Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lightweight Adaptive Attention Based Transportation Mode Detection Model Using Sensors Integrated in Smartphones

In transportation planning, user transportation mode is a significant part of traffic survey and an important basis for traffic design. However, how to obtain user transportation mode faces great challenges due to the lack of expert knowledge, high energy consumption, and limited usage scenarios. To tackle these challenges, in this paper, we propose a lightweight adaptive attention-based transportation mode detection model using sensors integrated in smartphones. We adopt Multi-Head Self-Attention (MHSA) mechanism and Fully Connected Residual Network (FR) in feature learning to improve model accuracy. Meanwhile, MHSA is highly parallelizable and has a compact parameter set, making it more suitable for mobile edge devices with limited device memory and computation capability. Adding residual connections to the model can improve its information transfer efficiency and reduce its training time. Extensive experiments on SHL datasets demonstrate that our proposed model outperforms five state-of-the-art baselines and achieves 4.20% higher accuracy than the best baseline on SHL dataset.
Adaptive wavelet-based multifractal model applied to the effective bandwidth estimation of network traffic flows

The authors investigate effective bandwidth estimation and Quality of Service (QoS) aware bandwidth provisioning for multifractal network traffic flows. They develop a novel adaptive wavelet-based multifractal model (AWMM) by using properties of the wavelet coefficients of multifractal cascade processes. The proposed AWMM has real-time updating capability and proves to be efficient in capturing multifractal network traffic characteristics. In addition, the authors derive an analytical expression for the effective bandwidth estimation of AWMM traffic flows, capable of being used to meet desired byte loss probabilities. Finally, they present an online effective bandwidth estimation algorithm that is incorporated into an adaptive bandwidth provisioning scheme and comparatively evaluated against some other bandwidth allocation methods.
Three Dimensional Liquid Metal Light Microrobot

Gallium-based room temperature liquid metal has attracted attention as a new functional material that has emerged in recent years. Its excellent conductivity, mobility and deformability have also led to the exploration of its application in many fields. Many applications are based on the precise control of liquid metals. The commonly used control methods are magnetic field control and electric field control. However, due to the lack of reliable and effective control, only a single liquid metal droplet can be controlled. In this paper, light foam balls are used to modify liquid metal so that they can be traced by electrodes to control movement in three-dimensional space and retain the properties of pure liquid metal. At the same time, several experiments are designed to demonstrate that the lightweight and reconfigurable liquid metal (LRLM) microrobot has excellent motion characteristics and cooperative clustering ability, which greatly expands the application range of liquid metal.
Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus Images

Proliferative Diabetic Retinopathy (PDR) is a seri-ous retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on im-proved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832,0.9265,0.9897,0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.
Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Multi-User Communications

Holographic multiple-input multiple-output (HMIMO) with a spatially continuous aperture is a promising solution for future radio access to handle the explosively increasing data demands. As a key enabler of HMIMO, the reconfigurable refractive surface (RRS) can serve as an antenna array with numerous programmable radiation elements. In this paper, we consider a multi-user system with an RRS-aided base station (BS) where the transmit signal is refracted by the RRS towards the users. A beamforming scheme is developed via codebook design and beam training. A larger codebook size implies more codewords, each corresponding to a directional beam. When the codebook size increases, the directivity of the refracted beam is enhanced, bringing a higher data rate. However, it also leads to an exponential growth of the training overhead. To achieve the critical tradeoff between the data rate and overhead, we evaluate the system throughput and model the relation between the codebook size of the RRS and the throughput mathematically. The optimal codebook size is then derived given different user distributions. Simulation results verify our theoretical analysis and show the influence of both codebook size and RRS size on the throughput.
The temporal imprint of mobile phone emission level when running various applications in 4G versus 5G networks

Human exposure to electromagnetic fields emitted by mobile communications sources is dominated by the mobile phone radiation. Not only the dose of radiation is important, but also dose- or fluence-rate. In this regard, present research emphasizes the features of the dynamics of mobile terminal emission levels by means of statistics, nonlinear data analysis with recurrence quantification and time-frequency analyses. Time series of radiated electric field strengths were recorded and processed for a set of five mobile applications usage per communication standard. Both 4G-LTE emission channel of 20 MHz bandwidth and 5G-NR FR1 emission channel with a bandwidth of 40 MHz were studied. Differences between 4G and 5G emissions variabilities are presented, and application-specific features of the time imprint are highlighted.
Improving the Energy Efficiency of Aerial-Terrestrial Communication Using Relays

Telecommunication is a fast-growing industry where wireless technology is one of the vital areas of the telecommunication industry, and many changes and capabilities provide with each new generation. There are various requirements in the future 5G network to overcome, but along with that energy efficiency is a huge responsibility to achieve and reduce carbon footprint in wireless networks. In wireless systems, relays can apply for improving the throughput, system capacity as well as minimising the transmission power. In this paper, we present a study on energy efficiency for a relay based system with High Altitude Platform stations, specifically to increase energy efficiency. The decoding & forward (DF) relay schemes and Raleigh channels are used in this relaying network to analyse the transmitting power and hence the energy efficiency. Simulations were conducted to study the energy efficiency performance based on the theoretical developments for the proposed relay based system. Our results show that a relay based system under certain channel conditions can improve the energy efficiency of the transmission compared to a no-relay based transmission system.
Collision Risk Assessment and Costmap Construction Methods for AUVs Using Forward-Looking Sonar

Autonomous underwater vehicles (AUVs) have been widely employed in a variety of tasks, ranging from environmental exploration and resource surveying to complex tasks that achieve superhuman performance. In this paper, a collision risk assessment method is proposed to estimating the probability of collisions between AUV s and obstacles. In the proposed method, an assessment model composed of a distance risk function and an orientation risk function is defined to calculate the collision risk. A rule base is designed to classify the collision risk of different levels while sending warning signals. In addition, a costmap construction method using forward- looking sonar is proposed, which estimates the collision cost and sonar cost to ensure the safety of AUVs. And the aspect ratio of the costmap is changed so that the planned path can meet the minimum turning radius constraint of the AUVs. The effectiveness of the proposed methods is demonstrated in a series of simulations.
A 25% Duty -Cycle Clock Generation Circuit in 0.13 μ m CMOS

A 25% duty cycle clock generation circuit for wireless transceiver is designed in this paper, including divider based on current mode logic (CML) latch, the AND gates and buffers. The advantages and disadvantages of main structure and the principle of the clock generation circuit are analyzed. The key sub-circuits and layouts such as the divider-by-two, logic gates and buffers are designed based on 0. 13μ m CMOS process. The simulation results show that the operating frequency range of the 25% duty cycle clock generation circuit is 0.02-2.5GHz, and the rise (fall) time is 0.02-ns.
SWVie-Food: A Dataset for Recognizing Foods in Southwest Vietnam Based on Deep Learning

Recognizing foods in general and foods in Vietnam, in particular, has been the subject of many studies. However, we have found no attempts to recognize popular local foods from the southwest of Vietnam. In this study, we introduce an SWVie-Food dataset consisting of 3,022 images from 50 categories of local foods in southwest Vietnam. Then, we fine-tune deep learning models for recognizing foods. The vision Transformer model for food recognition achieves a mAP of 0.931, which outperforms MobileNetV3, YOLOv5, and YOLOv7 with mAP scores of 0.772, 0.831, and 0.870, respectively. Finally, we build a mobile application to assist users to recognize foods from uploaded images or real-time. In addition, our application provides information about the origin, ingredients, and recipes of local foods. Our study contributes to advertising tourism in the southwest of Vietnam.
A Secure Turbo Codes Design on Physical Layer Security Based on Interleaving and Puncturing

Nowadays, improving the reliability and security of the transmitted data has gained more attention with the increase in emerging power-limited and lightweight communication devices. Also, the transmission needs to meet specific latency requirements. Combining data encryption and encoding in one physical layer block has been exploited to study the effect on security and latency over traditional sequential data transmission. Some of the current works target secure error-correcting codes that may be candidates for post-quantum computing. However, modifying the popularly used channel coding techniques to guarantee secrecy and maintain the same error performance and complexity at the decoder is challenging since the structure of the channel coding blocks is altered which results in less optimal decoding performance. Also, the redundancy nature of the error-correcting codes complicates the encryption method. In this paper, we briefly review the proposed security schemes on Turbo codes. Then, we propose a secure turbo code design and compare it with the relevant security schemes in the literature. We show that the proposed method is more secure without adding complexity.
A Comprehensive Review on Quality Prediction of Fruits and Vegetables using Feature Extraction and Machine Learning Techniques

The quality estimation of fruits and vegetables plays a vital role in the field of agriculture. This paper reviews the latest improvements in estimating the quality of fruits and vegetables as well as grading them using machine learning techniques. As fruits and vegetables have high nutritional value, their sales are on high demand. The prime importance is given to the supply of toxin-free, premium quality products to the end-users. Quality of a fruits and vegetables highly affected by detecting the defects on them. Keeping the spoiled foods along with good food may contaminate the whole collection. Features of interest are needed for proper identification of food product. After extracting and refining features of interest, the images can be trained to error free categorization. This paper presents an elaborated description of various feature extraction and machine learning techniques to identify and grade different kinds of fruits and vegetables. This research study has reviewed many articles to sort out the problems in estimating the quality and classifying them according to the need. The results of this review show that incorporating image processing and computer vision techniques with machine learning techniques surpasses the traditional methods.
Research on Optimization of Enterprise Financial Management System Based on Big Data Hadoop

At present, China is in the era of big data, and enterprises are stepping into a new stage of transformation and development. With the advent of big data era, enterprises will encounter the problem of massive data, and the information explosion makes financial data management extremely important. At the same time, the financial data of electric power enterprises has the characteristics of real-time, sudden, volatile and so on, so it is very necessary to build the enterprise financial data management system based on big data. This paper analyzes the current environment of financial data management, and analyzes the key technologies in three aspects: NoSQL's big data storage management, Hadoop based financial big data preprocessing and financial big data analysis and mining algorithm. Based on the above analysis, this paper studies how to build a financial management analysis system,which greatly improves the management efficiency and accuracy of financial data of electric power enterprises.
Learning to Manipulate Tools Using Deep Reinforcement Learning and Anchor Information

Endowing robots with tool manipulation skills helps them accomplish challenging tasks. While robots manipulate tools to achieve goals, the alignment of tools and targets is a noise-sensitive and contact-rich task. However, it is difficult to access the accurate pose of the tool and the target. When there is unknown noise in the observations, reinforcement learning can't be sure to perform well. In this paper, we define the easier-to-obtain accurate task-related information as anchor information and introduce a tool manipulation method based on reinforcement learning and anchor information, which can perform well when the observations include unknown noise. To evaluate the method, we build a simulated environment ToolGym, which includes four different kinds of tools and different noise sampling functions for each tool. Finally, we compare our method with baseline methods to show the effectiveness of the proposed method.
Pose Determination by Monocular Vision Based on Artificial mark

In the field of computer vision, 6-DoF pose estimation of the object is one of the research keys for many applications. In this paper, a method to recognize and locate artificial marks based on the parallel line segment is put forward, in order to resolve the problems about short recognition distance, large computational resource and low efficiency of multi-target recognition. One innovation of this method is recognizing the ellipses long axis instead of the line segment which can increase the robustness of recognition. Another is an evaluation coefficient proposed for the identification of multiple targets, so that the targets can be selected effectively and their relative pose can be calculated at the same time. In the contrast experiments with classical checkerboard and ARToolKit mark, our algorithm has obvious advantages in success rate of recognition, measurement distance, calculation efficiency and robustness, which would bring a wide application prospect in the case of low computational cost and high real-time requirement.
Kubernetes-Based Network Functions Orchestration for 5G Core Networks with Open Source MANO

Due to the constraints imposed by the 5G requirements in terms of low latency, high availability, but also because of the need to have flexible support for business models, mobile core networks have evolved towards a cloud-native (CN) approach. This implies a whole new approach to Network Function Virtualization (NFV), ensuring an adaptation of Virtual Network Functions (VNFs) running in virtual machines (VMs). These were phased out in favor of Cloud-native Network Functions (CNFs), or Kubernetes-based Network Functions (KNFs) as they are also called in Open Source MANO (OSM) terminology. This paper discusses the KNF -based deployment of an open-source 5G core network - free5GC, using OSM and following a cloud-native approach. The experiments highlighted the advantages of choosing a containerized approach in terms of availability, obtaining an average downtime of about five times shorter (5 seconds) than in the case of virtualization (25 seconds).
Dual-Band Microstrip Antenna Array Design for Low Power Energy at Frequencies 900 MHz and 1800 MHz

One form of energy utilization from energy sources that is wasted in the wild is to capture energy from radio frequency (RF) with an antenna. This wasted energy is generally very weak. In this study, a dual-band microstrip antenna is designed by calculating the antenna parameters and performing simulations to obtain the best antenna characteristics. The dual-band rectangular microstrip antenna array designed at frequency 900 MHz has S11 of −23.6 dB and a gain of 8.095 dBi while at frequency 1800 MHz it has S11 of −37.4 dB and a gain of 9.895 dBi. The design of the antenna in the form of an array has improved the performance of the antenna in terms of gain, S11, VSWR, and effective aperture.
Absolute Properties of the Main-Sequence Eclipsing Binary Star WW Camelopardalis

We present absolute photometric observations in uvbyβ and 5759 differential observations in the V filter (the most complete light curve ever obtained) measured by a robotic telescope, as well as radial velocities from spectroscopic observations of the detached, eccentric, 2.3 day, double-lined, eclipsing binary star WW Camelopardalis. Absolute dimensions of the components are determined with high precision (better than 1% in the masses and radii) for the purpose of testing various aspects of theoretical modeling. We obtain 1.920 ± 0.013 M⊙ and 1.911 ± 0.016 R⊙ for the primary, and 1.873 ± 0.018 M⊙ and 1.808 ± 0.014 R⊙ for the secondary. The effective temperatures and interstellar reddening of the stars are accurately determined from new uvbyβ photometry: 8350 ± 135 K for the primary and 8240 ± 135 K for the secondary, corresponding to a spectral type of A4m for both, and 0.294 mag for Eb-y. The metallic-lined character of the stars is revealed by high-resolution spectroscopy and uvbyβ photometry. Spectral line widths give rotational velocities that are synchronous with the orbital motion in a slightly eccentric orbit (e = 0.0098). The components of WW Cam are main-sequence stars with an age of about 490 Myr according to models.
Panoptic Segmentation Datasets for Rail-based Autonomous Vehicle Under Mixed-traffic Scenario

The challenge of percept the complex environment through visual recognition has turned the tide of research on image recognition toward panoptic segmentation by unifying the distinct task of instance and semantic segmentation into a single architecture. This work applies panoptic segmentation for the inner-city tram in the environment where the railway intersects with the road. Our study on Cityscapes, COCO-Stuff, and RailSem19 public datasets has limitations due to unsuitable semantic and instance masks for our mixed traffic scenario. Thus, we consider creating our self-curated dataset, which was collected on the route of the Batara Kresna railbus, for the model to generalize our local context better. The data consists of a combination of 1000 frames of images with a dense-pixel panoptic annotation. Among those sets, 200 of them are specified and taken using the front camera to maximize the occurrence of the railway. We decided to calculate the occurrence of an object and class distribution as an initial data quality assessment.
Self-Attention Generative Distribution Adversarial Network for Few- and Zero-Shot Face Anti-Spoofing

With the exponential growth of facial authentications, the face anti-spoofing area has come to play an indispensable role as a shield, protecting those systems against facial impostures. However, because most current anti-spoofing technologies work with type-specific supervision, they are only effective in their respective spoof types, which means they are unlikely to prove robust for unidentified attack forms that are beyond their predefined supervised limitations. With this point in mind, we herein propose a novel Adversarial Distribution Generative Network (ADGN) that extends its spatial attention to a comprehensive global context, thus extensively raising the level of generality for unknown cases that inherently provide few or even no clues with which to learn. In this paper, we are more in favor of speculating on 3D mask attacks, where a great scarcity of prior knowledge is virtually inevitable due to their prohibitive costs. We also demonstrate the resilience of our proposed model and test it against publicly available datasets on both seen and unseen spoof scenarios. This intends to show how our model provides competitive detecting performance against a wide range of spoof types in comparison with previous state-of-the-art methods.
Information Map Prediction based on Learning Network for Reinforced Autonomous Exploration

Autonomous exploration is the prerequisite for reli-able robot navigation in unknown environments via providing the necessary information. The goal is to build a precise environment map while at the same time locating the robot robustly. To achieve this goal, this paper develops an information map that jointly provides the information of mapping and localization uncertainty. It consists of a localization uncertainty map and a model uncertainty map. The former evaluates the landmarks around the robot and the latter indicates the map completeness based on information theory. The information map serves as the input for making better decisions of where-to-go to achieve higher localization and mapping performance. To improve the efficiency of information map prediction, we develop a learning-based map prediction framework, where a dataset is established autonomously. The effectiveness and efficiency of the developed framework are verified through experiments in simulation environments.
DROP: Deep Reinforcement Learning Based Optimal Perturbation for MPPT in Wind Energy

The fluctuating nature of wind energy has inspired researchers to look for a fast, efficient Maximum Power Point Tracking (MPPT) algorithm. The MPPT method aims to harness maximum power at varying wind speeds by adjusting rotor speed. Our contribution to the wind MPPT task is twofold. First, we use a predictive model to map the current operating point of the turbine speed and output power to the optimal operating point (i.e., optimal turbine speed for maximum output power). Second, we propose a Deep Reinforcement Learning based solution that provides adaptive speed control to reach the MPP fast and precisely. Our experimental results demonstrate the superior performance of our method compared with the existing techniques.