Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous rover guidance and localization by measuring the peak of a tall landmark

This paper handles locating an autonomous rover whose mission is to explore the unknown outdoor environments, while localizing itself. For localization, the rover occasionally measures the peak of a tall landmark which is known a priori. For the peak measurement, the rover utilizes a monocular camera which is stabilized utilizing a gimbal system. In addition, the rover uses object detection methods to detect a landmark in its camera view. We assume that the height of the landmark is known a priori. Also, we assume that the rover has an altimeter to measure its altitude. Under these assumptions, we present a method to make the rover estimate the relative position of the landmark. Suppose that the rover's target location is set, so that the rover needs to visit the target while avoiding collision with obstacles. Due to environmental disturbance, the rover may not maintain its desired course while it moves. We thus address a guidance control to make the rover approach the target, while compensating the environmental disturbance at the rover's position. As far as we know, this paper is novel in addressing the rover's guidance and localization by measuring the peak of a tall landmark which is known a priori. Furthermore, this paper is novel in utilizing gimbal camera for localization. Thus, we do not require heavy computational load caused by calculating the 6‐DOF transform between the camera and the rover's pose. The effectiveness of the proposed approach is verified utilizing MATLAB simulations.
A molecular calculation of electronic properties of layered crystals. II. Periodic small cluster calculation for graphite and boron nitride

Small (18-32 atoms) periodic clusters of two-dimensional hexagonal graphite and boron nitride are shown to represent some high-symmetry points in the Brillouin zone of the infinite crystal. Semi-empirical all-valence electron calculations are performed on these clusters and the binding energy, work function, bandwidth, band-to-band transition energy, band gap, charges and equilibrium distances are computed and compared with values obtained by tight binding and truncated crystal calculations. Favourable agreement with experimental data is obtained with selfconsistent calculations on these clusters.
A Note on Noisy Chaos

SUMMARY We prove that, under appropriate conditions, in a noisy environment an embedded deterministic dynamical system which admits a compact attractor can give rise to an ergodic stochastic system. This observation justifies the stochastic set-up in the study of deterministic chaos. We also clarify a folklore concerning polynomial autoregression. chaos and its relationship to statistical non-linear time series modelling. Loosely, a chaotic system may be defined as a dynamical system such that, starting from points over a certain region, all trajectories (solutions) are uniformly bounded but nearby trajectories initially diverge from each other exponentially and yet the trajec- tories have identical statistical properties. A trajectory obtained from a chaotic deterministic system thus looks like a realization from some random process. The theory of chaos casts the hope that an erratic time series could be fitted with some non-linear model so that highly accurate short-term forecasts may be available. The essential part of the theory of chaos consists of quantifying the chaoticness of a
Expansive Billiard flows

We consider polygonal billiards on surfaces of constant curvature. We show that the usual phase space of the billiard flow can be extended to a compact three dimensional manifold without boundary that is a circle bundle. We extend the dynamics $C^\infty$ to the manifold defined. We relate the expansiveness of the billiard flow with the existence of periodic orbits and with the injectivity of the itinerary map for billiard tables in the hyperbolic disc, the Euclidean plane and the sphere.
Multiple Crossover and Mutation Operators Enabled Genetic Algorithm for Non-slicing VLSI Floorplanning

Floorplanning is a crucial process in the early stage of VLSI physical design. It determines the performance, reliability, and size of chips. B*-tree is a simple yet efficient representation that encodes the layout of modules in a compact and non-slicing structure. Several B*-tree variants and corresponding operators have been proposed to deal with non-slicing floorplanning. However, these operators are considered and applied individually. A collective manipulation of them remains missing. This study proposes a genetic algorithm (GA) that enables multiple crossover and mutation operators for solving the non-slicing floorplanning problem. In particular, the GA selects one crossover operator and one mutation operator from the pool of operators whenever reproducing an offspring. The probability for an operator to be selected is based on its empirical performance. This study conducts experiments on two well-known benchmarks to examine the effectiveness of the proposed method. The experimental results show that the GA can achieve superior solution quality and efficiency on the non-slicing VLSI floorplanning.
R2-HMEWO: Hybrid multi-objective evolutionary algorithm based on the Equilibrium Optimizer and Whale Optimization Algorithm

Multi-objective evolutionary algorithms can be categorized into three basic groups: domination-based, decomposition-based, and indicator-based algorithms. Hybrid multi-objective evolutionary algorithms, which combine algorithms from these groups, are gaining increased popularity in recent years. This is because hybrid algorithms can compensate for the drawbacks of the basic algorithms by adding different operators and structures that complement each other. This paper introduces a hybrid-multi objective evolutionary algorithm (R2-HMEWO) that applies hybridization in the form of structure and operators. R2-HMEWO is based on the whale optimization algorithm (WOA) and equilibrium optimizer (EO). Elite individuals of WOA and EO are selected from a repository based on the R2-indicator and shifted density estimation-based method. In order to improve solutions' diversity, a reference points method is devised to select next-generation individuals. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems (ZDT, DTLZ, and CEC009) and compared with six state-of-the-art (SOTA) algorithms (NSGA-III, NSGA-II, MOEA/D, MOMBI-II, MOEA/IGD-NS, and dMOPSO). Based on the inverted generational distance (IGD) metric (mean of 25 independent runs), R2-HMEWO outperformed other algorithms on 14 out of 19 test problems and revealed a highly competitive performance on the other test problems. Also, R2-HMEWO performed statistically significant better than MOEA/D and dMOPSO in 15/19 and 14/19 test problems, respectively ($p < 0.05$), and reached significant performance in 4 test problems (from ZDT and CEC09) compared to other algorithms.
Hybridizing A Genetic Algorithm With Reinforcement Learning for Automated Design of Genetic Algorithms

The automated design of optimization techniques holds great promise for advancing state-of-the-art optimization techniques and it has already taken over the manual design by human experts in some problems. Genetic algorithms are one of the key approaches for tackling the automated design problem. Unfortunately, these algorithms may take several hours to run as the fitness evaluation involves solving some benchmark instances to determine the quality of a candidate configuration. In this paper, we hybridize a meta-genetic algorithm with reinforcement learning to automatically design genetic algorithms for the two-dimensional bin packing problem. The task of the meta-genetic algorithm is to search the configuration space of genetic algorithms and the task of reinforcement learning is to decide whether to evaluate a candidate configuration or not. Therefore, avoiding wasting the computational budget on poor configurations. The proposed hybrid and the meta-genetic algorithm without reinforcement learning produce solvers for the two-dimensional bin packing problem that are competitive with the state-of-the-art algorithms. However, the proposed hybrid consumes about 25% of the computational effort required by the meta-genetic algorithm without reinforcement learning.
Design of The Humanoid Biped Robot based on Luby Controller

This article introduces the hardware and software design of a humanoid biped robot based on Luby controller with “robot competition in Anhui province” humanoid sprint project as the background. Design a humanoid biped robot. The Luby controller is implemented by the STM32 chip, which was adopted to realize process control, through the pace planning, speed adjustment, sensor detection to complete the robot’s humanoid sprint action task. After the actual competition, it was verified that the biped robot has certain practicability and stability.
BCH-SPC based Cubic Turbo Product Coding

In this paper, we propose a cubic Turbo Product code (TPC) with the Bose-Chaudhuri-Hocquenghem (BCH) code and the single parity-check (SPC) code as components, as well as an improved turbo-oriented adaptive belief propagation decoding based on the partial layered and row-column weight scheduling (PL-RC-TAB). The proposed coding scheme has comparable encoding and decoding complexities with the two dimensional (2D) TPC based on the classical turbo-oriented adaptive belief propagation (TAB) decoding, but can achieve faster decoding convergence rate and improved error correction performance.
DMC-GRASP: A Continuous GRASP hybridized with Data Mining

The hybridization of metaheuristics with data mining techniques has been successfully applied to combinatorial optimization problems. Examples of this type of strategy are DM-GRASP and MDM-GRASP, hybrid versions of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, which incorporate data mining techniques. This type of hybrid method is called Data-Driven Metaheuristics and aims at extracting useful knowledge from the data generated by metaheuristics in their search process. Despite success in combinatorial problems like the set packing problem and maximum diversity problem, proposals of this type to solve continuous optimization problems are still scarce in the literature. This work presents a data mining hybrid version of C-GRASP, an adaptation of GRASP for problems with continuous variables. We call this new version DMC-GRASP, which identifies patterns in high-quality solutions and generates new solutions guided by these patterns. We performed computational experiments with DMC-GRASP on a set of well-known mathematical benchmark functions, and the results showed that metaheuristics for continuous optimization could also benefit from using patterns to guide the search for better solutions.
Joining mechanisms and mechanical properties of PA composites obtained by selective laser sintering

Purpose – Additive manufacturing is today a viable industrial solution alongside traditional processes. Techniques like selective laser sintering (SLS) address the issues of digital production and mass customization in a variety of materials. Composite parts can be obtained with specific functional and mechanical properties. Building orientation during additive manufacturing often causes anisotropy of parts' properties that is still unspecified in technical information. The purpose of this paper is to investigate the mechanical performances and failure mechanisms of an aluminium‐filled polyamide and of a new alumina‐polyamide composite produced by SLS, in comparison with unfilled PA.Design/methodology/approach – A specific focus is set on the evaluation of primary and secondary anisotropy in the case of metal or ceramic filler, as well as on the specific contribution of powder distribution modes and joining phenomena. Macroscopic mechanical tests and the observation of joining and failure micro‐mechanisms.
Experimental Benchmarking of Next-Gen Indoor Positioning Technologies (Unmodulated) Visible Light Positioning and Ultra-Wideband

Within the context of the Internet of Things (IoT), many applications require high-quality positioning services. As opposed to traditional technologies, the two most recent positioning solutions: 1) ultra-wideband (UWB) and 2) (unmodulated) visible light positioning [(u)VLP] are well suited to economically supply centimeter-to-decimeter level accuracy. This manuscript benchmarks the 2-D positioning performance of an 8-anchor asymmetric double-sided two-way ranging (aSDS-TWR) UWB system and a 15-LED frequency-division multiple access (FDMA) received signal strength (RSS) (u)VLP system in terms of feasibility and accuracy. With extensive experimental data, collected at two heights in a 8 m by 6 m open zone equipped with a precise ground-truth system, it is demonstrated that both visible light positioning (VLP) and UWB already attain median and 90th percentile positioning errors in the order of 5 and 10 cm in line-of-sight (LOS) conditions. An approximately 20-cm median accuracy can be obtained with uVLP, whose main benefit is it being infrastructureless and thus very inexpensive. The accuracy degradation effects of non-LOS (NLOS) on UWB/(u)VLP are highlighted with four scenarios, each consisting of a different configuration of metallic closets. For the considered setup, in 2-D and with minimal tilt of the object to be tracked, VLP outscores UWB in NLOS conditions, while for LOS scenarios similar results are obtained.
A conjugated evolutionary algorithm for hyperparameter optimization

With the recent upsurge in the use of deep learning and other computationally expensive machine learning models, hyperparameter optimization has become a quite important and widely researched area of study. Genetic algorithms, a subclass of evolutionary algorithms, have proven to be an effective approach and have been widely used in recent years. However, efficiently exploring the domain of possible solutions remains a challenging, and often computationally-expensive task. In this paper, we present a novel and efficient hyperparameter optimization strategy based on a genetic algorithms variant: Biased Random-key Genetic Algorithms (BRKGA). One of the main challenges of BRKGA is its limited capacity to explore the domain surrounding a particular individual. Although good genes will be preserved by its bias property, these genes are copied as they are, and even if a better solution exists in the close neighborhood of a particular gene it might never be explored. We tackle this problem by adding an exploitation component at the end of every evolutionary step, further exploring the hyperparameter domain. Several computational experiments on eight different publicly available datasets were performed to assess the effectiveness of the proposed approach and to prove it is a significant improvement over its predecessor. The results show that our proposed method outperforms, in terms of the $F_{1}$ score of the resulting Artificial Neural Network, not only BRKGA but also other commonly used methods in most of the test cases.
Solving the Point Feature Cartographic Label Placement problem using Jaccard index as a measure of labels intersection

This paper presents a new approach to solve the Point Feature Cartographic Label Placement (PFCLP) problem. The PFCLP is a relevant problem for Geographic Information Systems, where the objective is to position labels on a map avoiding overlaps to improve legibility. The first techniques proposed to solve this problem considered only the presence/absence of over-lapping, while more recent ones introduced the idea of estimating the intensity of such overlap. This work extends previous works by considering the overlap intensity through the Jaccard index. Traditional mathematical models for PFCLP were compared to the proposed strategy showing that the Jaccard index provided the best results in terms of legibility. A Clustering Search (CS) algorithm was also proposed to compare the performance of our strategy on a set of large-sized instances. The experimental results show good solutions for instances with up to 13206 points.
Investigation of The Impact of Routing Protocol in Green IoT with Netsim

Technology evolution nowadays moves vigorously to improve daily human life and directly affect the earth’s resource consumption. Reducing energy consumption leads to reducing carbon dioxide (CO2) emission to the surrounding, which can cause environmental and health issues. The balance of fast data transmission with efficient energy consumption is vital for achieving green IoT. One part of the IoT system, the wireless sensor, performs a duty to operate continuously without data delay. That is why most wireless sensors only collect raw data and do packet forwarding, where later it will be analyzed by another physical device. Although the sensors still need to recognize the routing topology by themselves, the energy consumed to forward the packet to its destination can be optimized. Researchers have used various ways to control energy consumption using different hardware and power management, so this research wants to focus on how using different routing protocols can impact energy consumption.
Diagnostics of Magnetron Transmitter System using Thermal Camera and Neural Network

The magnetron transmitter system is one of the most important components of weather radar. Monitoring the condition of the system is crucial to ensure the radar is operating normally. In this study, the temperature monitoring of the magnetron transmitter system was carried out. Thermal camera MLX90640 accompanied by NodeMCU ESP32 microcontroller provides a temperature matrix, which can represent the overall temperature of the radar system. The diagnosis system is built based on a neural network consisting of a hidden layer, and an output layer that classifies six system conditions. The results of this study showed that the thermal camera can measure the temperature of the magnetron transmitter system. This system can obtain 100% accuracy in identifying the conditions of the magnetron transmitter system, namely magnetron on, magnetron off, radar off, modulator power supply overheat, switch array unit overheat, and signal processing faulty. This innovation is expected to be early detection of an anomaly that occurs in the magnetron transmitter system so that it can minimize downtime to make repairs due to the unpreparedness of spare parts.
A multifactorial differential evolution with hybrid global and local search strategies

Evolutionary multitasking optimization (EMTO) solves multiple optimization tasks meanwhile in the framework of evolutionary algorithm, aiming at improving the solving performance on each task via knowledge transfer among tasks. As one of the representative EMTO algorithms, multifactorial evolutionary algorithm (MFEA) has attracted great attention and has been used to solve many optimization problems. However, most of MFEAs tend to suffer from premature convergence. To deal with this issue, this article designs a novel MFEA by integrating differential evolution, and a hybrid of global and local search strategies, named MFDE-GLS for short. Particularly, the global search strategy is based on an opposition-based learning and a Gaussian perturbation to improve the search ability and maintain population diversity. A local search strategy is introduced by combining 1-dimension search and n-dimension search to accelerate the convergence. Moreover, a new environmental selection mechanism is developed to keep the elite individuals while maintaining the population diversity based on the affinity propagation clustering method. Comprehensive experiments were conducted on both single-objective and multi-objective multi-task benchmark problems to show the effectiveness of the proposed algorithm.
Malaria detection based on ResNet + CBAM attention mechanism

Aiming at the low accuracy and time-consuming training of malaria detection, this paper proposes a malaria detection algorithm based on ResNet+CBAM attention mechanism. In the ResNet-40 model, which reduces the number of network layers and network width, the CBAM attention mechanism module is added and trained on the malaria dataset (Malaria dataset). The experimental results show that the detection method proposed in this paper improves the classification accuracy by 1% on the original basis.
Massive Wireless Access Enhancement Based on Self-Similarity of Fractal Channels in Multiscale Space

Massive Internet of Things (IoT) is an important application scenario for the next-generation mobile communication systems. The channel estimation is crucial for the performance of massive wireless access in IoT. However, when the number of terminals is large, the huge pilot overhead may seriously increase the burden of the wireless access system. Therefore, reducing the pilot overhead while ensuring the accuracy of channel estimation is an important challenge for massive wireless access systems. In this article, we propose a massive wireless access mechanism, which greatly reduces the pilot overhead and improve the energy efficiency (EE) of terminals. Based on the self-similarity of fractal channels, the channel-state information (CSI) of a subset of terminals is sampled to estimate the CSI of all terminals, thereby reducing the pilot overhead and solving the shortage of pilot resources in massive wireless access systems. Meanwhile, the optimal transmission power of terminals based on CSI can save the energy consumption of terminals in IoT. Compared with the traditional algorithm, simulation results indicate that the proposed massive wireless access mechanism improves the EE of terminal by 340% and reduces 70% of the pilot overhead.
Parametric S-Parameters for PCB based Power Delivery Network Design Using Machine Learning

In this contribution, a methodology using ANN techniques is presented for the analysis and design of power delivery network (PDN) in printed circuit board (PCB) design. The trained artificial neural networks (ANNs) are applied to answer relevant PDN design questions such as PCB resonance frequency and target impedance (TI) violations. Based on PCB geometry and material variations a PDN design space is defined. To train the ANNs, inside the design space a sparse sampling with 10000 physics-based (PB) simulations is performed. The S-parameter database is created using physics based via models which are validated by a commercial full-wave finite element method (FEM) solver in the frequency spectrum of 1MHz to 1GHz. The S-parameters are available in the SI/PI-Database. The flexibility of the unterminated S-parameters is demonstrated by a post processing termination using decoupling capacitor (decap) distributions. Limitations of the data generation are discussed with respect to computational resources and disk space.