Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of urban transport – a comparative analysis of selected cities by taxonomic methods

Abstract This paper aims to perform comparative analysis of urban transport in selected global cities and to verify the existence of clusters. It was based on data source from the World Organization Data Urban. It used Indicators proposed in ISO 37120 Sustainable Community Development. The test procedure exploited taxonomic methods as Ward’s hierarchical analysis and the deagglomerating k-means analysis. The empirical analysis comprises four indicators: (I) high capacity public transport system, (II) light passenger public transport system, (III) annual number of public transport trips and (IV) number of personal automobiles. Main results are the classification of selected global cities, identification and characterization of trends in the field of urban transport in each group. The empirical analysis confirms a substantial diversity of urban transport in selected cities. These differences arise from their past, geographical location, size, different reactions to the transition process and the different economic structures. The taxonomic analysis of the urban transport in selected cities separated five independent classes typological. This elaboration aims to examine the role of urban transport in city management based on a literature review, databases and reports of the European Union.
Methanogens (Methanococcoides spp.) Glycine Betaine as a Direct Substrate for

Nine marine methanogenic Methanococcoides strains, including the type strains of Methanococcoides methylutens , M. burtonii , and M. alaskense , were tested for the utilization of N -methylated glycines. Three strains (NM1, PM2, and MKM1) used glycine betaine ( N , N , N -trimethylglycine) as a substrate for methanogenesis, partially demethylating it to N , N -dimethylglycine, whereas none of the strains used N , N -dimethylglycine or sarcosine ( N -methylglycine). Growth rates and growth yields per mole of substrate with glycine betaine (3.96 g [dry weight] per mol) were similar to those with trimethylamine (4.11 g [dry weight] per mol). However, as glycine betaine is only partially demethylated, the yield per methyl group was signiﬁcantly higher than with trim-ethylamine. If glycine betaine and trimethylamine are provided together, trimethylamine is demethylated to dimethyl-and methylamine with limited glycine betaine utilization. After trimethylamine is depleted, dimethylamine and glycine betaine are consumed rapidly, before methylamine. Glycine betaine extends the range of substrates that can be directly utilized by some methanogens, allowing them to gain energy from the substrate without the need for syntrophic partners.
Optimum Design and Stiffness Analysis of a 3-RCU Parallel Manipulator *

Large tilt angle is required for parallel manipulators in many applications, this is a challenging issue in the field. In this paper, the optimum design of a 3-RCU parallel manipulator with 1T2R DoFs is carried out to realize the performance of large tilt angle output. The parameter-finiteness normalization method is used to build the parameter design space, and the motion/force transmission and constraint performance indices are used as the evaluation criterion. On these bases, the performance charts have been generated. Taking the constraint condition of achieving 45° tilt angle in all directions into consideration, an optimum region in the parameter design space has been derived and a group of optimized parameters is obtained. According to the results of optimum design, an CAD model of the manipulator is built. Based on perturbation method and principle of virtual work, a stiffness analytical model is established. Finally, the stiffness has been investigated, and the accuracy of the stiffness analytical model has been verified by comparing with the stiffness calculation using finite element analysis method. The work in this paper lays the foundation for the development of the manipulator.
A Cloud-based IoT-enabled framework for BCI applications

The interplay of brain signals and the Internet of Things (IoT) is an emerging area. The processing of electroencephalographic (EEG) signals is mainly divided into two categories: (1) signal analysis for diagnosis and (2) brain-computer interfaces that can control devices through brain signals. Some applications of EEG are early detection of seizures, wheelchair control, and smart homes. Processing EEG is a complex task that requires expertise in fields such as neuroscience and computer science. There are frameworks to facilitate EEG processing, such as MNE-Python. Utilizing distributed and parallel processing is crucial in dealing with large-scale EEG data or when requiring scalable models. We have looked into platforms like WeBrain and a Spark-based framework to understand their capabilities for the issue. Based on our findings, we suggest a cloud framework. It supports the main features of previous works. It includes EEG processing capability using common machine learning (ML) and deep learning (DL) approaches based on the user’s required action paradigm. Since it is possible to run some trained models on limited resources devices in fog computing, an IoT device with caching capability is proposed to send and receive data. Collaboration and data sharing among users are made possible in this system.
6D Grasp Pose Detection in Cluttered Environment from a Single-view

Robot grasping has always been a hot topic in the research field, but there are still a lot of problems to be solved. In this paper, we propose a general grasping method for the unknown objects in cluttered environment from a single-view. Our method is divided into three steps: sampling points selection, grasping pose sampling and grasping pose evaluation. Firstly, we generate a pixel-wise probability image based on the trained sampling affordance network from RGB images, and map the points with high probability to the 3D point cloud combined with the depth map. Then, we obtaine the candidate grasps by sampling on the proposal points, and finally we evaluate the candidate grasps through the trained grasp evaluation network. The network can capture the geometric structure represented by the point cloud information. The proposed method directly uses the original sensor data and can increases the sampling success rate.
Modelling and Simulation of Nanogenerator Using Vertically Integrated Zinc Oxide Nanowire Array

Recent developments in nano-level energy harvesting are mainly focused on using piezoelectric power generators from compression and vibration modes. In this work, a vertically integrated zinc oxide piezoelectric nanowire array was modeled to scavenge energy from a low-frequency compression force. COMSOL Multiphysics $\mathbf{5. 4}$ software was used to simulate and model nanowire array structures to ensure the potential distribution and overall electric energy generator of the piezoelectric structure under compression displacement. Piezoelectric constitutive equations were used to develop mathematical equations in terms of comparing and confirming induced piezoelectric outputs. Theoretical and mathematical results confirmed that the voltage output of the nanowire array does not depend on the number of nanowires. Total electric energy harvested by the array depends on the number of nanowires and nanowire density.
Exploiting and Enhancing Computation Latency Variability for High-Performance Time-Domain Computing-in-Memory Neural Network Accelerators

To address the inefficiency resulting from data movement in Von Neumann architecture, computing-in-memory (CIM) is a promising solution due to its in-situ analog computation. Among the various types of CIMs, time-domain CIM stands out as a promising solution for achieving high energy efficiency and high readout resolution by employing time-to-digital converters (TDC) instead of analog-to-digital converters (ADC) to convert time-domain delays into digital values. However, the performance of the accelerator may be constrained by the maximum operating frequency of time-domain CIM, which is significantly lower than that of digital circuits.This paper proposes an architecture for a time-domain CIM-based neural network accelerator that leverages the varying output time of the TDC. The key contributions of this work are as follows: 1) We introduce an early-termination scheme for time-domain CIM, which dynamically determines the length of the CIM clock period by deriving the maximum possible multiply-accumulate (MAC) value based on the current input. This approach reduces computation time for low-MAC results. 2) We propose an input-inversion scheme to decrease the computation time for high-MAC results. By employing linear combination, we perform bit-inversion on large inputs and compensate for the results using a low-cost digital circuit. 3) We propose a hardware optimization on the compensation circuit by combining it with shift-adders in traditional neural network accelerators.Experiments show that our schemes could gain 2× ∼ 2.9× speedup under different clock period specifications with 5.82% area overhead compared to the CIM macro.
SMRTS: A Performance and Cost-Effectiveness Optimized SSD-SMR Tiered File System with Data Deduplication

Storage tiering (e.g., SSD+HDD) is designed to achieve a better tradeoff between performance and cost-effectiveness for storage systems. With the development of Shingled Magnetic Recording (SMR) drives, replacing conventional HDD with a higher density of SMR drives in tiered storage can further improve cost-effectiveness. However, with data tracks overlapped in SMR drives, the "non-sequential' writes in SMR drives cause explicit performance penalties, which is the most challenging issue of using SMR drives in storage tiering.In this paper, we present SMRTS, a file system for SSD-SMR tiered storage with data deduplication. First, SMRTS deduplicates the files being migrated from SSD to SMR to solve the non-sequential write issue of SMR drives and further optimize the space utilization. Second, to address the performance overhead caused by deduplication, we propose file recipe reuse and refresh, hints-based container allocations, and fast container validation to address the penalties caused by data fragmentations. We conduct experimental evaluations of SMRTS using both benchmarks and real-world workloads. The evaluation results show that compared with a compatible file system on SSD+HDD tiered storage, SMRTS achieves a similar performance but provides a much larger space (at least 1.25X). The proposed optimizations improve migration performance up to 17X.
Carrier Bot: A UWB-based Human Following Mobile Platform for Intra-Office Transport with an Intuitive Teach-and-Repeat Programming

Despite the rapid development in robotics, the use of mobile robots in everyday life is currently not yet widespread. The complicated programming interface of mobile robots is often an obstacle to use for people who are not technically proficient. To this end, we present a novel method for teach-and-repeat of mobile robots by Ultra-Wideband (UWB) based human following. This approach has the following two advantages. First, the human following using UWB releases capacities for parallel activities during the teach-in process. Second, the programming of the robot path is conducted directly on the robot hardware using a self-developed UWB-based handset, which reduces setup time. Real-world experiments are conducted using our in-house developed mobile robot platform, the "Carrier Bot", whose hardware design has been specially selected to facilitate ease of use for indoor teach-in programming. Experiments show that the proposed system can reach a repeatability of 0.05 m of at least 12 meters path.
Learning-model-based Control for Robot Manipulators Sensorless Kinesthetic Teaching Using Sparse Feature Dynamics

In modern scenarios, kinesthetic teaching helps users to quickly program and plan trajectories in complex environments without specialized knowledge of robots. It has become one of the typical applications of human-robot interaction (HRI) to improve efficiency. Since the dynamic model of the robot reflects the mapping between joint motion and driving torque, accurate models are required to achieve good performance in sensorless kinesthetic operation control. Unfortunately, due to model uncertainty, assembly errors, and the lack of information provided by manufacturers, it is difficult to obtain a reliable dynamic model. This paper proposes a novel robot sensorless kinesthetic teaching scheme based on sparse feature learning dynamics. Firstly, without physical structural parameters, precise robot dynamics are learned through data-driven technology. Secondly, a learning-model-based reference controller is designed to effectively reflect the motion state of the robot under external force. The sequential minimal optimization (SMO) algorithm is used to eliminate the adverse effects of static friction on traction. Finally, the control scheme is used to achieve sensorless kinesthetic teaching on a 7-DOF robotic arm.
A Data-Driven Phantom Zeros Prediction Algorithm for Traction Force Sensor in Kinesthetic Demonstration

Kinesthetic demonstration requires accurate force/torque measurements from sensors. However, even in static conditions, the sensor readings exhibit non-zero fluctuations, which can lead to unstable force control and impair the quality of kinesthetic demonstration. In this paper, we refer to the above problem as phantom zeros and systematically analyze factors contributing to their variability of a traction force sensor. Neural networks (NN) are first introduced to model the complex nonlinear mapping between sensor orientations and phantom zeros. The initial parameters of the NN are then optimized using a genetic algorithm (GA) to prevent convergence to local optima and improve modeling accuracy. In addition, we develop an experimental platform with a physical UR10 robot and a custom traction sensor to comprehensively evaluate the proposed approach. Results demonstrate that the GA-optimized NN achieves higher precision and robustness in predicting phantom zeros under different orientations compared to least squares and vanilla NN baselines. By modeling and predicting phantom zeros, the proposed method can filter out phantom force fluctuations during kinesthetic demonstration, while preserving critical motion information. This work provides insights into modeling and mitigating force/torque sensor uncertainties for enabling more precise robot control and interactive guidance.
Vehicle Trajectories Prediction via Nearest Neighbor Historical Behavior

Trajectory prediction is crucial in enabling autonomous driving systems to make informed decisions, plan appropriate paths, and enhance traffic safety and efficiency. It remains an immensely challenging task due to complex interaction between vehicles, the difficulty of extracting traffic rules information, and the dynamic changes in traffic flow. Existing methods model the interactions among vehicles or extract traffic flow density features, but overlook the effects of neighboring vehicles’ movements and interactions, which contain traffic rules and the influence of surrounding traffic conditions. To achieve this, we propose a new method taking into account neighboring vehicles’ dynamic behaviors and history, allowing for a more comprehensive understanding of the traffic environment. Firstly, a novel dual-stream nearest vehicle attention mechanism method is proposed that leverages the historical state and position of the neighbors’ vehicle and captures its features. Secondly, in order to effectively encode these features, we recombine them by the multi-head attention mechanism. Lastly, in order to fusion these features and other inputs, we extract and combine the relationships between them by a self-attention mechanism. Our approach not only outperforms other methods in evaluation metrics but also demonstrates excellent results in real-world scenarios.
Non-Rigid 3D Point Set Registration With Reliable Hybrid Mixture Model

Non-rigid point set registration is an essential technique in fields such as robotics, computer vision, image-guided surgery, and augmented reality. However, effectively addressing non-rigid deformations and handling noise interference remains a significant challenge in this context. This paper introduces a novel hybrid mixture model (HMM), combining a Gaussian mixture model (GMM) to describe positional features and a Fisher mixture model (FMM) to represent orientation features. This hybrid mixture model offers a solution to the non-rigid point set registration (PSR) problem. The proposed approach iteratively optimizes model parameters through the maximum expectation (EM) algorithm. It comprehensively explores registration performance, accounting for non-rigid deformations amidst isotropic and anisotropic positional noise. The proposed approach further incorporates reliable normal vectors for evaluating orientation features. Extensive experiments on non-rigid PSR demonstrates the improvements our algorithm offers in terms of both robustness and accuracy.
Echo-based dynamic trajectory generation for customised unilateral exoskeleton applications

For unilateral pathologies, effective rehabilitation relies on the use of a customised trajectory in order for the user to relearn a natural and symmetrical gait. In recent years, lower-limb exoskeletons have seen a growing interest due to their capacity to provide support and facilitate repetitive exercises while correcting the user’s motion. However, in the context of robotic-assisted locomotion, the investigated trajectory models tend to rely on generating standardised walking patterns that lack step-specific customisation, and therefore do not account for the dynamic variations of natural gait.This paper investigates the viability of an echo-based approach for trajectory generation, which centres around the dynamic relabelling of a time-invariant reference trajectory, based on the motion of the contralateral leg. The presented cascaded network combines (1) a classifier that determines the gait phase performed by the sound leg and updates the reference trajectory accordingly, with (2) a regressor that uses electromyography inputs from the investigated leg to predict the gait cycle percentage performed, and provide the associated knee angle based on the dynamic reference.This trajectory generation framework was evaluated on 6 able-bodied subjects, using both steady-state and transient speeds. Despite some discrepancies in the range of motion, the produced knee angle trajectory strongly resembles the experimentally captured ones for both conditions, with an average mapping Root Mean Squared Error across subjects of 4.62°±0.39° for steady-state and 5.88°±1.83°for transient speeds. This proof-of-concept implementation demonstrates the potential of an echo-based approach for personalised dynamic trajectory generation in unilateral exoskeleton applications.
Conservation Voltage Reduction in Low Voltage Distribution Networks

Conservation Voltage Reduction (CVR) is the practice of operating power systems at the lower voltage limit (0.95 – 1 p.u) of the acceptable voltage range. CVR is considered globally as part of the energy savings program. The journey to save energy in power systems has led to the exploration of Volt/Var optimizers and voltage optimization including CVR implementation. CVR is implemented because of the problems that come with operating close to the upper limit of the acceptable voltage range such as mass of wasted energy as well as higher than necessary electrical bills and reduced lifespan of equipment. The problems that come from operating at a voltage level close to the upper limit affect both consumers and distributors. This project showed that implementing conservation voltage reduction in an llkV/400V distribution network will provide some level of energy savings to both the consumer and utility distributor. In this paper, MATLAB/Simulink was used to model both the control and CVR with real time parameters either calculated or referenced from the Electricity Company of Ghana (ECG). The simulation results for the CVR model managed to maintain the voltage within the lower range of 220-230V (0.95 – 1 p.u.) instead of the upper range of 240–250 under conditions such as overvoltage, undervoltage, increase load demand and reduced load demand.
Fast Visual Servo for Rapidly Seafood Capturing of Underwater Delta Robots

In this paper, we propose and design an underwater delta robot for fast seafood grasping. First, the hardware structure of the robot is described in detail. After that, a visual servo control method for fast catching of this underwater delta robot is proposed. The method is able to generate real-time radial trajectories so as to realize the catching based on the swaying of the robot body as well as the movement of the object to be caught. In the actual grasping test, the moving platform and the slave arm can occlude to the target resulting in the loss of target position information. Therefore, we propose a position prediction method to predict the position of the grasped object when occlusion occurs, thus improving the success rate of grasping and ensuring a smooth robot trajectory. Finally, several land and underwater experiments were conducted with good results, which verified the feasibility of the robot structure and algorithm.
HyF2FS: A Filesystem to Fully Exploit the Parallelism of Hybrid Storage

Hybrid storage systems can fully leverage the advantages of multiple devices to achieve better performance. However, current systems are designed primarily for a slow disk with an expensive fast device at high costs. They ignore device features and workload status while placing data. The issue of cache pollution is affecting their data hotness identification. Besides, inconsistent load status in multiple devices is overlooked during migration. These shortcomings constrain the overall performance of the system.To solve this, we propose HyF2FS, a hybrid storage filesystem based on F2FS. HyF2FS features a cache-tiering integrated architecture that stores hot data and metadata in an accelerator while asynchronously migrating cold data to the SSD, which provides cost-effective opportunities to optimize device parallelism. HyF2FS uses multidimensional scores to place data on the appropriate device to achieve high bandwidth. To improve data hotness identification, HyF2FS proposes two-level counters. Besides, a migration window is employed to minimize the impact of migration on foreground I/O. By implementing these scheduling algorithms, HyF2FS can fully exploit the parallelism of both fast and slow devices. Experimental results demonstrate significant improvements in throughput (116%-244%) and latency reduction (49%-64%) compared to F2FS and other hybrid storage systems.