Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Object Detection in Autonomous Driving

Vehicular technology has been rising rapidly in recent years, to obtain a safe and reliable transportation experience, autonomous vehicles gain more and more advanced equipment to be prepared for the future, which made the foundation of the widespread introduction of Autonomous Vehicles. It leads to many ways to use accurate positioning systems and intelligent environment perception technology, and comprehensive algorithms to deal with various driving situations. Sometimes autonomous vehicles need to be able to tackle tricky choices, the complex expectation of pedestrian behaviors, path organizing, and road sign recognition. For these requirements, Avs need specific targeting data set to train and validate before they can do so. This paper attempts to convey ideas of recent Artificial Intelligence-based techniques in significant functions of Autonomous Vehicles. Finally, this survey outlines the challenges and introduces possible future research directions.
RC-Mask: Rotated-Count-Mask for Chromosome Segmentation

Chromosome karyotyping plays a vital role in birth defect diagnosis and biomedical research, in which segmentation is an indispensable task. Current state-of-the-art segmentation methods suffer from bottlenecks due to the small size and polymorphism of the chromosomes, and the overlap between chromosomes is a severe obstacle as well. This work proposes an algorithm for preprocessing datasets and a segmentation network termed RC-Mask for chromosome segmentation. The RC-Mask extends current instance segmentation models by adding a branch for predicting count in parallel with the existing branches, moreover, we add orientation information to both the object detection and semantic segmentation branches. In summary, RC-Mask achieves state-of-the-art segmentation accuracy on the original chromosome dataset(63.2%mAP), which outperforms all existing methods in chromosome automatic segmentation. Code is available at GitHub where repository name is RC-Mask.
Go To The Right: A Real-Time and Accurate Polyp Segmentation Model for Practical Use

Although deep learning-based approaches have achieved a fairly good performance in polyp segmentation, most of them are unable to be deployed in real-world medical scenarios due to the excessive computational effort and the large model size. To deal with this issue, we propose a real-time and accurate polyp segmentation model for practical use. Specifically, we construct it from two respects. First, we try to effectively extract global contextual features to improve segmentation performance. Thus, we leverage large kernel attention to effectively capture global contextual dependencies while maintaining the low computational effort. Second, we improve the learning capability of the decoder and introduce feature transitions to enhance the performance of cross-level feature fusion. Extensive experiments on five polyp segmentation benchmarks demonstrate that our proposed model achieves state-of-the-art performance with less than 5M parameters and runs at 82.66 FPS on a GeForce 1080Ti GPU. Code will be released1.
A Broadband Magneto-Electric Dipole Transmitarray Antenna with Improved Aperture Efficiency

In this paper, a broadband magneto-electric (ME) dipole transmitarray antenna (TA) with a high efficiency is proposed, whose transmission phase is manipulated by simply varying the length of the delay lines. Operating at millimeter-wave (mm-Wave) frequency band, it is demonstrated that the proposed TA element has a wide 1-dB bandwidth of about 44.4%. Several TA designs with different phase compensation schemes are simulated and compared. The results show that the aperture efficiency of a 16 × 16-element TA adopting the continuous phase adjustment reaches 56.8% with a 3-dB gain bandwidth of 33.9%, covering from 25.5 to 36 GHz. Compared to the 1-bit TA, the aperture efficiency of the proposed TA antenna is significantly improved. Meanwhile, to solve for the bandwidth degradation in large-sized TAs and moderate-sized TAs with small F/D ratio, the true-time delay strategy is adopted. Specifically, a true-time delay moderate-sized TA is designed, simulated and tested. It exhibits a wide operating bandwidth of 32.3%. The demonstrated TA can be a promising candidate for a variety of mm-Wave applications.
Nanothorn Photoanodes: A New Approach for Efficiency Enhancement in Dye-Sensitized Solar Cells

Dye-sensitized solar cells (DSSCs) are a promising alternative to conventional silicon-based solar cells due to their low cost, lightweight, and flexible nature. A proficient photoanode is vital layer for obtaining high efficiency in DSSCs. Achieving this proficiency can be accomplished by selecting suitable materials and optimizing the device architecture. In this works, a preliminary result of novel nanothorn photoanode layer was obtained for the first time by a combination layer of rutile TiO2 nanoflower (rT-NF) synthesized by hydrothermal method and coated with Cu2o (rTCu2O-NF) by RF Magnetron sputtering method with 30 mins and 60 mins time deposition at high RF power source. The field emission scanning electron micrograph, X-ray diffraction, UV-Vis spectroscopy and current density to voltage characteristics, were used to investigate the morphology, structural, optical and electrical properties of rT-NF and rTCu2O-NF layer. The evaluation revealed that the optical bandgap energy decreased from 3.0 eV to 2.63 eV with increasing deposition time of Cu2o. The study observed a distinctive nanothorn morphology in the rTCu2O-NF photoanode layer after a prolonged sputtering process at high RF power for 60 minutes, which resulted in the highest power conversion efficiency of 8.34% among the tested samples. This finding suggests that modifying the nanoflower structure into a nanothorn structure by extending the sputtering time at high RF power during the photoanode deposition process can considerably enhance the efficiency of dye-sensitized solar cells.
Ensemble Multiple Instance Learning for Bladder Cancer Diagnosis

This paper presents a method for automated diagnosis of bladder cancer from digital cytology slides. The proposed method relies on a-priori selection of the most atypical cells and an ensembling of Multiple Instance Learners to predict diagnosis. Our model is trained on a large clinical trial dataset to predict the outcome of cystoscopy and histology examinations directly from voided urine cytology slides. To the best of our knowledge, it is the first time such approach is published. The considered task is known difficult: Yafi et al. [1] evaluated the sensitivity and specificity of experts analysing voided urine cytology respectively at 30% and 87%. The proposed method achieves a sensitivity of 76% and a specificity of 79%, showing that computer-aided analysis of digital cytology slides is a promising approach for bladder cancer diagnosis able to improve patient care as, unlike cystocopy/histology examinations, voided urine cytology is non-invasive and inexpensive.
A Transformer-based Object Relationship Finder for Object Status Analysis

Basketball analysis systems are essential tools in modern basketball, where identifying the ball handler is one of the most critical tasks. The reason for this challenge comes from the overlapping of players in basketball, which makes it easy for the analysis system to misjudge the ball handler. We found that it is easy to misjudge ball handler using traditional algorithms, such as calculating the degree of intersection over the union or calculating the coordinate distance between the player and the ball. In this paper, we propose a transformer-based object relationship finder to classify the relationship between players and the ball, which uses features of different objects, such as the use of coordinate information and skeleton information as inputs, to learn the relationship between players and the ball through self-attention. Experimental results show that our method achieves an accuracy of ball handler up to 91.2% based on a smaller dataset, surpassing the 83.9% accuracy of traditional algorithms and the 77.8% accuracy of Resnet-based convolutional neural networks.
Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network

Retinal image quality assessment (RIQA) is essential for retinal examinations, as it impacts the certainty of both manual and intelligent diagnosis. Unfortunately, domain shifts, such as the variance of colors and illumination, are prone to confuse RIQA. Though efficient domain adaptation solutions have been proposed, properly transferring RIQA models to new domains remains a troublesome task. This paper presents a domain adaptative RIQA algorithm with knowledge distillation using a competitive teacher-student network (CTSN) to address the above issue. The main structure consists of a teacher network, a student network, and a competition module. The teacher network provides pseudo-labels by adapting source and target domain features, and the student network learns features from target-specific pseudo-labels. The competition module boosts the fine-grained adaptation of RIQA. Comparison experiments and ablation studies demonstrate that our method performs outstandingly in RIQA with domain shifts.
Framework and Quantization Method Design for CIM

This paper proposes a framework for simulating RRAM-based CIM. The framework is designed to explore changes in accuracy under the constraints of ADC precision, resistance on/off ratio, and activated WL number. We also propose an accuracy-aware quantization methodology to reduce the number of quantization bits required. Experiment results show that the quantized IFMs only reduce the accuracy by 0.09%, while the accuracy of quantized weights dropped by 10.55%. In the future, we will add R on/off ratio and current error to further study the relationship between number of activated WL and CIM accuracy.
A Cloud-Edge Collaborative Framework for Cross-environment Human Action Recognition based on Wi-Fi

Device-free human action recognition (HAR) based on Wi-Fi signals is an essential support in the field of the Internet of Things and shows bright application prospects. With the rapid development of deep learning (DL), HAR based on DL models has become mainstream and achieved good performance. However, most of these methods are still far from the practical application, the main challenges include poor cross-environment recognition performance and the high requirements for sensing devices of DL models. Based on this, we propose a cloud-edge collaborative HAR framework (Co-WiSensing), which explores the possibility of cross-environment HAR with low resource consumption. Considering the characteristic of the massive resources of cloud servers and the resource constraints of edge devices, a high-performance multi-branch cloud HAR model is delicately designed and the personalized model compression and offloading strategies are proposed to construct lightweight edge HAR models for different environments, this allows the edge users to realize perception under resource-limitation conditions. Extensive experiments are conducted to validate the effectiveness of the proposed framework. Experimental results show that our framework can provide better HAR accuracy across all environments while using less computation and storage cost than the state-of-the-art lightweight models.
A GNSS Positioning Algorithm Assisted by LSTM Neural Network and EKF

In Global Navigation Satellite System (GNSS) positioning, the Kalman filter algorithm can effectively utilize the dynamic model characteristics and observation data of the mobile carrier to estimate the dynamic characteristics of the mobile carrier. However, due to the constantly changing motion state of the carrier, there is a mismatch between the preset dynamic model and the actual motion model, resulting in some deviation between the model and the real motion state. To address this issue, this paper proposes a GNSS positioning algorithm based on a Long Short-Term Memory (LSTM) neural network-assisted Extended Kalman Filter (EKF). The LSTM neural network tracks and learns the change in dynamic model error in the Kalman filter. The predicted error is used to correct the Kalman filter in order to improve its performance and achieve higher positioning accuracy. Simulation results show that compared with the algorithm using only the Kalman filter for positioning, the proposed algorithm improves the accuracy of the x, y and z directions by 48%, 13% and 34% respectively. In addition, the positioning results are more stable, having effectively suppressed the error divergence caused by the Kalman filter and significantly improved the positioning accuracy.
DNS++: Dynamic Name Resolution with Homomorphic Encryption Based Privacy

This paper presents DNS++, a re-design of the Internet's name resolution system that addresses dynamic information and privacy. DNS++ uses a pub/sub overlay to send updates about a given service to interested clients, allowing them to (re)select between replicas according to their requirements, as updates about services and their features dynamically change. Since third-party brokers in the overlay are not always trusted for the confidentiality of the content flowing through them, clients' privacy is preserved in DNS++ through homomorphic encryption. Brokers are prevented from accessing encrypted service information but can perform homomorphic match and forward service updates to relevant clients through the overlay accordingly. Assuming that forwarding tables in each broker are implemented via ordered data structures, the time required for adding a new client's subscription, and to perform homomorphic match between existing subscriptions and service updates, would grow logarithmically with the number of entries within a table. This is shown by our performance evaluation, which confirms that DNS++ is feasible to be deployed with an acceptable performance overhead.
Application of traffic scheduling mechanism based on time sensitive network in Intelligent Manufacturing Network*

In order to solve the contradiction between data traffic growth and real-time control information in intelligent manufacturing fusion network, a traffic scheduling mechanism based on time sensitive network is proposed. This technology adopts the idea of exclusive and shared time slot window in transmission time, adopts the mechanism of frame preemption in transmission channel, classifies according to traffic characteristics at the front end, and optimizes the configuration of parameters through algorithm at the back end. This technology can improve the traffic scheduling mechanism of MAC layer on traditional switches in intelligent manufacturing fusion network, improve the effect of deterministic delay scheduling on time sensitive traffic, and provide transmission services with low delay jitter for workshop industrial network in intelligent manufacturing fusion network.
Role of Carrier Gas and its Flow Rate to Produce Uniform, Large-Sized MoS2 Monolayer via CVD

Recently unprecedented interest has been immersed towards the synthesis of two-dimensional (2D) transition metal dichalcogenides via chemical vapor deposition (CVD) system. Synthesis of uniform and large-sized monolayer $\text{MoS}_{2}$ atomic thin film via CVD is still a major bottleneck owing to strong dependence on diverse associated growth parameters. In this work, we have investigated the effect of individual carrier gas during $\text{MoS}_{2}$ growth and its flow rate. Selection of Ar carrier gas with flow rate 150 sccm is the optimum amount to obtain the largest triangular $\text{MoS}_{2}$. While $\mathrm{N}_{2}$ as a carrier gas with 150 sccm is help to deposit dendrite $\text{MoS}_{2}$ flakes. In addition, mixture of Ar and $\mathrm{N}_{2}$ gases in 2:1 ratio during $\text{MoS}_{2}$ growth is suitable to deposit large and uniform monolayer of $\text{MoS}_{2}$.
Two-Stage Malware Detection Using Calling API-based BiLSTM

In this paper, we propose a two-stage malware detection using calling API (Application Programming Interface)-based BiLSTM (Bidirectional Long Short-Term Memory). In the first stage, to identify the overall characteristics, we use all the calling APIs of the application as input to determine whether the application is malware. By contrast, in the second stage, we divide the calling APIs into small windows and analyze each window separately. Through simulation experiments, we demonstrated the effectiveness of the proposed method using two-stage detection.
Non-invasive SpO2 Monitoring Using Reflective PPG: A Low-Cost Calibration Method

The increasing availability of wearable devices has resulted in a rise in the use of non-invasive physiological monitoring applications. In this paper, we develop a reflective-type photoplethysmography system. A low-cost calibration method is established by implementing noise reduction and signal quality control. The proposed method eliminates the need for subject inhalation of low concentration oxygen during calibration, making it more convenient and cost-effective.
MLN-Visualizer: Interactive Visualization of Graphs and Multilayer Network Analysis Results

Big data analysis is broader with a life cycle from choosing a model to represent data, generation of the model for the given data set, analysis using operations relevant to the model, and finally, visualization to understand the results. The ability to perform this end-to-end workflow has become more important as the data size and complexity have increased significantly. Visualization is especially important when the data sizes are large and models are complex (e.g., graphs, multilayer networks (MLNs)) which is difficult to comprehend without visualization.In this poster paper, we describe a web-based tool MLN-Visualizer for visualizing graphs and MLN analysis results. Interactively, the user can visualize their uploaded input data and analysis results, in multiple ways, using a menu-driven interface. We hint at adding privacy to the resulting visualization.
Estimating Plant Nitrogen Accumulation in Winter Wheat Based on Nitrogen Partitioning Model and Hyperspectral Data

Real-time and non-destructive monitoring of nitrogen accumulation is important for accurate nitrogen management and high quality and efficient production of wheat. The problem is most of the existing remote sensing monitoring models for wheat nitrogen accumulation are based on a single growth period and cannot be extended throughout the whole growth period. Therefore, this study comprehensively used hyperspectral data and relative growth stage (RGS) to systematically analyze the dynamic changes of nitrogen allocation between leaf and plant with phenological progress, to construct a nitrogen allocation model, and finally to achieve the prediction of plant nitrogen accumulation (PNA) in the whole growth period. The results showed that (1) the ratio of leaf nitrogen accumulation (LNA) to PNA and RGS showed a significant linear relationship, with a coefficient of determination ($\mathrm{R}^{2}$) of 0.90. (2) The green band normalized difference vegetation index (GNDVI) was highly correlated with LNA, and the linear model based on GNDVI inversion of LNA had high accuracy with $\mathrm{R}^{2}$ and root mean square error (RMSE) of 0.70 and 1.31 g/$\mathrm{m}^{2}$, respectively. (3) The coupled nitrogen partitioning model and LNA inversion model were used to invert the PNA, and the $\mathrm{R}^{2}$ and RMSE of the validation data were 0.72 and 2.98 g/$\mathrm{m}^{2}$, respectively, with a high agreement between the predicted and measured PNA. Overall, this study can accurately estimate the nitrogen accumulation in winter wheat plants at different growth periods and provide a basis for accurate diagnosis and quantitative fertilization of wheat growth.
Hybrid Vision Transformer for Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy Videos

The early detection of pancreatic cystic lesions plays a significant role in the survival chance of a patient with pancreatic cancer. Yet it is still a huge challenge. Unfortunately, most pancreatic cancers were diagnosed when the tumor was metastatic. In this study, the Hybrid Transformer, which is the combination of the VGG19 network and Vision Transformer, is utilized as a learning model to predict the pancreatic cystic symptom types in needle-based confocal laser endomicroscopy. A total of 16,944 images containing five types of pancreatic cystic are collected as the training and validation data. Our method can automatically classify the feature type of pancreatic cystic in the test videos and record the prediction results frame by frame. In our experiment, the proposed method successfully identifies the symptom types of 13 from 18 test videos and achieves an accuracy as high as 72%.
Chapter 3 The Role of Communication Technologies in Vehicular Applications

Nowadays, communications become essential in the information society. Everyone can get information anywhere, even in mobility environments, using different kinds of devices and communication technologies. In this frame the vehicle is another place where users stay for long periods. Thus, in addition to safety applications, considered as the most important services, other networked applications could bring an additional value for the abStract
60-GHz 5G-NR Optical Fronthauls Based on CS-DSB Technique

This paper reports the implementation of 5G New Radio (5G-NR) optical fronthauls based on radio-over-fiber (RoF) technology and optical carrier-suppressed double sideband (CSDSB) technique, operating at 60 GHz. Experimental results over 20-km fiber-optics links demonstrate the successful transmission of QPSK, 16-QAM and 64-QAM modulated signals with up to 400-MHz bandwidth. The proposed 5G-NR RoF system performance meets the 3rd Generation Partnership Project (3GPP) requirements, achieving a total throughput of 800 Mbps.
Managing Erbium Emissions through ZnO Host Crystallinity

We report on the correlations between the optical emission of the Erbium and the structural conditions of ZnO host thin film. The influence of the local chemical environment on the luminescence properties of the trivalent RE ions is often disregarded, and the management of host crystallinity in Spray-Pyrolysis synthesis is provided by the precursor dilution, i.e., solution molarity (M). Here we present an analysis of Erbium photoluminescence depending on the host crystallinity, which implies different Stark fields. The use of lower molarity results in transparent films well texturized in c-axis while the use of high molarity provides an opaque powder-like film.
Traffic-Aware In-Network Aggregation Placement for Multi-Tenant Distributed Machine Learning

Distributed machine learning is an effective method to alleviate intensive computation costs of training; however it suffers from network bottlenecks while gathering local results. Recent advent of programmable data planes opened a new avenue, in-network aggregation, which executes gradient aggregations in the middle of the network resolving network bottlenecks and further accelerates distributed machine learning. However, due to resource-constrained features of current programmable data planes, installation of in-network aggregation functionalities throughout the network would impose unacceptable burden, posing a need for sophisticated deployment. In this paper, we consider a problem of deploying in-network aggregation functionalities, so as to minimize the total network traffic in multi-tenant distributed machine learning. Since the formulated problem is an integer linear programming problem, which is known as NP-hard, we propose a traffic aware placement of in-network aggregation (TAPINA) algorithm with lower complexity and near-optimal performance. TAPINA decides aggregation points of multiple tenants sequentially in order of their expected traffics and reuses the already selected aggregation points by other tenants to reduce the overall deployment cost. Simulation results demonstrate that TAPINA shows near-optimal performance, achieving up to 20 % traffic reduction compared to the state-of-the-art algorithm in most cases.