Miracle-dz/UniFAR-FTUs huggingface dataset:
Dislocation–grain boundary interactions: recent advances on the underlying mechanisms studied via nanoindentation testing

Dislocation–grain boundary interactions: recent advances on the underlying mechanisms studied via nanoindentation testing Farhan Javaid1,2, Habib Pouriayevali1, Karsten Durst1,a) 1 Physical Metallurgy Division, Materials Science Department, Technische Universität Darmstadt, Darmstadt, Germany 2 School of Chemical & Materials Engineering (SCME), National University of Sciences & Technology (NUST), H‐12, Islamabad 44000, Pakistan a) Address all correspondence to this author. e-mail: k.durst@phm.tu-darmstadt.de
Statistics estimation in neural network training: a recursive identification approach

A common practice in mini-batch neural network training is to estimate global statistics using exponential moving averages (EMA). However, such methods can be sensitive to the EMA decay parameter, which is typically set by hand. In this paper, we introduce A daptive Li near S tate E stimation (ALiSE), an online method for adapting the parameters of a linear estimation model such as an EMA. Our work establishes a connection between parameter estimation methods in deep learning, including ALiSE, and recursive identification techniques in control theory. We apply ALiSE to a range of deep learning scenarios and show that it can learn sensible schedules for the EMA decay parameter. Compared to the naive EMA baseline, ALiSE leads to matching or accelerated convergence during training.
G-KMM: A Flexible Kernel Mean Matching Optimization Method for Density Ratio Estimation Involving Multiple Train & Test Datasets

In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM (kernel mean matching) method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The codes associated to our Python implementation can be found at https://github.com/CDAlecsa/ Generalized-KMM.
Synthesizing Realistic Cracked Terrain for Virtual Arid Environment Generation

Virtual environments play a pivotal role in various applications such as video games, simulations, and virtual reality experiences. Creating realistic and immersive virtual arid environments necessitates the generation of terrain featuring cracked surfaces, mirroring the parched landscapes of arid regions. This paper presents a novel approach for synthesizing realistic cracked terrain to enhance the authenticity of virtual arid environments. We explore the underlying principles of terrain generation, the significance of realistic cracked features, and the computational techniques used to achieve this realism. Through a series of numerical experiments, we validate the correctness of our proposed technical approach for synthesizing realistic cracked terrain for virtual arid environment generation.
Ultrafast Saturation of Electronic-Resonance-Enhanced Coherent Ultrafast Saturation of Electronic-Resonance-Enhanced Coherent Anti-Stokes Raman Scattering and Comparison for Pulse Anti-Stokes Raman Scattering and Comparison for Pulse Durations in the Nanosecond to Femtosecond Regime Durations in the

The saturation threshold of a probe pulse in an ultrafast electronic-resonance-enhanced (ERE) coherent anti-Stokes Raman spectroscopy (CARS) conﬁguration is calculated. We demonstrate that while the underdamping condition is a sufﬁcient condition for saturation of ERE-CARS with the long-pulse excitations, a transient gain must be achieved to saturate the ERE-CARS signal for the ultrafast probe regime. We identify that the area under the probe pulse can be used as a deﬁnitive parameter to determine the criterion for a saturation threshold for ultrafast ERE-CARS. From a simpliﬁed analytical solution and a detailed numerical calculation based on density-matrix equations, the saturation threshold of ERE-CARS is compared for a wide range of probe-pulse durations from the 10-ns to the 10-fs regime. The theory explains both qualitatively and quantitatively the saturation thresholds of resonant transitions and also gives a predictive capability for other pulse duration regimes. The presented criterion for the saturation threshold will be useful in establishing the design parameters for ultrafast ERE-CARS.
ScholarWorks@UARK ScholarWorks@UARK

Antibiotic resistance is increasing prevalence, particularly in Candida glabrata . This opportunistic pathogen is closely phylogenetically related to Saccharomyces cerevisiae; however, its characterization is limited . C. glabrata is only second to Candida albicans as a fungal pathogen in immunocompromised patients. Commonly resistant to azoles, the most common fungal therapy, it has become costly and challenging to treat. A histatin 5 derived antifungal peptide, KM29, has a high degree of efficacy in Candida species and S. cerevisiae . The objective of this work is to advance our understanding of the mechanism of action of KM29 against C. glabrata . Previous work in the lab used the S. cerevisiae deletion library as a related fungal system to identify mutants that resulted in increased resistance to KM29, with the goal of discovering pathways that could then be tested in C. glabrata . In this work we focused on plasma membrane transporters identified in the S. cerevisiae screen. Validation of the KM29 killing activity showed no significant increased resistance in deletions of four hexose transporters ( HXT2 , HXT3 , HXT5 , and HXT10 ) or two metal transporters ( FET4 and ZRT2 ). Deletion of the membrane sterol regulator LAM1 and its paralog SIP3 showed significant resistance to KM29, as well as the polyamine transporter AGP2 . We concentrated on setting up the CRISPR-Cas9 system to delete C. glabrata AGP2 . A successful deletion was confirmed by DNA sequencing and positive mutants displayed increased resistance to KM29 in C. glabrata , although no increased resistance to polyamine toxicity was observed. Further analyses are needed to investigate AGP2 function. We conclude that the screen in S. cerevisiae can be useful to identify pathways in C. glabrata to understand the fungicidal mechanism of KM29.
An Adaptive YOLO Model for Detection of Faulty Insulators in Power Transmission Network Using Unmanned Aerial Vehicle

Effective monitoring and problem detection are thought necessary for assuring continuous power supply. This study developed a machine learning approach to detect and diagnose insulator failures in power transmission lines by analyzing photos of cracked, polluted, and flash-overed insulators recorded using a drone. Through numerous rounds of the object recognition model YOLO (You Only Look Once), a diverse dataset of power transmission line photos was used to train and compare the performance of an enhanced YOLOv8x model versus YOLOv5n, YOLOv5x, YOLOv7, YOLOv7x, and YOLOv8n. Furthermore, approaches such as transfer learning and augmentation have been used to improve the model’s performance. The YOLOv8x model outperformed the other YOLO models tested, with an accuracy of 0.94, recall of 0.934, and Mean Average Precision (mAP) at 0.5 of 0.944 for insulator failure detection. The suggested fault detection machine learning approach combined with a drone-based system provides an adaptive fault monitoring system with high precision and low cost.
MultiFuse: Efficient Cross Layer Fusion for DNN Accelerators with Multi-level Memory Hierarchy

In order to facilitate the deployment of diverse deep learning models while maintaining scalability, modern DNN accelerators frequently employ reconfigurable structures such as Network-on-Chip (NoC) and multi-level on-chip memory hierarchy. To achieve high energy efficiency, it is imperative to store intermediate DNN-layer results within the on-chip memory hierarchy, thereby reducing the need for off-chip data transfers to/from the DRAM memory.Two well-established optimization techniques, node fusion and loop tiling, have proven effective in retaining temporary results within the on-chip buffers, commonly used to minimize off-chip DRAM accesses. In this paper, we introduce MultiFuse, an infrastructure designed to automatically explore multiple DNN layer node fusion techniques, enabling optimal utilization of the on-chip multi-level memory hierarchy.Experimental results demonstrate the effectiveness of our retargetable infrastructure, which outperforms Ansor’s algorithm. Our exploration algorithm achieves a remarkable 70% reduction in Energy-Delay Product (EDP) while gaining a 67x speedup in search time when executing the data-intensive MobileNet model on a single DNN accelerator.
Low Contact Resistance and Low Temperature Hybrid Bonding with Polyimide and Highly <111>-oriented Nanotwinned Cu

Among the recent decades, the development of fine pitch hybrid bonding is eye-catching, since this kind of technology could realize high-speed signal transmission with high reliability due to the shrunk electrical joints and the surrounded dielectrics [1]. The polyimide (PI) possesses low Dk (~3.61 at IMHz), low Df (~0.037 at 1MHz), low curing temperature, simple process ability at low cost, excellent mechanical properties, high resistance and stability. It is considered as an excellent candidate to protect joints and enhance their bonding strength [2]. As for nanotwinned copper (NT-Cu), it is also a candidate for low-temperature Cu-Cu bonding. Highly <111>-oriented NT-Cu can be electroplated with good uniformity on Si wafers. With the high surface diffusivity and the low oxidation rate [3]–[4], the bonding can be achieved at lower temperature in the same time. The bonding temperature can be reduced to below 250 °C by using high surface diffusivity NT-Cu and low curing temperature PI which has low glass transition temperature (Tg), and increase the bonding strength simultaneously. As a result, PI is incorporated with highly <111>-oriented nanotwinned Cu (NT-Cu) to achieve the low contact resistance hybrid bonding (< 1.7 x 10–8 Ω. cm2) with low bonding temperature (220 °C) in this study.
Differential Flatness and Minimum Snap Trajectory Generation for Cooperative Manipulation of Quadrotors Slung-Load System

The problem of cooperative manipulation of quadrotors slung-load system with the two quadrotors and a cable-suspended point-mass load is emphasized in the paper. The hybrid dynamic model is presented on the configuration space SE(3)×S2. The quadrotors slung-load system is created to be a differential flat system with the yaw angles of quadrotors and the load position using as the flat outputs. The attitude and control input of the ith quadrotor can be described by this set of outputs and their finite order derivatives. Moreover, the minimum snap trajectory with guiding term is designed for the quadrotors slung-load system to safe pass through a fixed rectangular corridor. The optimal load trajectory can be obtained by jointly constructing equality and inequality constraints based on minimum objective function. Finally, the numerical simulations are conducted to estimate the effectiveness of the recommend control pattern.
A Field-Validated Numerical Prediction Model for Railway-Induced Ground-Borne Vibrations

The pursuit of optimal land usage has endangered a reduction in the spatial separation between buildings and train passages, resulting in heightened vibration exposure for occupants and structures. Prolonged vibration exposure to structures, caused by dynamic train loads, alters their structural properties, and leads to deleterious effects such as damage, deterioration, and an increased risk of failure together with the degradation of serviceability conditions. However, the implementation of diligent vibration monitoring protocols holds promise for mitigating these adverse effects. In this study, a sensor-based accelerometer device called VIBSEN (Vibration Sensing) was developed to measure train-induced ground vibrations in the field. The dependability of the VIBSEN device was proven through gravity calibration tests and by comparing the results with those obtained from a calibrated vibrometer. Furthermore, a three-dimensional (3D) numerical prediction model of a train-track-ground system was developed using MIDAS GTS NX software. This model incorporated rail beams, concrete sleepers, ballast, sub-ballast layers, and subgrade. The acceleration-time history data obtained from the numerical model aligned well with the data obtained from the VIBSEN device during the experimental study. These findings affirm the dependable accuracy of the numerical prediction model and the efficiency of the VIBSEN device in measuring train-induced ground vibrations.
Lightweight Map Storage and Retrieval Method for Autonomous Navigation of UAVs in Large-Scale Cylindrical Spaces

The inspection of autonomous Unmanned Aerial Vehicles (UAVs) in large cylindrical enclosed spaces has difficulties in positioning and processing massive global map data in real-time required for planning. Because such environments have the characteristics of darkness, GPS signal rejection, approximate cylinder, and large-scale environmental spaces. Therefore, we built a UAV using multi-line lidar and simultaneous localization and mapping (SLAM) in this environment to adapt to the former characteristics. In the face of the feature degradation in the height direction caused by the cylindrical-like features of the environment, the constraint information of the height sensor is added in lidar SLAM. And a map storage method called selective ordered indexed map (SOIM) is proposed for storing and real-time retrieval of huge amount of map data in large spaces required by path planning during autonomous inspection tasks. We conducted inspection experiment in a desulfurization tower of a thermal power plant to verify the function of designed system. The results prove the feasibility of our system and presented that the proposed SOIM saved 98.86% of storage space compared to the point cloud map in this environment, and the voxel search speed was 22.53% higher than that of octree search.
UAVs Images Based Real-Time Insulator Defect Detection with Transformer Deep Learning

Insulator defect detection is important for the safety operation of the power grid, which can be inspected via the unmanned aerial vehicles (UAVs) patrolling, demanding high accuracy and real-time capability. In response to this requirement, this paper investigates a real-time end-to-end insulator defect detection algorithm, RT-DETR (Real-Time DEtection TRansformer) with the combination of the model compression method based on the parameter quantization and knowledge distillation. In order to reduce the model parameters and accelerate the detection speed, a lighter backbone and a regularization method for refining the attention computation are applied in the model. Further, a quantization training approach which combines the parameter quantization and self-distillation is used for the model compression. The proposed method is trained and validated on an open-source dataset. Experimental results demonstrate that the average mean average precision (mAP) of the proposed method for the insulator defect detection is 99.5%, and the inference speed is 23ms, meeting the requirements for the UAVs real-time inspection.
Navigation Strategy of Last-Mile Delivery Robot and Its Application to Disabled Ramp Access

In urban environments, irregular and unstructured ramp accesses are often found in various scenarios. To achieve efficient last-mile delivery, it is challenging to analyze the traversability of various ramp accesses and design a robot navigation strategy. In this paper, a navigation strategy for last-mile delivery robots in ramp access scenarios is proposed using techniques such as mapping and localization, traversability analysis, and path planning. First, 3D point cloud data is collected via the 3D LiDAR equipped on the top of the robot and imported into the 3D mapping module to achieve point cloud correction, segmentation, and matching. Thus, a 3D point cloud map and the pose of the delivery robot are obtained. Second, the pre-processed 3D point cloud map is further transformed into a 2.5D elevation map as well as a binary traversability costmap using Bayesian generalized kernel inference. Finally, the global and local path planners of the delivery robot are constructed by the dual-tree rapidly-exploring random trees star (RRT*) algorithm and the dynamic window approach (DWA) algorithm. This strategy is executed on the costmap where the traversability analysis has been conducted. Both simulation and application to our real-world delivery robot show that the proposed strategy can achieve ramp navigation effectively and reliably.
Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air.
Programmable Pressure Pneumatic System for Soft Robots

In the realm of soft robotics, the pneumatic system is a crucial component that plays an essential role in achieving optimal motion performance since all motion performance is ultimately linked to precise pressure control within the air chambers. To efficiently provide programmable pressure states for driving soft robots, this paper proposes a programmable pressure pneumatic system consisting of air pumps and valves with eight independent air channels. This system can provide precise hybrid negative and positive pressure for soft pneumatic robots. Closed-loop pressure control is achieved by sensing the pressure states in each air channel through sensors. Furthermore, two types of soft robots based on single-chamber soft pneumatic actuators are designed to verify the effectiveness and practicality of the pneumatic system. The proposed pneumatic system is a universal platform that can meet the pressure control requirements of various soft robots driven by both positive and negative pressure and holds significant implications for actuation and control in soft robotics.
A VUI Foundation and Performance Validation for Voice-to-Motion Control of Snake-like Robots

The locomotion of snakes is unique as it allows snakes to efficiently navigate complex and uneven terrains without articulated limbs. This movement pattern is attractive in the field of robotics exactly for its hyper redundancy, versatility, and lack of requirement for relatively complex locomotive systems. Snake-like robots that imitate these movement patterns are uniquely suited for use in extreme and hostile environments like deep sea exploration and outer space. Controlling robots in environments like these requires lots of training and intense concentration during operation. Voice User Interfaces (VUIs) can be used to bypass much of the need for this training and attention to operation by abstracting the direct control of a robotic system into the domain of speech. The design and implementation of a VUI demonstrating this idea is discussed here. The basic structure of a VUI is described and the implementation of a VUI in conjunction with a snake robot is shown. In experimentation the performance of the VUIs components were evaluated and the navigation of an obstacle course via the developed VUI and a remote controller were compared. The command recognition capability of the VUI was found to be 96% and the ability of the VUI to enable navigation of the obstacle course was found to be comparable considering the differences of the control formats.
An Underwater Snake Robot that Does Not Consider Actuators’ Waterproof: Design and Primary Experiments

This paper presents the design of an underwater snake-like robot featuring four fully submersible joints and verifying its primary underwater properties. The actuators that drive these joints are designed to focus on minimal friction, have a lubricant-free gear reducer, and have no waterproof sealing, making them suitable for direct exposure to underwater experiments. The paper thoroughly discusses the efficiency and command/actual torque measurement of these actuators, the design of the snake robot, and the primary underwater verification. The ultimate objective of this snake robot is to enable it to engage in interactive tasks that require direct mechanical interaction with the underwater surroundings. The execution of various tasks with this snake robot remains part of future work. The robot demonstrates serpentine and eel-like locomotion in the underwater environment, showcasing its adaptability and potential for various applications in underwater environment.
Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot*

This paper focuses on the autonomous navigation problem of the fixed-wing aerial robot, of which the inertial measurement unit (IMU) has time-varying error characteristic parameters. A robust variational Bayesian adaptive Kalman filter (RVBAKF) is proposed to tackle the noise outliers in integrated navigation brought by sensor uncertainties. The proposed method formulates the joint probability distribution of the system state vector and the measurement noise covariance matrix. A fading factor matrix with strong tracking quality is introduced to enhance the prediction on the prior distribution of the process noise covariance. Then a weighted sliding window mechanism has been constructed to obtain the posterior distribution of the measurement noise outliers. Therefore, the proposed approach is impressive in approximating both the process and measurement noise. The RVBAKF algorithm is implemented in an integrated navigation system which is composed of the strapdown inertial navigation system (SINS) and the global navigation satellite system (GNSS). The integration framework based on RVBAKF is conducted on two typical verification scenarios, which is proven to be exceptional in coping with the time-varying process and measurement noise by comparing the average root mean square error in misalignment angle, velocity and position with the strong tracking filter and the variational Bayesian filter.
Device-Aware Test for Ion Depletion Defects in RRAMs

Many companies are heavily investing in the commercialization of Resistive Random Access Memories (RRAMs). This calls for a comprehensive understanding of manufacturing defects to develop efficient and high-quality test and diagnosis solutions to push high-volume production. This paper identifies and characterizes a new defect based on silicon measurements; the defect is called Ion Depletion (ID). In our case study, 45% cycles suffered from an intermittent reduction in high resistance state and did not impact low resistance state. The paper shows that the traditional fault modeling based on linear resistors as a defect model is not accurate. To address this challenge, the Device-Aware (DA) defect modeling method is applied; an RRAM model of the defective device is developed and calibrated using measurements to accurately describe the impact of the defect on the electrical behavior of the memory device. Afterward, fault analysis is performed based on the DA defect model, and appropriate fault models are introduced; they show that the ID defect may sensitize undefined state faults. Finally, dedicated test and diagnosis solutions for the ID defect are proposed.