Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of LiDAR Array System for Damaged Road Characteristic Identification on Moving Vehicle

In smart city infrastructure, road management is a critical component of transportation infrastructure inspection to ensure a safe driving experience. The current method relies on a visual inspection or survey to monitor road conditions. Several studies have been widely developed to change visual surveys, such as ultrasonic detection systems, but the accuracy of the devices is limited due to environmental factors. LiDAR, on the other hand, provides a more accurate alternative by measuring distance. In this paper, we propose a low-cost infrared Time of Flight (ToF) sensor with a programmable Region of Interest (RoI) feature to adjust the pointing of the sensor. The RoI identifies road characteristics by setting a LiDAR array, including depth/height, width, and length information. The success of this system was tested through an experiment with an error rate of less than 20% for the width and depth measurement but 35% for the length measurement.
A 286-299GHz Dual-Core Push-Push VCO With Harmonic Enhancement in 130-nm SiGe BiCMOS Technology

A 286-299GHz dual-core push-push voltage-controlled oscillator (VCO) fabricated in 130-nm SiGe BiCMOS is presented in this letter. A compact and precise layout is applied around the transistor, while the LC-tank is designed for optimal Q factor. To increase the second harmonic output power of common-mode nodes, a harmonic enhancement technique is adopted in this letter, which involves specific transmission lines and a common-mode capacitor Cr. The presented VCO delivers a peak output power of -1.2dBm, with a DC-to-RF efficiency of 0.66%, and achieves a phase noise of -100dBc/Hz as well as a FoMT of -161.9dBc/Hz at 10MHz offset.
Dynamic optimization method of target coverage based on reinforcement learning for wireless visual sensor network

Wireless visual sensor network (WVSN)-based video surveillance systems have been widely used in a variety of monitoring fields. The problem of maximizing target coverage with the minimum visual sensors is receiving a great deal of attention in WVSN. Because video sensors’ sensing range is limited by their field of view (FoV), the different rotated angles cover the various target nodes. We propose a distributed adaptive learning algorithm for target coverage of a visual sensor network based on the direction coverage characteristics of the visual sensor nodes. First, we built a dynamic coverage optimization model of considering energy efficiency based on the different coverage directions of visual sensor network nodes, and then proposed a distributed target coverage optimization algorithm based on reinforcement learning (DTCRL). Experiment results show that the proposed DTCRL algorithm has a significantly higher average target coverage ratio than typical distributed algorithms. Furthermore, the proposed method uses fewer active sensor nodes and has a higher target coverage ratio than other algorithms. It also indicates that the proposed algorithm not only improves the utility of the target coverage but also reduces energy consumption. It provides technical support for the visual sensor network’s target coverage application.
Underwater breadcrumb localization

In most cases, underwater localization implies a fusion of two or more sensors, usually inertial and acoustic ones. Acoustic localization demands the deployment of the acoustic baseline if precise measurement is needed. Using a fixed predeployed baseline limits the accuracy of location estimates outside certain operating areas defined by the baseline. In case an AUV or a diver has to be localized underwater, their trajectory is usually unknown, so it is a challenging task to deploy a baseline in such a manner that localization is accurate. In this paper, the sequential deployment of acoustic breadcrumb modems is suggested as the alternative to the conventional baseline deployment. This type of sequentially deployed acoustic baseline in combination with IMU is first simulated and later on tested in a constrained environment in shallow waters in order to show what are the main challenges, advantages and potential problems with this type of localization.
Learning-Based Predictive Control Using a Hybrid Model with Adaptive Domain of Validity*

The model quality is a key factor for any model predictive control (MPC) scheme to ensure safe and optimal operation of the controlled system. We propose a novel learning-based MPC (LBMPC) approach based on a hybrid model formed of a mechanistic model augmented with a data-based model trained offline which compensates the mismatches of the mechanistic model. If the prediction accuracy of the nominal model deteriorates due to disturbances or new modes of operation, the nominal model is augmented with an additive model learned online using a limited amount of data to capture the residual of the offline trained model. As using data-based models can be unreliable outside their training regions, we estimate their domain of validity via a one-class support vector machine (SVM) trained on low-dimensional projections of the training data. The predicted validity of each data-based model is used to weigh its contribution to the overall model. During online application, the domains of validity of the data-based models are adapted if needed. We show the advantages of the proposed LBMPC approach via a continuous stirred tank reactor (CSTR) example.
Ensemble Boosting Method of Power System Transient Stability Assessment Based on Cost-Sensitive Learning

Data-driven techniques have proven effective in enhancing prediction accuracy and speed for power system transient stability assessment (TSA). However, these methods are still beset by challenges such as small sample size issues and lack of interpretability. To address these problems, this study proposes a transient power system evaluation method based on Adacost, which uses a neural network as the base classifier and introduces a cost-sensitive coefficient into the Adaboost algorithm to increase the adjustment coefficient of misclassified samples. The proposed model is validated on an electromechanical transient simulation example of the IEEE39 bus test system, demonstrating its efficiency in handling cases with imbalanced sample categories. Furthermore, the SHAP method are employed to explain the model prediction results in terms of feature importance.
MIPAD: Mini Program Analysis for Clone Detection using Static Analysis Techniques

In recent years, third-party platform-mounted applications, referred to as mini programs, such as health QR codes, transport codes, and utilities, have been gradually replacing traditional mobile applications due to their no-installation-uninstallation and use-it-and-go feature. However, the massive growth of mini programs has led to concerns about protecting the copyright of their code. Currently, there is not enough research on clone detection for mini programs, and the language features of mini programs make it difficult to detect plagiarism due to incomplete behaviour observation and challenges in calculating similarity. To address this gap, we propose MIPAD, a detection method based on static feature analysis, including statistical features (SF) for clustering analysis, layout features (LF), and code features (CFF, FDF, TLDF) for similarity detection. To enhance the robustness of the LF and CFF, FDF, TLDF features during the feature extraction phase, we used a fuzzy hash algorithm. To speed up the dependency graph similarity computation, we propose a fast anchor-based similarity computation algorithm. To address the lack of publicly available large sample datasets in this domain, we designed a mini program crawler method that can fuzzy crawl samples based on a seed list and expand the list in real-time, and we used this method to crawl 100,000-level mini program samples. Using these samples, we evaluated MIPAD using a Random Forest as a classifier and X-means as a clusterizer, which showed an accuracy of 90.5% and an average sample time overhead of 15. 83s, demonstrating that MIPAD can detect cloned mini programs quickly and effectively.
Parallel Action Network Structure D3QN Based Channel Access and Power Control In DSA

Underlay dynamic spectrum access (DSA) has powerful effectiveness and efficiency for sharing radio spectrum among DSA users. But two fundamental issues have to be addressed: 1) avoiding harmful interference to Primary User (PU), 2) enabling effective transmission and coordinating interference with other Secondly User (SU). To solve the aforementioned issues, we propose a novel channel access and power control algorithm, which named Parallel Action Network Structure (PANS) Deep Double Dueling Q-Network (D3QN). Additionally, We enable SUs flexibly determine whether to perform single-channel transmission or have the opportunity for dual-channel transmission based on the current state. With the support of the two innovations mentioned above, SUs can independently select channels and control transmission power, further more improve their throughput.The numerous simulation results demonstrate the effectiveness and reliability of the proposed algorithm.
On applying artificial intelligence techniques to maximise passengers comfort and infrastructure reliability in urban railway systems

Railway infrastructure reliability and availability are key to guarantee an efficient rail service. In particular, track maintenance has a significant impact on the ride quality and train speed, since the reduction of speed limits is imposed in case of poor track condition. This implies consequence for the passengers comfort and delays of travel times. In this context, this research is aimed at developing an integrated model for the identification of track defects and the optimal ordering of maintenance interventions that can reduce operational costs for the infrastructure manager, improving capacity, efficiency as well as passengers’ satisfaction. The application of the proposed approach to a real urban railway line is described, showing its effectiveness.
Iterative learning-based model predictive control for mobile robots in space applications

This paper presents an iterative learning-based model predictive controller (MPC) for trajectory tracking control of an autonomous planetary rover on unknown terrain. In order to achieve accurate trajectory tracking under model uncertainties, a nonlinear controller and an MPC are utilized, combined with a learning-based uncertainties approximation. The model uncertainties and disturbances are learned using a deep neural network (DNN) as well as a parametric model and results are compared. For test and validation purposes, a gazebo simulation is used, which is itself already validated using data from a prototype rover. With that, the trajectory tracking performance of the proposed learning-based MPC is validated and compared to other well-performing controllers. The results show that the algorithm is able to learn model uncertainties and to compensate them during runtime while being practicable for the implementation and in the training phase.
Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot

This paper investigates the trajectory tracking control problem of omnidirectional mobile robot (OMR) with unknown parameters. A novel concurrent learning-based adaptive tracking control strategy is proposed. Different from currently available adaptive control schemes, the parameter estimation error in this paper can exponentially converge to zero under a mild rank condition, by resorting to concurrent learning technique. This implies that the identification of unknown parameters is achieved. Moreover, the trajectory tracking error can also tend to zero exponentially. Unlike some results with asymptotical convergence rate, the convergence rate of the tracking error can be improved in this paper. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.
A Clustering-Based Data Reduction for the Large Automotive Datasets

Large datasets used in automotive consist of a set of recorded sequences that represent possible road scenarios. Such scenarios are mainly utilized as test scenarios to verify developed driver assistance systems. Another application of the dataset is the training and verification of machine learning-based algorithms. As the number of possible road scenarios is, in fact, infinite, the process of selecting representative and meaningful sequences is a difficult and challenging task. This article presents an approach based on various clustering techniques for data reduction for large datasets that are used in the automotive industry to evaluate environmental perception algorithms. The approach is supported by the results obtained on representative datasets.
Delayed Recoil Control of Deepwater Drilling Riser Systems via A Non-Small Delay Approach

This paper considers the delayed recoil control problem of deepwater riser systems by a non-small delay approach. A velocity feedback controller with interval-like time-varying input delay is proposed to attain the suppression effect of the frictional disturbance on recoil responses. The main difference from the existing results lies in two aspects: a delayed static output feedback controller is designed with partially measurable velocity information, and a non-small delay method instead of small delay method is attempted for solving the control problem. Some interval-delay-dependent criteria of $H_{\infty}$ performance analysis and control design are derived. Simulation results are given to verify the effectiveness and advantages of the delayed recoil control method.
Markov Model for French-Wolof Text Analysis

Sentiment analysis is an essential task in natural language processing. It aims to automatically evaluate the sentiment polarity. Several methods and tools have been developed to address this issue which is more difficult in a bilingual context, such as the case of Senegal where the official French language is often mixed with Wolof the national language. [1] has been recently proposed as effective solutions for overcoming the complexities of this bilingual sentiment analysis. The lemmatizer-methods used there are based respectively on word-level similarity and the word-level trigrams (list of consecutive three letters) to convert inflectional forms and derivationally related forms of a word to a common base form. This morphological analysis approach is very complex and has some limitations for normalizing some words because French words are morphologically complex. Furthermore, the lemmatization of Wolof words is very difficult to implement because they are written using the French alphabet. In addition, these previous approaches use Bayes independence assumption to determine the sentiment of a sentence. This is another limitation when considering syntactic rules in a linguistic expression. To overcome these drawbacks and address the challenges and opportunities of bilingual sentiment analysis, we propose: (i) a French-Wolof dictionary and dictionary-based lemmatizer for better deal with verbs’ conjugation or some words’ declination in both languages, and (ii) a method for determining context dependent sentiment words based on Markov Model. Experimental conducted on a more larger data [2], shows that it successfully predicts sentiment polarity with an accuracy of 0.94 (implying an increase of 21% compared to the previous work).
Magnetic Fields and Minimum Safe Distances for UAS During Transformers Inspection

Nowadays, Unmanned Aerial Systems (UAS) play a crucial role in inspecting and maintaining powerline systems, such as transmission towers, power lines, transformers, etc. UAS operating in the vicinity of these high-voltage components are constantly affected by the emitted magnetic fields, such as large power transformers, which can impact the electronic components of the UAS, resulting in collisions, casualties, and injuries. Minimal work has been done to estimate these fields and the safe distances from transformers for UAS to operate. This paper uses three-dimensional (3D) finite element analysis to analyze the minimum safe operating distances for UAS around power transformers emitting low-frequency magnetic Fields. We Examine a three-phase 2500 kVA pole-mounted transformer and a bank of single-phase pole-mounted 20 kVA transformers using finite element analysis and provide analyzed data on the safe operational distances for UAS.
Design considerations on modular WPT charging systems for drones in civil applications

This paper provides design considerations for a wireless power transfer (WPT) charging system for drones in civil applications employing a modular paradigm referred to the Qi Specifications. Three different designs are developed and compared, under different possible mounting positions. The designs are analyzed by means of electromagnetic and circuit simulations to evaluate the main characteristics in terms of losses, weight, and other specific performance indicators discussed in the text. Particular attention is devoted to the evaluation of the possible interaction with the drone frame materials.
On-silicon Capacitor Sharing Technique Based on Package Power Merging for PDN Optimization

In this paper, the on-die decoupling capacitor (OPD) sharing from package technique is presented for power delivery network (PDN) design optimizations with next generation Field-Programmable Gate Array (FPGA). The paper mainly focuses on AC coupling noise mitigation technique among channels for transceiver power delivery design on large FPGA packages. With the proposed method, on-package decoupling capacitor (OPD) can be eliminated to enable small-size, low-cost packages without compromising package noise performance for transceiver power rails. Package layout schemes, power sharing techniques within different layers, and comparison among sharing within different layers to alleviate noise coupling between channels to mitigate voltage droop and overshoots are presented and analyzed in detail. The power integrity (PI) challenges lying with high-speed transceivers on FPGAs is analyzed and enablement solutions on package level are proposed to meet noise specifications without additional costs. An accurate noise simulation methodology and package routing optimization method in noise optimization is essential to an efficient system evaluation and demonstrated in this paper.
A pulse neural network reinforcement learning algorithm for partially observable Markov decision processes

This paper considers learning by a pulse neural network and proposes a new reinforcement learning algorithm focusing on the ability of pulse neuron elements to process time series. The conventional integrator neuron element is modeled in terms of the average firing rate of the biological neuron. But the pulse neuron is a modeling of the input– output relation of the time-series pulse (spike) and the decay of the internal state (internal potential). The application of such neural networks has been considered in recent engineering studies. It is known in particular that a pulse neuron with a high decay rate acts as a coincidence detector. The proposed model combines pulse neuron elements with different decay rates, which facilitates the processing of the time-series input information and the discrimination of fuzzy states in a partially observable Markov decision process. The proposed network is a four-layered feedforward network in which the pulse neuron elements forming the two hidden layers provide a pseudo-representation of the state in the environment. The elements generate a secondary reinforcement signal which results in learning similar to the conventional reinforcement scheme based on the state evaluation function. A computer experiment verifies that the proposed model works effectively in an environment which is strongly partially observable. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(3): 42–52, 2005; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/scj.10645
Design of 2x2 Multi-Band MIMO Antenna for Wireless Applications

In this paper, a novel 2x2 wideband multiple-input-multiple-output (MIMO) antenna is proposed. The novel design exhibits pattern diversity and displays pentaband characteristics over the multiple bands. "Pi-shaped" slot is etched on each radiating element resulting in complementary radiation patterns and improved bandwidth. Centralized staircase and "comb-like" structure is etched resulting in improved S parameters. Decoupling technique is used in the ground plane to enhance isolation among the antenna elements. The proposed MIMO antenna is designed using FR4-epoxy substrate of dimensions 0. 513λ*0. 292λ*0. 008λ. The novel antenna thus radiates at frequencies 1.60GHz, 2.31GHz, 2.92GHz, 3.43GHz and 4.65GHz. Observed inter element isolation is more than 11 dB with a peak value of 60dB. The proposed novel design has an operational bandwidth of 4.1 GHz and can be used for multiple wireless applications.
Design of Cavity-backed Coaxial Ring Slot Antenna for Dual-band Applications

This paper presents a coaxial ring slot antenna with a cavity backing using substrate-integrated waveguide (SIW) technology. This antenna consists of rectangular and circular SIW cavities each having a circular ring slot on the top conductor of the antenna. A coaxial feed is used to feed circular cavity which is inserted into the rectangular cavity fed with inset microstrip feed. With the slot antenna radiators size suitably optimized in the two cavities, a small frequency ratio is achieved between the two bands. The proposed antenna has been designed to radiate two different frequencies at 5.36GHz and 5.76GHz. Good radiation pattern characteristics are achieved which are unidirectional and have a stable gain of 3.6dBi at the resonance frequencies. Further, the antenna is easy to realize and is very compact. The measured results are found to be in good arrangement with simulation result.
Automated Close-Quarter, High-Resolution Inspection and 3D Reconstruction of Unknown Infrastructure

This paper presents a new automated workflow for 3D reconstruction of unknown infrastructure utilising UAS technology, developed within the EU funded RAPID project. A multi stage approach is used to produce highly accurate reconstructions of infrastructure, specifically unknown infrastructure. Using this approach, the drone operator is not required to perform extensive pre-flight surveying and mission planning, adding only a few high level inputs to the overall workflow. This benefits inspection workflows where the pilot is not onsite and cannot make visual observations on safety. Furthermore, this results in uniform high resolution reconstructions and a higher level of consistent and simple task loading for operator. Real-world environment tests consisting of using one of the most popular commercial UAS platforms to perform autonomous surveys was completed, with results to demonstrate the feasibility of the developed system.