Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the methods used for the detection of rust in coffee crops in different parts of the world

Currently, coffee is one of the most cultivated products in the Americas. Brazil is the country that commercializes and produces the largest percentage of coffee in the world. The coffee plant is characterized for being cultivated in tropical and subtropical zones to ensure optimal development from the planting stage to the first flowering. Rust is the most serious disease that weakens and directly attacks the adult leaves of the coffee plant. This article describes the development and analysis of the techniques implemented to detect rust in coffee crops. The information and results were obtained from the latest research found in the IEEE database and the Google Scholar search engine.
Low-Latency and Scalable Full-path Indexing Metadata Service for Distributed File Systems

Distributed file systems (DFS) are the cornerstone of modern mass data processing systems. In DFS, the metadata service, as the core component, often becomes a performance bottleneck. Existing metadata service solutions have implemented flattened metadata management and full-path indexing to achieve high scalability in terms of capacity and throughput. However, these approaches have limitations, such as conflicts with POSIX-style permission verification and flawed support for super directories, leading to high and unstable latency that cannot provide reliable service for latency-sensitive applications. To overcome these limitations, we propose Duplex, a scalable DFS metadata service based on full-path indexing, which aims for low and stable latency. Duplex incorporates three key designs: a fast access path featuring a centralized permission server for efficient permission verification, a permission merging algorithm to reduce the PMS's space footprint, and flattened metadata management based on double consistent hashing that enables low-latency access to super directories. Our evaluations demonstrate that, compared to state-of-the-art metadata solutions, Duplex significantly reduces the average lookup latency by up to 84% and the 99th percentile tail latency by up to 88.2% for metadataintensive benchmarks. Additionally, Duplex improves the lookup IOPS by up to 7.6 × /2.3× compared to CephFS and BeeGFS.
Research on Dance Movement Recognition and Assessment through Human Pose Estimation

As an artful movement, dance demands performers to execute precise actions and maintain graceful postures. However, evaluating a dancer's movements can be highly subjective and cumbersome. To address this issue, our study proposed an intelligent method for dance movement recognition and assessment based on human posture estimation. Regarding movement recognition, we refined the original COCO skeleton model, followed by a lightweight improvement to the OpenPose model. Compared to the original model, the parameter quantity decreased by 88.9%. We employed the improved OpenPose algorithm to extract key points of the human body from videos and used Singular Spectrum Analysis (SSA) to denoise the data. For the assessment aspect, we utilized the Dynamic Time Warping (DTW) algorithm to measure the similarity between actual dance movements and standard dance movements in terms of joint angles, thereby assessing the dance movements. This method can provide dancers with real-time feedback and guidance, objectively evaluating their dance movements. The proposed intelligent method can be used in controlling humanoid robotics or rehabilitation robotics due to the accurate human pose estimation and movement assessment results.
Optimized UAV Navigation Overcoming LoS Obstructions for Maximized Power Grid Tower Inspections in Mountainous Terrains*

Performing integrity inspections of power grid towers in mountainous and uneven terrain can be a challenging task and prove to be inefficient and dangerous when done manually. Deploying unmanned aerial vehicles (UAVs) can provide a practical alternative that offers greater efficiency and safety for these grid facility inspections. We present a novel UAV navigation approach, underpinned by the Randomized Rapidly-exploring Random Tree (RRT) algorithm. Our proposed technique formulates a composite optimization problem and manages to maximize the number of power grid inspections maintaining the same Quality of Service (QoS) while concurrently ensuring Line-of-Sight (LoS) connection and enabling obstacle circumvention. This method is corroborated through a series of simulated experiments which apply our algorithm to UAV path planning and facility inspection operations within mountainous settings, thereby attesting to the efficacy of the proposed approach.
Personalized Mood-Based Song Recommendation System Using a Hybrid Approach

Music recommendation systems are becoming a crucial concern for the music industry because of the rise of digitization and the subsequent increase in music consumption. Music applications continuously strive to enhance their recommendation systems to ensure that users have an exceptional listening experience and remain loyal to their platform.In the early days, the recommendation system used collaborative filtering and content-based approaches to achieve this goal, but these approaches have an issue with a cold start, and context awareness of these approaches is less. Researchers identified in the context of the personalization of songs, Emotion, and mood can play a luge role. Research has shown that a user’s current emotional state significantly influences their musical preferences in the short term. Therefore, the recommendation system moves toward mood-based recommendation approaches. The vast variety and context-dependent character of the data that must be considered present the main difficulty for mood-based recommendation systems. This information can vary greatly and is depending on several variables, including the user’s environment and personal circumstances.Hybrid approaches have shown very good results in this domain. Therefore, in this paper, we are proposing a hybrid approach for a mood-based personalized song recommendation system. This approach combines content-based and context-based approaches together. The proposed solution produces the output as a personalized song recommendation for the music listener. This output is determined by several parameters including user mood, the profile of the user, and history of previously listened to songs. This solution impacts all the stakeholders. it improves the quality of service of music streaming platforms and improves the user experience.
A Real-Time Assistance System with Virtual-Real Fusion for Similar Components Assembly

Assembly tasks are common in industrial production and daily life, however, paper instructions usually lack expressiveness and interactivity. In addition, components often have similarities in appearance and shape, which are not considered by traditional pose tracking methods and thus perform poorly. This paper proposes a real-time assembly assistance system, especially for similar parts assembly scenarios to improve working efficiency and user experience. The system obtains 6-DoF pose of each part for 3D synchronous display, detects assembly state, corrects wrong steps, and provides guidance for the next step. To cope with the problem of multiple similar parts obscuring each other during the process, our method VOSCO utilizes overall and positional consistency constraints to effectively segment target objects for the subsequent pose estimation FICP. Additionally, in the accelerated point cloud alignment operation, we compute the depth gradient by an optimized discrete differentiation operator and use it as the basis for point cloud sampling, which dramatically improves computational speed while maintaining alignment accuracy. Both VOSCO and FICP achieved good results in the custom dataset BP-SEG and BP-POSE for similar parts assembly scenarios, and the operation of the system satisfies real-time and good user experience.
Designing a Node.js Project Architecture for RESTful Microservices

Microservices architecture is a software design approach in which an application is constructed as a set of autonomous, loosely coupled, and independent services. Each service is designed to perform a specific business function and can communicate with other services through well-defined APIs or messaging protocols. Microservices find application in diverse domains, including e-commerce, media, finance, healthcare, government, military, logistics, and gaming, among others. This research contains a short review of the state-of-the-art issues of microservice development and proposes an effective approach to project architecture composition created for Node.js microservice. This approach includes best practices used specifically for Node.js applications.
Inversion Algorithm of Vertical Visibility Based on Laser Detection

Visibility has always been one of the main contents of meteorological observation[1]. Currently, there are various types of products for detecting visibility under different conditions, but none of them can detect vertical visibility. The vertical and horizontal distribution characteristics of the atmosphere are different, and there is a great demand for detecting vertical visibility in some practical applications[2]. This article analyzes the vertical distribution characteristics of atmospheric aerosols, uses LiDAR to detect the backscatter signal profile of atmospheric aerosols, inverts and calculates the backscatter signal profile, and obtains the atmospheric extinction coefficient. Firstly, it proposes to use the backward recursive method to calculate the vertical visibility value, and conducts detection experiments.
Landmark-Based Localization Using Range Measurements: A Stochastic Geometry Perspective

Many modern wireless devices with accurate positioning needs have access to many vision sensors, such as a camera, radar, and Light Detection and Ranging (LiDAR). In numerous scenarios where wireless-based positioning is either inaccurate or unavailable, using information from vision sensors becomes highly desirable for determining the precise location of the wireless device. While localization utilizing vision information has been explored from different algorithmic perspectives, the underlying mathematical underpinnings of this problem space remain largely unexplored. Inspired by this, we develop a new analytical framework for vision-based localization in which error-free distance measurements in vision data are utilized to accurately determine the position of the target. Compared to wireless-based positioning, a notable differentiation of this approach is the inclusion of non-unique landmarks, such as lampposts, which may lack distinguishable features in the vision data. For instance, when the target is located close to a lamppost, it becomes challenging to precisely identify the specific lamppost (among several in the region) that is near the target. By assuming that the landmarks of various types follow a marked Poisson point process (PPP), we establish that three range measurements are sufficient for determining the correct combination of landmarks in a two-dimensional plane. When the number of measurements is less than three, there exists a potential for making errors in associating these range measurements with the corresponding landmark combination. We provide a mathematical characterization of this probability of error, which involves a novel joint distribution of key random variables.
THz-Over-Fiber System With Orthogonal Chirp Division Multiplexing for Integrated Sensing and Communication

To achieve integrated sensing and communication (ISAC) applications with high data rates and high resolution, the terahertz-over-fiber (ToF) system has been recognized as a promising solution. However, the frequency-selective fading would deteriorate the performance of the ToF-based ISAC systems. In this work, we propose the orthogonal chirp division multiplexing (OCDM) waveform-based ToF system for high-performance ISAC applications. The system encodes data with the phase of the sub-chirps and obtains radar images by processing echoes with the zero-padded matched filtering algorithm. We experimentally demonstrate a proof-of-concept OCDM-ToF system, which simultaneously achieves a 32 Gbit/s data rate and 1.875 cm range resolution after transmission over a 10 km optical fiber and a 3.14 m wireless link at the THz band, for the first time. The experimental results indicate that the OCDM-ToF system can enhance robustness against the frequency-selective fading issue, as a benefit improving communication performance with high data rates while preserving the high sensing resolution of OFDM in the THz ISAC applications.
A Novel Frequency Reconfigurable Antenna for X, Ku, and K Bands Applications

In this paper, we introduce a novel frequency-reconfigurable antenna design that leverages the principles of split ring resonator (SRR) and defected ground structure techniques (DGS). These techniques are strategically applied to the antenna’s patch and ground, respectively. Additionally, the incorporation of two PIN diodes, situated within the radiating element and the ground plane, introduces the capability to achieve distinct resonant frequencies. Notably, these frequencies span the range of 8 GHz to 26 GHz, effectively covering the X, Ku, and K bands. The configuration of the proposed antenna incorporates two PIN diodes, enabling us to achieve four distinct states. Notably, a majority of the resonant frequencies exhibit a positive gain, resulting in an average gain of 2.55 dBi. Additionally, the peak S11 value is noteworthy, and the VSWR remains consistently below 2 across all resonant frequencies. The radiation pattern in the E-plane is omnidirectional, offering broad coverage, while in the H-plane, we observe a directional radiation pattern predominant at most frequencies. To ensure the reliability of our findings, we employ CST Microwave Studio (MWS) for initial simulations and subsequently validate the outcomes using High-Frequency Structure Simulator (HFSS).
BlzFS: Crash Consistent Log-structured File System Based on Byte-loggable Zone for ZNS SSD

Emerging Zoned Namespace (ZNS) SSDs provide hosts with fine-grained, performance-predictable storage management. The ZNS SSD divides its address space into zones, and each zone must be written sequentially and cannot be overwritten. However, the write constraint results in frequent small modifications of file metadata to record the latest logical block addresses of the updated data blocks, increasing the overhead of fsync() system call. We comprehensively analyze the metadata overhead of fsync on a real ZNS SSD and find that (1) the update size of most metadata blocks (e.g., tens of bytes) is far less than the IO unit of the zone (e.g., 4 KiB), and frequent fsyncs cause severe metadata write amplification; (2) metadata block IOs significantly increase the fsync latency, decreasing the throughput of workloads.To reduce the metadata overhead of fsync, we build the byte-loggable zone based on the persistent write cache of the opened zone, a novel extension of ZNS, which can be appended at byte granularity instead of block. Based on the byte-loggable zone, we present BlzFS, a high-performance and crash-consistent file system for ZNS SSD. Firstly, BlzFS maintains the update ranges of the metadata block by the update bitmap. Secondly, BlzFS ensures the persistence and consistency of metadata by logging the partial updates of metadata blocks to the device’s byte-loggable zone. Finally, BlzFS restores metadata consistency by replaying the metadata update log and scanning the list of recent allocated metadata blocks. In the evaluation, BlzFS shows a 33.5%∼96.1% reduction in metadata write traffic and a 1.37×∼1.91× throughput improvement compared with the original ZNS storage system.
Controllability Analysis and Stabilization of Untethered Tumbler Floating ROV

Floating ROVs have extensive applications on oceanographic research, environmental monitoring, and search and rescue operations. In this work, two levels of floating ROV stabilization is realized. First, the intrinsic stability is induced by the design of center of mass and moment of inertia based on the stability analysis, such that the ROV can always keep its upwards standing configuration. Besides, an underactuated control method for this novel untethered tumbler floating (TF) ROV is introduced for stabilization, which effectively enhances the stabilization efficiency based on the attitude controller. The model considering rigid body dynamics based on Euler equations, and nonlinear controllability with one, two, or three torques are analyzed serving for the moment of inertia design and underactuated controller. Simulation experiments are conducted to validate the efficacy of our approach, and the results demonstrate its effectiveness for stabilizing the TF-ROV.
How to Price Fresh Data with Strategic Users

The interests in obtaining fresh data in real-time applications have facilitated fresh data markets. However, existing works on designing fresh data markets have ignored strategic users. Being strategic means that users can optimally time their data purchases, which affects markets' profit. In this paper, we study a fresh data market, where strategic users with heterogeneous data valuations stochastically arrive over time. The strategic users decide the time of data purchase, considering the evolution of data freshness and prices, while the platform decides the data pricing policy over time to maximize its profit. We first consider a dynamic pricing policy, where the platform offers a price menu to each arrival user. The analysis is technically challenging, as heterogeneous users face different integer programming problems in optimizing their data purchase time, making direct optimization of data prices infeasible. To tackle the challenge, we adopt a mechanism design approach. We show that the direct mechanism design problem relax the original problem and obtain the optimal pricing policy analytically. Next, to reduce the implementation complexity, we study a single pricing policy, where the price is fixed over time. We derive the optimal single price analytically in a two-period refreshing model. Perhaps surprisingly, although strategic users have more purchasing options than non-strategic users, users who behave strategically may be worse off. Simulation results show that, although a platform refreshes the data less frequently in the presence of strategic users than facing myopic users, it can earn up to 5 times higher profit.
FlexZNS: Building High-Performance ZNS SSDs with Size-Flexible and Parity-Protected Zones

NVMe zoned namespace (ZNS) SSDs present a new class of storage devices with attractive features including low cost, software definability, and stable performance. However, one primary culprit that hinders the adoption of ZNS is the high garbage collection (GC) overhead it brings to host software. The ZNS interface divides the logical address space into size-fixed zones that must be written sequentially. Despite being friendly to flash memory, ZNS requires host software to perform out-of-place updates and GC on individual zones. Current ZNS SSDs typically employ a large zone size (e.g., of GBs) to be conducive to die-level RAID protection on flash memory. This impedes flexible data placement, such as mixing data with different lifetimes in the same zone, and incurs sizable data migrations during zone GC. To address this problem, we propose FlexZNS, a novel ZNS SSD design that provides reliable zoned storage allowing host software to configure the zone size flexibly as well as multiple zone sizes. The size variability of zones poses two interrelated challenges, one for the SSD controller to establish per-zone RAID protection, and the other for host software to manage variable zone capacity loss caused by parity storage. To tackle the challenges, FlexZNS decouples the storage of parity from individual zones on flash memory and hides the zone capacity loss from the host software. We verify FlexZNS on a ZNS-compatible file system F2FS and a popular key-value store RocksDB. Extensive experiments demonstrate that FlexZNS can significantly improve the system performance and reduce GC-induced write amplification, compared with a conventional ZNS SSD with large-sized zones.
Autonomous Trajectory Planning Based on Bézier Curve with Curvature Constraints and Piecewise-Jerk Speed-Time Optimization

In this paper, we propose an efficient trajectory planning algorithm with path smoothing based on the Bézier curve with curvature constraints and piecewise-jerk speed-time optimization. We use hybrid A* to generate a rough path and construct a safe corridor by inflating the path. After that, we formulate the smooth problem as a nonlinear programming(NLP) with piecewise Bézier curves. Since the curvature constraints for Bézier curves are difficult, we employ quartic Bézier Curves with special forms and compute the closed-form solution for the maximum curvature to simplify the representation of the maximum curvature. By using the special Bézier curves, we realize the gear shifts and easily guarantee the security, continuity, and feasibility of the path. Meanwhile, we add time variables based on PJSO, improving the quality of trajectory within an acceptable increase in time, making the allocation of time and speed better. Simulation and real-world experiments with a car-like robot in various environments confirm that our algorithm can generate a smooth, feasible, and high-quality trajectory for robots.
Conveyor: Towards Asynchronous Dataflow in Systolic Array to Exploit Unstructured Sparsity

Systolic array (SA) architecture efficiently offers parallel computation using a simple data movement across processing elements. However, their rigid structure and synchronous dataflow limit flexibility in handling sparse computations, resulting in underutilized resources and suboptimal performance. In this paper, we propose Conveyor-SA, a novel SA-based accelerator architecture leveraging asynchronous dataflow for unstructured sparsity exploitation. Conveyor-SA introduces three core mechanisms: Chunk Propagation for parallel data processing, PE Grouping to accelerate efficiently both sparse and dense CNN models, and Conveyor Queue for load imbalance mitigation. Our experimental results demonstrate that Conveyor-SA achieves an average speedup of 1.68x over the competitors while processing conventional CNN models. In addition, Conveyor-SA delivers 1.42x speedup over state-of-the-art sparse SA architecture while remarkably reducing the chip area requirement by 19.5%.
Big Data Visualization System Based on Target Data Query

This paper aims to explore a Big data visualization system, which applies Big data technology to actual business to meet user needs. Firstly, based on the user’s query request, determine whether the corresponding business data needs to be generated according to the request; Secondly, generate corresponding business data based on the user’s query operation; Finally, based on the characteristics of business data, the results of the system will be presented so that users can better understand and analyze business processes. By querying to obtain target business data, we can determine the best visualization processing methods and use these methods to improve the visualization effect of the data. In this way, we can effectively solve the problem of poor Data and information visualization effect in current technology.