applicable scenarios. In this work a novel multi-purpose cost for stereo-matching is proposed, based on census transformation on image gradients and evaluated within a local matching scheme. It is demonstrated that when the census transformation is applied on gradients the invariance of the cost function to changes in illumination (non-linear) is significantly strengthened. The calculated cost values are aggregated through adaptive support regions, based both on cross-skeletons and basic rectangular windows. The matching algorithm is tuned for the parameters in each case. The described matching cost has been evaluated on the Middlebury stereo-vision 2006 datasets, which include changes in illumination and exposure. The tests verify that the census transformation on image gradients indeed results in a more robust cost function, regardless of aggregation strategy.
Iberian banks’ performance: a panel data analysis

This paper aims to analyze the performance determinants of investment banks in the Iberian Peninsula. To achieve this goal, we have studied 15 Portuguese banks and 53 Spanish banks between 2011 to 2020. To test the hypotheses we have used bank-specific variables and macroeconomic variables through the Generalized Method of Moments (GMM)-system estimation method by [1] and [2]. The results point out that internal determinants are the most important to explain bank performance in both countries. Still, the managers and shareholders have different perceptions depending on the measure of bank performance. To the best of our knowledge, this is the first study to analyze the performance determinants of the Iberian investment banks as a single joint market.
Refining System’s Needs Analysis Using Systems Engineering Process Approach

This paper carefully examines the literature concerning the system’s need analysis, as many studies provided a partial picture of how to conduct the analysis. It also discusses the usual mistakes in conducting a need analysis for application design and development exercises. The final part of the paper discusses the common misunderstanding of need analysis which led to the inaccuracy of the exercise. A clear need analysis framework is thus produced to clear this misunderstanding.
An Alzheimer's disease classification method based on ConvNeXt

Alzheimer's disease (AD) is an irreversible neurodegenerative disease that severely affects the cognitive ability and quality of life of patients. Deep learning techniques have great potential to classify the disease, but there are still some challenges: images of Alzheimer's disease are very similar to those of Mild Cognitive Impairment (MCI), and it is difficult to classify the disease by images only; many studies have focused on binary classification, but few have performed multi-classification for the progressive stages of the disease. In this paper, we proposed a new multi-classification method: Firstly, the ConvNeXt network is employed to extract features from AD images. Next, the subject's identity information is parameterized using Dynamic Multilayer Perceptron (DMLP) and projected onto the image features to enhance them. Finally, the multi-classification is performed using fully connected layers and softmax layers. Experiments demonstrate that the method shows higher accuracy than classical methods in multi-classification tasks, and also provides a method for deeper understanding and diagnosis of AD.