Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Analysis of PTS and PDL Clipping Technique to Reduce PAPR OFDM in Nonlinear High Power Amplifier

Orthogonal frequency division multiplexing (OFDM) is one of the technologies used in wireless communication systems. OFDM has advantages such as having resistance to multipath fading and having a large capacity. However, this technique has drawbacks, including the high Peak to Average Power Ratio (PAPR) causes lower transmit power. This system is also easily contaminated by nonlinear distortion that occurs in the amplifier from the transmission power. Several techniques are used to reduce PAPR, namely signal distortion techniques and random signal techniques. This paper aims to analyze the performance of the BER and the resulting PAPR reduction by comparing OFDM signals with the original PAPR and OFDM signals with the derived PAPR. The technique used to reduce PAPR is Partial Transmit Sequence (PTS) combining using random signal technique and Palm Date Leaf (PDL) Clipping using signal distortion technique. This technique is applied to nonlinear high-power amplifier (HPA) to obtain effective power so that it has low cost.
Integrating GPS with GLONASS for high‐rate seismogeodesy

High‐rate GPS is a precious seismogeodetic tool to capture coseismic displacements unambiguously and usually improved by sidereal filtering to mitigate multipath effects dominating the periods of tens of seconds to minutes. We further introduced GLONASS (Globalnaya navigatsionnaya sputnikovaya sistema) data into high‐rate GPS to deliver over 2000 24 h displacements at 99 stations in Europe. We find that the major displacement errors induced by orbits and atmosphere on the low‐frequency band that are not characterized by sidereal repeatabilities can be amplified markedly by up to 40% after GPS sidereal filtering. In contrast, integration with GLONASS can reduce the noise of high‐rate GPS significantly and near uniformly over the entire frequency band, especially for the north components by up to 40%, suggesting that this integration is able to mitigate more errors than only multipath within high‐rate GPS. Integrating GPS with GLONASS outperforms GPS sidereal filtering substantially in ameliorating displacement noise by up to 60% over a wide frequency band (e.g., 2 s–0.5 days) except a minor portion between 100 and 1000 s. High‐rate multi‐GNSS (Global Navigation Satellite System) can be enhanced further by sidereal filtering, which should however be carefully implemented to avoid adverse complications of the noise spectrum of displacements.
Research on Surface Defect Detection of Steel Strip Based on Deep Learning

Surface defect detection of steel strip has become an indispensable part of quality inspection in steel strip production. An enhanced algorithm is suggested by this paper, GRW-YOLO, based on the YOLOv5 model, which solves the problem of insufficient precision in surface defect detection of steel strips and meets the requirements of real-time detection in industry. First, the backbone network incorporates the GAM (Global Attention Mechanism) to enhance the model's capacity in representing targets. Secondly, the Res2Net is used to reconstruct the C3 module to reduce the number of parameters and improve the model's generalization ability and precision. Then, the WIoU bounding box loss function is used to solve the problem of category imbalance and improve the model's robustness. Finally, to enable the network to detect objects of different scales, a detection layer is incorporated. Experimental results show that GRW-YOLO achieves a mean average precision (mAP) of 81.9% on the NEU-DET dataset, which is an improvement of 5.8% compared to YOLOv5s. Additionally, the detection speed reaches 67 FPS, surpassing the industrial requirement of 30 FPS. This demonstrates a favorable balance between accuracy and speed for GRW-YOLO.
Graph-Based Attack Path Discovery for Network Security

Enterprise network systems are confronted with an escalating threat landscape, requiring timely and effective attack detection and mitigation of the risk of potential financial losses and system damages. However, existing algorithms mostly rely on machine learning techniques or attack knowledge bases. They face challenges dealing with the large volumes of noisy network logs in enterprises, as well as the emergence of unknown cyber attacks. Moreover, previous research has predominantly focused on anomaly detection using raw network traffic capture, with limited exploration on attack path prioritization. To address these challenges, this paper introduces a novel algorithm for attack path detection and prioritization in network systems. Our approach gathers comprehensive asset information and network logs from multiple Network Intrusion Detection Systems (NIDSs). Through data processing and collation, the network data undergoes significant noise reduction and transformation into a network communication graph format. Subsequently, a Graph Neural Network (GNN) based anomaly detection algorithm is employed to extract and prioritize potential attack paths on the graph. This methodology leverages the power of unsupervised Machine Learning (ML) techniques and operates independently of prior attack databases. Incorporating path mining techniques, our algorithm provides visibility into identified attack propagation chain and the sequence of assets involved, which offers more valuable information compared to the repetitive atomic network traffic data from NIDSs. The algorithm is evaluated using the UNSW-NB15 dataset and proven to be effective and accurate with comprehensive experiment settings.
Fusion of AUV-Mounted 360-Degree Underwater LiDAR and Side Scan Sonar Data

Recent advancements in mapping coastal waters have birthed underwater bathymetric LiDAR systems, addressing the limitations of energy loss through the interaction of light with the air-water interface by Airborne bathymetric LiDAR systems. This study introduces an innovative and compact underwater 360° Pulsed Laser Line Scanner (PLLS-360°) system. This system boasts a 360° scanning angle and a 360° field-of-view (FOV) receiver, providing its ability to map the seafloor, water column, and water surface simultaneously thereby enhancing its utility in various scenarios. The PLLS-360° is suitable for diverse applications such as seafloor mapping, underwater asset inspection, and construction progress monitoring. The LiDAR system is integrated with an Iver3 AUV equipped with an interferometric side scan sonar (SSS) allowing LiDAR-sonar data fusion. A test involving mapping an artificial reef site at the Blue Heron Bridge in Florida, was carried out. The acquired LiDAR raw waveforms were corrected for spreading loss as the light returned from the target to the LiDAR receiver. The peak intensities of the corrected waveforms were detected, and the ranges were calculated from the peak position. The range was corrected for the AUV motion and georeferenced to obtain the final bathymetry. The sonar image and bathymetric data were fused, providing a more detailed description of the seafloor.
Reliability Centered Maintenance for Noncoherent Systems

Reliability Centered Maintenance (RCM) is used by the majority of complex industries to identify maintenance requirements and optimize maintenance strategies. With RCM, an asset can be determined as preventive maintenance significant if its failure would degrade system performance. However, for noncoherent systems where component’s working condition can also contribute to system failure, which essentially goes against the principle of RCM, it has never been discussed what maintenance implications should be derived from an RCM analysis. This paper attempts to present the potential noncoherent issue in RCM through a simple industrial example. The developed RCM automation system is applied to analyze the noncoherent system. Implications on how a noncoherent RCM can be performed will be given.
Towards an Uncertainty-aware Decision Engine for Proactive Self-Protecting Software

Proactive protection of software systems can be achieved through Moving Target Defense (MTD) techniques, which are designed based on addressing the questions of what to move, how to move, and when to move. However, the deployment of MTD techniques is subject to overhead and run-time uncertainties that can impact their effectiveness in defending against cyberattacks. The goal in this research is to improve the uncertainty-awareness and self-adaptation in a MTD decision engine in order to enhance the effectiveness of MTD techniques. The proposed approach is based on using the Partially Observable Markov Decision Process framework to quantify uncertainties. A systematic approach is proposed to achieve this objective, which includes modelling, designing, and engineering phases. The outcome of this research will make contributions related to (i) modelling solutions for run-time uncertainty, (ii) designing scalable methods for uncertainty-aware MTD planning, and (iii) realizing an uncertainty-aware MTD decision engine.
Distributed Approach to Satellite Direct-to-Cell Connectivity in 6G Non-Terrestrial Networks

Satellite direct-to-cell (D2C) connectivity considers the communication between satellites and low-cost handheld devices on Earth. It represents one of the most challenging aspects of the integration between terrestrial and non-terrestrial networks. Low Earth orbit (LEO), sub-6GHz bands, and large aperture satellite antennas are the key to enable D2C connectivity. The industry is tackling the problem with a conventional approach, consisting of the design of very large reflectors or phased arrays. This article proposes a new way to face the problem: the distributed approach. A satellite base station (BS) is decomposed into several small platforms in a so-called swarm configuration to form a sparse phased array. The use of small satellites promises cost-effective solutions, while distributed satellite systems (DSSs) increase the fault tolerance, and thus the reliability, of the entire constellation. This article compares the performance of conventional and distributed approaches under different conditions. It shows that distributed approaches outperform conventional ones even under unfavorable conditions and pessimistic assumptions. Important tradeoffs are derived showing the flexibility of distributed approaches. Finally, major research aspects for exploiting the full potential of the distributed approach are highlighted.
Covarying Adverbs in Post-Modal Position: A Collostructional Approach

This paper employs Covarying collexeme analysis and Correspondence analysis to explore the attractiveness and correspondence between two-character Chinese modals and adverbs in the pattern of [Modal + Adverb. ] A corpus of 2.95 billion Chinese characters was built to explore the collocational patterns. The results indicate that the attractions of adverbs may influence the modification boundary of the modal, and further reveal that the types of attracted adverbial collexemes largely correspond to modality types. Specifically, deontic modals yingdang and bixu tend to attract evaluative adverbs, which can convey requirements or expectations. Dynamic modals keyi and nenggou are inclined to attract descriptive adverbs that modify VP, typically indicating abilities or effort. Epistemic modals keneng, biran, yiding prefer adverbs indicating scopes, time, and negation. Notably, yinggai shows distinct behavior as it is inclined to attract question words and degree markers. The results also indicate that the combinational relationships of [Modal + Adverb] are correlated with modality types and underscore the importance of incorporating such patterns into the constructional modeling of modal semantics.
Continual Learning for Cuffless Blood Pressure Measurement using PPG and ECG Signals

Although numerous studies have been conducted on cuffless blood pressure (BP) estimation using machine learning methods, most of the data-driven models are static, with model parameters fixed after training is complete. However, BP is dynamic and the performance would degrade for a static model when the to-be predicted BP distribution deviates from the training BP distribution. In this paper, we propose a continual learning (CL) framework in which deep learning models are developed to learn dynamically and continuously for arterial BP (ABP) estimation with photoplethysmography (PPG) and electrocardiogram (ECG) waveforms. The effectiveness of the CL model is validated on UCI Repository and MIMIC-III database with a total of 132 individual samples, and compared with conventional training method. It was found that the CL model improved the ABP estimation accuracy in terms of mean absolute error (MAE) by 17.47% on average compared with conventional training model. Furthermore, the improvement increased with the variability of ABP. These results demonstrate that CL model has potential to estimate dynamic ABP, which has been challenging with conventional training.
Synchronizing Motor Imagery Cue in fNIRS Brain-Computer Interface to reduce confounding effects of respiration

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging method that measures oxygenated hemoglobin (HbO) levels in the brain to infer neural activity using near-infrared light. Measured HbO levels are directly affected by a person’s respiration. Hence, respiration cycles tend to confound fNIRS readings in motor imagery-based fNIRS Brain-Computer Interfaces (BCI). To reduce this confounding effect, we propose a method of synchronizing the motor imagery cue timing with the subject’s respiration cycle using a breathing sensor. We conducted an experiment to collect 160 single trials from 10 subjects performing motor imagery using an fNIRS-based BCI and the breathing sensor. We then compared the HbO levels in trials with and without respiration synchronization. The results showed that respiration synchronization yielded HbO levels that were less dispersed across trials, and a negative correlation between the dispersion index of HbO levels with MI decoding accuracies was found across the 10 subjects. This showed that synchronizing motor imagery cues to respiration can yield increased HbO level consistency leading to better MI performance. Hence, the proposed method holds promise to improve the decoding performance of fNIRS-BCI by reducing the confounding effects of respiration.
VIW-Fusion: Extrinsic Calibration and Pose Estimation for Visual-IMU-Wheel Encoder System

The data fusion of camera, IMU, and wheel encoder measurements has proved its effectiveness in localizing ground robots, and obtaining accurate sensor extrinsic parameters is its premise. We propose an extrinsic parameter calibration algorithm and a multi-sensor-based pose estimation algorithm for the camera-IMU-wheel encoder system. First, we propose a joint calibration algorithm for the extrinsic parameters of the camera-IMU-wheel encoder system, which improves the accuracy and robustness of the camera-wheel encoder calibration. We then extend the visual-inertial odometry (VIO) to incorporate the measurements from the wheel encoder and weight the wheel encoder measurements according to angular velocity in global optimization to improve the performance. We further propose a novel method for VIO initialization by integrating wheel encoder information, which significantly reduces the scale error in initialization. We conduct extrinsic parameter calibration experiments on a real self-driving car and validate the performance of our multi-sensor-based localization system on the KAIST dataset and a dataset collected by our self-driving vehicles by performing an exhaust comparison with the state-of-the-art algorithms. Our implementations are open source11https://github.com/chunxiaoqiao/VIW-Fusion.git.
Structure Design and Mechanical Properties of Novel Honeycombs Bumper with Various Densities for Underwater Robot of Port

In this paper, honeycomb bumpers with various densities were proposed, which absorb impact energy and provide support in engineering application of underwater robot of port. The Honeycomb bumper was based on Double Arrowhead Wings Honeycomb (DAWH) cell, which were mainly composed of double arrowhead honeycombs cell and fused with re-entrant honeycombs connection. In order to achieve longitudinal variable density and gradient design, the number of cell layers, cell size, number of cells, and connection positions in the longitudinal direction of the honeycomb dumpers exhibited difference. The finite element model of honeycomb dumpers with three typical densities based on equivalent method were established and selected matrix of polyurethane with resistant to seawater corrosion. The mechanical properties, energy absorption characteristic, dynamic response, and structure efficiency of honeycomb dumpers were studied with rubber bumper. The mechanical properties, energy absorption, deformation mode, section plane, and strain energy were recorded and discussed. The stress and energy absorption of the structure increased with the increase of cell combination diffusion angle and cell density. Honeycomb dumpers possessed a cost similar to rubber bumper but significantly greater performance, which were of great significance for the application of underwater robot of port and coastal engineering.
Fabrication of Implantable Microelectrode Array using Cyclic Olefin Copolymer and SU-8 via Photocrosslinking Lamination

In this work, a fabrication process for implantable electrodes using a Cyclic Olefin Copolymer (COC) substrate and a SU-8 passivation layer was presented. COC and SU-8 were shown to be suitable for implantable neural electrodes due to their biocompatibility, chemical resistance, and thermal stability. The electrodes were successfully patterned on the COC film, and the SU-8 passivation layer was coated while maintaining site-opened via photolithography. The photocrosslinking lamination of the substrate and passivation layer was used to produce electrodes with fine line widths of 20um without applying heat.
Key Feature Mining Algorithm Based on Self-organizing Fuzzy Rough Analysis for Target Uncertainty Sensing Information

Feature selection is a complex problem of knowledge discovery, especially when both perceptual features and target labels are ambiguous. In this paper, we propose an algorithm based on the theory of “self-organization analysis” and “fuzzy rough approximation” for mining key features from raw low-density features captured by various sensors such as radar, sonar, optical devices, etc. First, the spatial distributed clusters (SDCs) of the label-missing samples are obtained by iterative fuzzy self-organizing analysis. Second, a subset of candidate features is selected, which is then used to create a fuzzy correlation function and a fuzzy equivalence partition for the target sample. This fuzzy equivalence partition helps us to approximate the original SDCs and obtain an approximate degree value. Finally, all possible combinations of feature elements are traversed by the above method, and the feature subset with the largest approximation degree is selected as the key feature subset. Experiments based on target recording data show that the proposed algorithm can mine the key feature set efficiently without sacrificing the classification rate for target uncertainty sensing information.
Swarm Control Approach Combining Leader and Predator Agents

This study explores the challenges of identifying and controlling swarm behavior under the influence of external stimuli and environmental factors. We propose to improve the adaptability of swarm control approaches by utilizing a hybrid approach that combines both leader (attractive) and predator (repulsive) types of steering agents. To achieve this goal, I plan to complete three research steps during my PhD program and the following academic career. The expected result is to provide theoretical support for practical applications in the field of swarm systems.
Active Planar Mass Distribution Estimation with Robotic Manipulation

In this work, we present a method to estimate the planar mass distribution of a rigid object through robotic interactions and force/torque feedback. This is a challenging problem because of the complexity of modeling physical dynamics and the action dependencies across the model parameters. We propose a sequential estimation strategy combined with a set of robot action selection rules based on the analytical formulation of a discrete-time dynamics model. To evaluate the performance of our approach, we also manufactured re-configurable block objects that allow us to modify the object mass distribution while having access to the ground truth values. We compare our approach against multiple baselines and show that it can estimate the mass distribution with around 10% error, while the baselines have errors ranging from 18% to 68%.
Project-based learning of Feedforward Neural Networks with a physical pendulum

In this paper the design and implementation of a Feedforward Neural Network-based controller on a pendulum are presented to demonstrate an example of a hands-on project that can be applied at the graduate and undergraduate levels in data-driven control courses. The project is planned in four main steps: system identification, PID control implementation, inverse model identification, and Feedforward Neural Network controller implementation. These steps are explained in detail in the paper, along with the models and control structures, and the mathematical expressions and algorithms are provided for use in physical deployment. The contribution of this paper is a detailed description of the design process of the controller using Neural Networks and a PID.
Hierarchical Transformer for Visual Affordance Understanding using a Large-scale Dataset

Recognition, detection, and segmentation tasks in machine vision have focused on studying the physical and textural attributes of objects. However, robots and intelligent machines require the ability to understand visual cues, such as the visual affordances that objects offer, to interact intelligently with novel objects. In this paper, we present a large-scale multi-view RGBD visual affordance learning dataset a benchmark of 47,210 RGBD images from 37 object categories, annotated with 15 visual affordance categories and 35 cluttered/complex scenes. We deploy a Vision Transformer (ViT), called Visual Affordance Transformer (VAT), for the affordance segmentation task. Due to its hierarchical architecture, VAT can learn multiple affordances at various scales, making it suitable for objects of varying sizes. Our experimental results show the superior performance of VAT compared to state-of-the-art deep learning networks. In addition, the challenging nature of the proposed dataset highlights the potential for new and robust affordance learning algorithms. Our dataset is publicly available at https://sites.google. com/view/afaqshah/dataset.