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This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

This study aims to investigate the hydraulic behaviour of a pilot-scale, two-staged, vertical flow constructed wetland (VFCW) for septage treatment, in terms of factors such as hydraulic retention time and hydraulic loading rate and its influence on the treatment dynamics. Because of intermittent feeding mode of VFCW systems and variation in its loading, its hydraulic behaviour is highly variable and need to be understood to optimize its treatment performance.

Tracer test were carried out using bromide ion with varying hydraulic loading rates (HLR) of 6.82 cm/d, 9.09 cm/d and 11.40 cm/d (i.e. equivalent to 75 L/d, 100L/d and 125 L/d). Tracer data is then analysed using the Residence Time Distribution (RTD) method.

RTD analysis showed that the increase in HLR increases the average hydraulic retention time (HRT). Subsequently, the increase in HLR results in a lower recovery of effluent, resulting in poor productivity in treatment. The study also showed that the removal of nitrogen and organic matter improved with increasing HRT. However, observations show no correlation between HRT and total solids removal.

A performance evaluation method (by tracer) is proposed to understand the hydraulics of flow in constructed wetlands, which has not been widely studied. Additionally, the dynamics of treatment in VFCWs treating septage may also be revealed by the tracer method. The study can be applied to any constructed wetlands designed for treatment of wastewater, septage or sludge.

The purpose of this paper is to reduce the strain and vibration during robotic machining.

An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.

The proposed intelligent approach can significantly reduce robotic machining strain and vibration.

The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.

Knowledge of the Internet Protocol (IP) address is essential for connection establishment in certain classes of synchronous distributed applications, such as Internet telephony and video‐conferencing systems. A problem of dynamic IP addressing arises when the connection to the Internet is through an Internet service provider, since the IP address is dynamically allocated only at connection time. Proposes and draws a contrast between a number of generic methods that can be classified as online and offline methods for the resolution of dynamic IP addressing. Online methods, which include the World Wide Web, exchange server and the dynamic Domain Name System, are only effective when both the caller and recipient are logged on to the Internet. On the other hand, offline methods, which include electronic mailing and directory service look‐up, provide an additional means to allow the caller to leave messages when the recipient is not logged on to the Internet. Of these methods, the dynamic Domain Name System and directory service look‐up appear to be the best for resolving dynamic IP addressing.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

To achieve new emission standards and enhance the denitrification of the oxidation ditch in the Handan West Sewage Treatment Plant, this research used the ASM Bio-P model based on self-made simulation software to study the optimum operation of the denitrification efficiency. The results indicate that:1) the volume of the aerobic zone was increased and the volume ratio between aerobic and anoxic zones was controlled to approximately 3:1; 2) the concentration of the dissolved oxygen (DO) in the aerobic zone was increased and was controlled at 1 mg/L in summer and 3 mg/L in winter; 3) the proper sludge retention time (SRT) was controlled and was maintained at 15 d in summer and 20 d in winter; and 4) the appropriate reflux ratio was controlled to 100% and 50% for summer and winter, respectively. The COD, TN, NH₃-N and TP in the effluent reached the first (B) standard of the Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant (GB18918-2002).

The small area network for data communication within routers is suffering from storage of packet, throughput, latency and power consumption. There are a lot of solutions to increase speed of commutation and optimization of power consumption; one among them is Network-on-chip (NoC). In the literature, there are several NoCs which can reconfigurable dynamically and can easily test and validate the results on FPGA. But still, NoCs have limitations which are regarding chip area, reconfigurable time and throughput.

To address these limitations, this research proposes the dynamically buffered and bufferless reconfigurable NoC (DB2R NoC) using X-Y algorithm for routing, Torus for switching and Flexible Direction Order (FDOR) for direction finding between source and destination nodes. Thus, the 3 × 3 and 4 × 4 DB2R NoCs are made free from deadlock, low power and latency and high throughput. To prove the applicability and performance analysis of DB2R NoC for 3 × 3 and 4 × 4 routers on FPGA, the 22 bits for buffered and 19 bit for bufferless designs have been successfully synthesized using Verilog HDL and implemented on Artix-7 FPGA development bond. The virtual input/output chips cope pro tool has been incorporated in the design to verify and debug the complete design on Artix-7 FPGA.

In the obtained result, it has been found that 35% improvement in throughput, 23% improvement in latency and 47% optimization in area has been made. The complete design has been tested for 28 packets of injection rate 0.01; the packets have been generated by using NLFSR.

In the obtained result, it has been found that 35% improvement in throughput, 23% improvement in latency and 47% optimization in area has been made. The complete design has been tested for 28 packets of injection rate 0.01; the packets have been generated by using NLFSR.

Recent changes in the economy have altered both the internal and external operations of organizations. In response to the economic downturn, organizations have been forced to dramatically change their work practices and processes. Such practices inevitably create concern for employees as resources become more scarce, rewards and processes become more uncertain, and the marketplace becomes more competitive. To avoid these stressful situations and survive within their organizations, workers have to become more flexible and responsive. However, the specific ways in which the economic downturn will affect worker well-being has yet to be determined. In this chapter, we propose an integrative model of the politics– stress relationship and demonstrate the key role played by economic conditions.

The purpose of this paper is to provide a contemporary look at the current state‐of‐the‐art in wireless sensor networks (WSNs) for structure health monitoring (SHM) applications and discuss the still‐open research issues in this field and, hence, to make the decision‐making process more effective and direct.

This paper presents a comprehensive review of WSNs for SHM. It also introduces research challenges, opportunities, existing and potential applications. Network architecture and the state‐of‐the‐art wireless sensor communication technologies and standards are explained. Hardware and software of the existing systems are also clarified.

Existing applications and systems are presented along with their advantages and disadvantages. A comparison landscape and open research issues are also presented.

The paper presents a comprehensive and recent review of WSN systems for SHM applications along with open research issues.