Miracle-dz/UniFAR-FTUs huggingface dataset:
Waveform Design of Integrated Communication and Navigation based on Orthogonal Time Frequency Space

The integration of communication and navigation is the development trend of future communication technology. We propose a communication and navigation integration strategy based on OTFS that can adapt to high-speed motion communication and navigation scenarios. For communication, OTFS can provide more significant performance gains in the case of high Doppler extension. In addition, OTFS has a naturally sparse channel model, which provides us with a good convenience for estimating channel parameters. We transform the channel estimation into a problem of sparse signal recovery, which is solved using the gradient projection method. Ranging accuracy and communication performance of the proposed waveform is analyzed and discussed under various conditions, includes the signal noise ratio of the surrounding environment, the transmission device’s speed, the separation between the transmitter and receiver, and the communication rate. Our simulation findings show that the integrated communication and navigation technique is reliable, particularly in high-speed motion situations.
A Two-Stage Lesion Recognition System for Diagnostic Support in Colon NBI Endoscopy

In this paper, we propose a two-stage Computer-Aided Diagnosis (CAD) system for lesion recognition using detecting and classifying method based on deep learning architecture. The proposed CAD system can presents quantitative inference results from images token by colorectal Narrow Band Imaging (NBI) endoscopy to clinical doctors, which aims to reduce the variation and burden of diagnoses due to the experience of diagnosing doctors. As a result, for our test dataset, the current accuracy has reached 67% for magnified images.
Target Detection Method Based on Unsupervised Domain Adaptation for Through-the-Wall Radar Imaging

Due to the ability of automatic learning and extraction, deep learning is widely used in robust target detection for through-the-wall radar(TWR). However, most of the existing target detection algorithms rely on large-scale labeled datasets to ensure the accuracy of detection which is difficult to obtain in practice. Therefore, this paper proposes a target detection method based on unsupervised domain adaptation for TWR imaging. Firstly, unsupervised domain adaptive network is adopted in the first stage to transfer the labeled simulated images to the TWR-like images and produce large-scale labeled datasets. The adaptive residual block and multi-scale discriminator are combined to improve the image quality. Then, the transformer multi-head attention mechanism is introduced into the target detection network to improve the detection probability of weakly scattering targets by making full use of context information. The experimental results show that the proposed method can realize the detection of weak scattering targets and adjacent overlapping target images without a large number of labeled datasets.
Research on Testing Technology of Complex Permittivity Based on Ring Resonator

A microstrip ring resonator is designed to measure the complex permittivity of the glass substrate. The sample is prepared by the traditional thin film process, and the insertion loss cure of the ring resonator is tested. The simulation model of the ring resonator is created. The simulation curve of the insertion loss is compared with the test curve. Then the effects of complex permittivity on the frequency offset and amplitude fluctuation of resonant peak are analyzed. The relation curves between dielectric constant and resonant frequency, loss tangent and quality factor (Q) of the glass substrate are obtained, and the corresponding empirical formulae are extracted, which can be used for rapid calculation of the complex permittivity of a substrate.
Improved MAPPO-based Collaborative Computing Offloading Algorithm in Broadband Mobile Internet

Collaborative computation offloading technology can offload computational tasks from mobile devices to mobile edge servers and idle devices, thus breakthrough the performance bottleneck of MEC computation offloading systems and providing a new idea to meet the needs of computation-intensive mobile applications. In collaborative computation offloading systems, variations in the number of mobile devices cause changes of their state space and action space, and traditional deep reinforcement learning-based computation offloading decision algorithms (e.g., MADDPG) are usually unavailable at this case. A computational offloading algorithm based on improved MAPPO is proposed to transform variable dimensional input information into fixed dimensional aggregated information through the attention mechanism, ensuring that the input dimensions of the policy network and value network do not change with the number of smart devices, which meet the demand for flexibility and scalability of computational offloading decision algorithms in collaborative computational offloading systems.
Service Function Chain Scheduling Method for Power Wide Coverage 5G Multi-link Converged Networks

Currently, the extension of the end of 5G power communication network by deploying multi-link multimedia access terminals integrating multiple communication technologies has become an important development trend in power communication technology. This paper incorporates the characteristics of power communication networks and designs a converged network architecture based on 5G power converged access network and 5G-MEC, aiming to support the collaboration and flexible extension of edge network resources. Based on the consideration of constraints such as latency, computational resources, storage resources, and bandwidth resources, this paper establishes a joint optimization goal. Finally, based on the designed VNF deployment prioritization strategy that distinguishes service latency sensitivity, a VNF deployment decision algorithm based on the A3C algorithm is proposed. Simulation results show that the proposed algorithm can meet the demand for low-latency power services while avoiding network congestion, and more broadly support the “low latency, high reliability, and large bandwidth” demand for multimedia transmission, services, and network services in power networks.
Shooting for the Stars: How a STEM Initiative has Evolved to Address Gender Challenges in Work and Education

There are fewer women than men in Science, Technology, Engineering and Mathematics (STEM). To address this imbalance, numerous STEM intervention programmes have been implemented worldwide. These programmes are aimed at helping girls and women to reach their full potential in terms of school and work.Planning and implementing a STEM intervention programme is a complex process. There are many different stakeholders, and their needs can be varied and evolving. This paper discusses STEM interventions, and it presents our experience with a specific intervention, the University of Limerick-Lero/Johnson & Johnson WiSTEM2D (Women in STEM, Manufacturing and Design) programme. This programme has evolved based on the learnings identified in areas such as participant group dynamics, invited speaker engagement and presentation delivery, and teamwork-centered activities. Such learnings can be applied across a range of STEM fields, including software engineering. We believe that initiatives, such as WiSTEM2D, which support and promote gender equality in STEM2D fields, and specifically Computer Science (CS) education and industry, are of key importance. Our review of the programme has identified three primary lessons: (1) individuals did not benefit as strongly as those working in groups (2) online events allow for more speakers and greater transfer of knowledge and (3) working in teams provides the students with the opportunity to socialise with other women in a work environment.
A Generalized Time-Domain Signal Generation Algorithm for FSOC Turbulence Channel Simulation

To evaluate various methods and techniques proposed for mitigating the effects of atmospheric turbulence on optical communication performance, it is critical to generate optical channel states. Meanwhile, due to the complexity of the communication environment, traditional channel simulation methods are not always applicable. Based on the Gamma-Gamma turbulent channel, we propose a channel simulation method with an autoregressive model and rank matching method, which can simulate any channel condition described by autocorrelation function and probability density distribution. The method is simple to implement and its results are comparable to some more sophisticated methods. Although this paper discusses one-dimensional random signals, the method can also be extended to multiple dimensions, generating multiple random signals with mutual correlation numbers.
Super Class AB Buffer Amplifier with ICMR and Gain Enhancement for LED Backlight Driver

This paper presents a single stage class AB operational transconductance amplifier (OTA) with input common mode range (ICMR) and gain enhancement as the buffer amplifier for 1:4 time-multiplex liquid crystal display (LCD) panel light-emitting diode (LED) backlight application. The proposed buffer amplifier increases the ICMR to meets the input voltage requirements of the common anode LED driver and the DC gain to improve the accuracy of the current mirror. The design is implemented using the CSMC 0.18um 5V BCDMOS process. The post-layout simulation results show that the proposed buffer amplifier has an ICMR of 0V-4.2V, DC gain of 71.3dB, can drive a 500pF capacitive load with the positive and negative slew rates of 2.56V/us and -6.34V/us, respectively.
Status and Outlook of Image-based Malware Detection Technology

In recent years, computer vision technology has achieved rapid development and is widely used in the field of information security. Researchers have carried out a lot of research on the malicious code analysis technology based on image analysis, and many excellent image analysis models have successfully migrated to the malware detection area. This paper systematically collates the malware detection technology based on image analysis technology from different angles. Firstly, the development of computer vision technology in the field of malware, the main representation methods of feature images, malicious code detection models, and other issues are sorted out. After that, the relevant representative literature is collected and sorted out. Finally, the potential research direction of computer vision in the realm of malware detection is deliberated.
Assessing User and Manufacturer Perceptions of Fitness Trackers through Amazon Review Analysis

Fitness trackers encourage people to be more active, people with obesity track their diet, and older adults to understand their health by knowing their heart rate. Companies display advertisements for these types of products and describe them as beneficial. However, users are looking for the products that best suit their personal needs, for which they often review the opinions of other users on e-commerce platforms such as Amazon. In this research, we study the opinion of users who have used physical activity trackers, to assess whether their satisfaction meets the quality offered by the manufacturer. Through: the use of natural language processing techniques, and the analysis of information provided in Amazon reviews. A sentiment analysis was carried out based on the technical characteristics of the device. We employ transfer learning of a Transformer-based language model (RoBERTa: Robustly optimized BERT Pretraining Approach). Which was retrained for two classification problems in independent modules, the first module classified 20 technical aspects of the device (93% precision), and the second module classified user sentiments (70% precision). A comparison was made between the average feeling of the user vs. the manufacturer, getting a 3.11 for users and 3.99 for manufacturers with a difference of 0.88, concluding that the user’s sentiment having a lower expectation than the manufacturers.
Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway system. In such cases, train timetables need to be rescheduled. However, timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization efficiency. This paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based decision-making. Firstly, the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer, providing the better understanding of overall operation in the high-speed railway system. Then, a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast convergence. Extensive experiments on various delay scenarios are conducted. The results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
Prediction of COVID-19 Victim's Well-being using eXtreme Gradient Boost Algorithm

In many areas, Machine Learning (ML) and data-driven approaches are becoming very important and popular. Gradient tree boosting is one technique of the machine learning approaches included in practice that appears in many application domains. Tree boosting has appeared to execute numerous normal measurements for classified benchmarks. A highly efficient and commonly used machine learning approach is tree boosting. This study proposed a Calibrated Random Forest Model (CRFM) improved by the eXtreme Gradient (XG) Boost algorithm. The model takes into account the location, method of transportation, medical history, and demographics of the Corona Virus Disease-2019 (COVID-19) patient to predict the severity of the case and the likelihood of a positive or negative outcome. The system has a 0.92 F1 score and 95% accuracy on the repository used. The descriptive statistics indicate a strong link between sexual orientation and demise and also show that most victims are between the ages of 40 and 60 in India.
Survey on Security Based Approach to Prevent Attacks in SDN Using IoT Framework

Network evolution trendincludes Internet of Things (IoT) and Software-Defined Networking (SDN). By separating the data plane from the control plane, the software defined networking architectural framework is used and it will makes the network admins works more easily. SDN mainly focuses on network management and controls at the higher levels to monitoring and sharing the connection between the devices with the help of network connectivity in real time using the Internet of Things concepts. Almost in all the fields, IoT devices used so have to improve the security of IoT devices. The SDN has the centralized control logic, if the number of network traffic increases then centralized controller may not be handle all the network traffic requests at the same time, because the SDN controller is a single point of failure, its failure can bring the entire network down. The Software Defined Network (SDN) is easily exposed to various attacks mostly to DDoS attacks. Majority of the existing research is concentrated only on sensing of HR-DDoS attacks occurring over the control layer and not focusing on LR-DDoS attack occurring over the data layer because the accuracy of attacks on the LR-DDoS attacks are very low. This study mainly focusing on how to design the multi controller SDN architecture to improve the scalability, how to increase the detection accuracy of low-rate DDoS attacks on the SDN data layer and how to detect the attacks using machine learning algorithms.
Quality Control Using U-Net: Detecting Defects in Leather

Recently, there has been a significant amount of attention towards computer vision algorithms, particularly those that focus on semantic segmentation applications. This is due to the availability of big data to train models, as well as the computational ability of these algorithms. Among the various computer vision applications, the U-Net has gained popularity due to its reliable accuracy, simplicity in construction, and ease of application.Despite the advantages of this network structure, there are still some unclear aspects within the U-Net that have not been significantly covered in literature — to the best of our knowledge —. This study seeks to clarify and explain these ambiguous points and construct different architectures to demonstrate their pros and cons. Finally, a series of experiments were carried out on natural leather samples from MVTec AD to confirm our findings. The outcomes highlight our discoveries and provide a framework for determining the fine-tuning parameters of U-Net.
Understanding Student Needs around Academic Enrollment: Insights from a Co-design Study

We present the insights of a co-design process of an enrollment support tool for undergraduate students. Our goal was to design an effective tool that would streamline the enrollment process and address common challenges faced by students. Undergraduate students were involved in a 4-month co-design process, providing different types of feedback on the tool’s design and functionality at different stages of the design process. Our observations indicate that the co-design process led to a tool that would better meet the needs and preferences of undergraduate students, resulting in a more efficient and user-friendly enrollment process. This study highlights the benefits of involving users in the design process and provides insight into how co-creation can be used to improve the effectiveness of technologies for educational settings.
Australia's mangroves : the authoritative guide to Australia's mangrove plants

Although often under-valued, about 20% of the Australian coastline is occupied by a very special and beneficial habitat of extraordinary trees and shrubs bathed regularly by flooding tides and washing waves. This practical guide describes, for the first time, each of the highly adapted plants that make up this habitat around Australia, and provides you with the means to easily identify them. The book includes: 1) descriptions of all 41 Australian mangroves; 2) illustrated keys for easy identification; 3) more than 1000 photographs and icons covering each species; 4) featured artworks by artist, Fran Davies; 5) sections for each State/Territory with contributions from local specialists; 6) a ‘know-your-coast’ manual for community awareness and education; 7) a water-proof key, called the 'Wheel', for use in the field. This is a book for research, teaching and the eco-minded.
A 0.75V 30.1 ppm/°C 300nW Bandgap Reference and Low Power Low-dropout Regulator with Hybrid form of Resistors as Size Reduction Technique

This paper proposes a low-power bandgap reference(BGR) and low-dropout regulator(LDO) circuit that apply size reduction technique. Using stacked-MOS transistors in triode region as resistors occupies less layout area than poly resistors with the same resistance. Although the resistance of MOS transistors has wider PVT variations than that of poly resistors, The hybrid form of both MOS resistors and poly resistors can have acceptable PVT variation of resistance with noticeable size reduction. The proposed BGR and LDO with the size reduction technique have the layout size of 0.034mm2 total, which could be 0.050mm2 with poly resistors only. The layout size saving effect is 0.016mm2, which is 31.36% of the total. The proposed BGR produces 0.75V reference voltage with temperature coefficient of 30.1[ppm/°C] over temperature range of −40°C∼125°C. The power consumption is 300nW with layout size of 0.025mm2. The proposed LDO has 0.9V output voltage with input voltage of 2∼3.6V. The operating current is 260nA. The layout size of the LDO is 0.009mm2.
Analysis of interference from UAV system to adjacent radio location services

The applications of Unmanned Aerial Vehicle (UAV) have become increasingly diverse and widespread, a variety of application scenarios have emerged, which are mainly divided into UAV-mounted base station scenarios and UAV-mounted terminal scenarios. When the UAV system flies in high altitude, the signal interaction with the ground may interfere with the typical land-based radar system in the neighboring frequency radio positioning service. Therefore, in this study, the simulation of coexistence is conducted and the nearest protection distance required for the coexistence of the above two systems is derived for the above interference scenarios, taking into account the deployment parameters of the UAV system. The conclusion of the analysis shows that it is easier to achieve inter-system coexistence when the interval between the operating bands of the two systems is larger. Compared with the UAV-carrying base station scenario, it is easier to protect the land-based radar system when the UAV carries a terminal.
Local e-gov mobile application(s) review

This study aims to evaluate the adoption of e-government services in Portuguese municipalities, specifically focusing on the usage of mobile applications. Data was collected from 40 of the most populous municipalities in Portugal and analyzed for usage of e-government services such as incidents, traffic conditions, and population alerts through mobile apps. The study found a mixed correlation between population size and e-government app usage, with lower population municipalities showing lower adoption rates. The results suggest that further efforts are needed to improve accessibility and promote e-government services to increase engagement and usage among the local population. The study provides valuable insights for local government representatives and stakeholders to effectively use and access e-government services for the benefit of all residents. The results emphasize the need for effective outreach and promotion strategies, as well as the importance of user testing and feedback in the development process, to ensure that the applications meet the needs and expectations of users. Furthermore, the study highlights the need for investment in marketing and communication initiatives to increase awareness and adoption of e-government apps among the population. In conclusion, this study contributes to the literature on e-government adoption in Portuguese municipalities and provides a foundation for future research in this area. The findings can inform the development of effective policies and strategies to promote the adoption and usage of e-government services, enhance accessibility and efficiency of local government services, and improve the quality of life for residents.
A Novel Direction Vector-based Coordinate Deduplication Algorithm in the Visual Sorting System with Cloud Programmable Logic Controller

In industrial internet of things (IIoT), automation and intellectualization have increasingly been the basic factor of the intelligent manufacture. Visual sorting system has attracted more attention with cloud programmable logic controller (CPLC) in many industrial applications, which is characterized by high processing speed, precise positioning, and flexible deployment improving the intelligent developments. The critical procedure of the visual sorting system with CPLC is reporting the coordinates of the materials from the visual recognition module to CPLC. However, there is always existing the imbalance between the reporting speed of the coordinates and the sorting speed of the actuator in the visual sorting system, which causes CPLC receiving the duplication information of the coordinates that further leads to ineffective actions and lower accuracy. In this article, we propose a novel direction vector-based coordinate deduplication algorithm, which establishes direction vectors to recognize the coordinate duplication information. Moreover, we build a real crane visual sorting system to demonstrate the effectiveness of the proposed algorithm.