Miracle-dz/UniFAR-FTUs huggingface dataset:
Geospatial Object Detection in Hyperspectral Imagery Using Spectral-Spatial Networks

By recording a wide range of spectral bands across the electromagnetic spectrum, hyperspectral imagery (HSI) delivers extensive information. Due to the complicated interaction between spectral and spatial properties, extracting meaningful geographical information from HSI is difficult. This article uses Spectral-Spatial Networks to detect geographical objects in hyperspectral data. The proposed deep learning system blends spectral and spatial information to improve object discrimination in complicated scenarios. SSN uses convolutional neural networks (CNNs) and spectral attention mechanisms to adaptively weigh spectral bands and capture spatial correlations. A large dataset of annotated hyperspectral photography with ground truth object identification trains the SSN. The collection includes different lighting, topography, and item scales. The object detection task is fine-tuned after pre-training on a large HSI dataset. Experimental results on benchmark hyperspectral datasets show that the SSN outperforms state-of-the-art approaches. Precision, recall, and F1-score for numerous object classes reveal that the suggested technique can distinguish objects in complicated and congested situations. The suggested SSN framework is also resilient to illumination, climatic conditions, and sensor properties, making it suitable for real-world applications. The SSN architecture's scalability is further examined, showing its suitability for heterogeneous HSI datasets with different spectral resolutions.
Ageing Studies of Mega Battery Packs for Grid Storage Applications Using Physics Based Modeling

The lower Levelized Cost of Electricity (LCOE) from wind and solar photovoltaics has enabled for greater integration of variable energy resources with energy storage tech-nologies such as larger centralized lithium-ion battery megapacks (with 1MWh total energy and 500V rating) to provide utility-scale services to grid operators. The daily cycling of standalone lithium-ion grid storage will reduce the battery cells' capacity due to several degradation mechanisms. A comprehensive physics-based Pythontm framework called Liionpack was developed to estimate these megapacks' remaining life and ageing. The study includes various degradation mechanisms coupled to the electrochemical-thermal model at the pack level. The effect of the inhomogeneities from cell-to-cell thermodynamic and kinetic properties for different working conditions including temperature and charge/discharge protocols on the ageing of a megapack are presented here.
Convolutional Neural Network (CNN) Technology to Detect Welding Defects in Motorcycle Frames using Transfer Learning and Optimizer

The motorcycle frame is a component that supports the performance and safety of the vehicle. The design and material of the frame are carefully chosen by manufacturers to achieve an optimal balance between strength, weight, and maneuverability. The most crucial part of frame manufacturing is the welding process as it can influence the strength of the frame. This research discusses the classification of welding defects with categories: Incomplete Fusion, No Defect, Spatter, and Void using Convolutional Neural Network (CNN). The dataset used consists of 4734 images trained using the GoogleNet architecture and RMSprop optimizer. The accuracy obtained in this research is 92.04%, which is then compared with 5 previous research references that used VGG19, AlexNet, and ResNet-50 architectures. The accuracy of this research is better than the 5 existing references, with a sufficiently high accuracy value. This study can provide an easy solution for the inspection process in welding within industrial environments.
Elevator Breakdown Prediction Using LSTM Analysis of Monitoring Data

Elevator malfunctions represent significant challenges in modern building infrastructures due to their resulting inconvenience and downtime, impacting transportation quality and reliability. To address these issues, the current study proposes to utilize Long Short-Term Memory (LSTM) networks to predict safety-related breakdowns that lead to immediate elevator stops. The methodology combines monitoring data analysis with LSTM networks to forecast elevator safety chain breakdowns. In response to the often-encountered problem of unbalanced datasets, a real balanced dataset is constructed using monitoring data from operating elevators worldwide. This dataset is formed using language modeling techniques, identifying behavioral patterns across various time horizons, and mapping these to a classification problem. Further steps involve setting temporal boundaries, embedding data within these boundaries, deploying an LSTM neural network, and subsequently fine-tuning hyperparameters. Experiment results indicate that an LSTM neural network can predict elevator safety chain breakdowns with an F1-score of 85 % on average across multiple time windows.
In the Era of Emerging Technologies: Discovering the Impact of Artificial Intelligence Capabilities on Timely Decision-Making and Business Performance

In the contemporary environment of rapidly advancing technologies, this study investigates the influence of artificial intelligence (AI) capabilities on business performance, focused on the era of emerging technologies. Furthermore, this study is to examine how the combination of artificial intelligence capability; the timely decisions influence overall business performance, and to provide insights into how these factors interact and contribute to enhancing organizational success in the context of modern AI-driven decision-making processes. The research employs a quantitative approach to analyze data collected from 184 upper echelons from diverse firms. The study revealed that businesses leveraging higher artificial intelligence capability often achieved improved timely decisions. However, the optimal balance between timely decision-making varied across contexts, influencing overall business performance outcomes. Understanding this interplay is crucial for organizations seeking to enhance their operational effectiveness and strategic outcomes. The general perspective of this study offers valuable insights for organizations aiming to strategically enhance operational efficiency and overall effectiveness in an AI-driven environment.
Design and Development of Garbage Classification System Based on Deep Learning

This paper aims to discuss the design and development of garbage classification system using deep learning, which can effectively and accurately classify garbage. The method based on transfer learning is used, because the pre-training model can extract the basic features of the image, which slows down the occurrence of over fitting to a certain extent. And it is transferred to the garbage image dataset for fine tuning training to improve the recognition accuracy. Additionally, a method combining threshold segmentation and edge detection has been adopted. The system uses MobileNetv2 as the core to build a lightweight feature extraction network, and optimize and improve its model, so as to finally achieve high-precision classification effect.
Fuzzy-Based Model Predictive Control for Bidirectional Charging of EV: An Adaptive Weighting Factor Algorithm

Model predictive control strategy is an advanced control algorithm for bidirectional active and reactive power flow in electric vehicles. However, tuning the weighting factor is a time-consuming process and its value is constant for various working conditions. To solve this problem, fuzzy control is applied to generate an adaptive weighting factor to meet all unexpected requirements. A two-stage bidirectional charger is built in Matlab/Simulink with the proposed fuzzy model predictive control. Simulation results show that dynamic performance can be achieved in the proposed method. The total harmonic distortion of the grid current is limited to 5%. The weighting factor can be adjusted online according to different demands. No tuning procedure of the weighting factor is required.
Localization and Tracking for LDR-UWB Systems

Localization and tracking (LT) algorithms for low data rate (LDR) ultra wideband (UWB) systems developed within the Integrated Project PULSERS Phase II are reviewed and compared. In particular, two localization algorithms, designed for static networks with mesh topologies, and one Tracking Algorithm, designed for dynamic network with star topologies are described and/or compared. Each of the localization algorithms adopts a different approach, namely, a centralized non-parametric weighted least squares approach (WLS), and a distributed Bayesian approach that relies on the cooperative maximization of the log-likelihood of range measurements (DMLL). The performance of these two alternatives are compared in a 3D indoor scenario under realistic ranging errors. The tracking algorithm is a fast non-parametric technique based on multidimensional scaling (MDS) and its performance is tested in a dynamic scenario. The proposed algorithms are practical and robust solutions addressing distinct network topologies and/or service requirements related to LDR-LT applications.
Opinion Mining of Heterogenous Social Media Reviews on the Proposed Adoption of eNaira

The Nigerian eNaira version launched recently was aimed at deploying digital currency services for the Nigerian monetary market. The opinion of Nigerians towards the adoption of the eNaira was conducted in this study. The methodology entails eNaira review acquisition from opinion mining reviews across heterogeneous social media platforms, text pre-processing, and sentiment analysis of tokenized review corpus. A dataset of 29505 unigram textual corpus was analysed to determine the positive, negative, and neutral sentiments expressed in their polarities. The pre-processing involved stop-word removal, stemming, lemmatization, Part-of-Speech tagging, entity tagging and tokenization. The opinion mining methodology deployed the Valence Aware Dictionary and Sentiment Reasoner (VADER) approach for the sentiment analysis of the eNaira tweets. The experimental results showed 56.25 %, 22.76% and 20.98% neutral, positive, and negative sentiments, respectively towards the adoption of the eNaira. The unigram analysis of the tokenized corpus likewise returns tokens including ‘app’, ‘bvn’ and ‘password’ as the most prominent unigram tokens while the bigram analysis returns (‘reset’, ‘password’) and (‘sent’, ‘email’) as the most prominent 2-word phrase with highest information gain. The result shows that eNaira enjoys favourable consideration for electronic commerce transaction.
Optimal Control of Quadruped Robot Using HQP-Based Virtual Model Control

This paper proposes a virtual model control framework based on hierarchical quadratic programming to achieve the optimal distribution of foot forces for a quadruped robot. The framework comprises the optimal force distribution of the support phase and acceleration adaptive virtual stiffness control of the swing phase. In the support phase, the optimal distribution of the foot force is transformed into a hierarchical quadratic programming problem. The goal is to achieve task priority coordination in different stages of the support phase. The mapping relationship between the ground reaction force on the foot and the virtual force on the torso is used for this purpose. In the swing phase, the virtual stiffness is adaptively adjusted to improve the trajectory tracking performance according to the acceleration demand of the foot trajectory tracking. Simulation experiments demonstrate that the robot performs trot and other gaits with significant stability improvement, and the results validate the feasibility and effectiveness of the proposed control framework.
Time-domain full waveform inversion using instantaneous phase information with damping

In time domain, the instantaneous phase can be obtained from the complex seismic trace using Hilbert transform. The instantaneous phase information has great potential in overcoming the local minima problem and improving the result of full waveform inversion. However, the phase wrapping problem, which comes from numerical calculation, prevents its application. In order to avoid the phase wrapping problem, we choose to use the exponential phase combined with the damping method, which gives instantaneous phase-based multi-stage inversion. We construct the objective functions based on the exponential instantaneous phase, and also derive the corresponding gradient operators. Conventional full waveform inversion and the instantaneous phase-based inversion are compared with numerical examples, which indicates that in the case without low frequency information in seismic data, our method is an effective and efficient approach for initial model construction for full waveform inversion.
Engaging local communities in stewardship of World Heritage

A new addition to the World Heritage Papers series provides guidance and introduces tools for best practice in engaging indigenous and local communities in World Heritage. Engaging Local Communities in Stewardship of World Heritage draws on over a decade of experience of the Community Management of Protected Areas for Conservation (COMPACT) initiative at eight natural World Heritage Sites in countries of Africa, Latin America, the Caribbean and Asia, and on emerging experience at two new sites.
Harmonising Chorales by Probabilistic Inference

We describe how we used a data set of chorale harmonisations composed by Johann Sebastian Bach to train Hidden Markov Models. Using a probabilistic framework allows us to create a harmonisation system which learns from examples, and which can compose new harmonisations. We make a quantitative comparison of our system's harmonisation performance against simpler models, and provide example harmonisations.
Aluminum to Aluminum bonding at room temperature

In this work we show that high density and very low pitches face to face Aluminum/Aluminum cold bonding is feasible when using Aluminum coated micro-tubes inserted into Aluminum pads. First, mechanical simulations by FEM show that the insertion pressure felt on the top edge of the Aluminum coated micro-tube is sufficient to break native aluminum oxide present at the interface and also insure the large plastic deformation of aluminum terminations necessary for good electrical and mechanical contact at the joint. Then an electrical demonstrator with over 106 connections at a 10 μm pitch is designed, assembled and characterized: the resistance values of the Al/Al bonds prove to be similar to those obtained with Au/Al bonds. Ion Beam cross-sections associated with SEM microscopy is used to characterize the morphology of the interface and shows that seamless Al/Al interfaces can be obtained if certain conditions are fulfilled. Finally the fundamentals of the cold insertion bonding process are discussed and a relationship established with “cold roll bonding” mechanisms, it is shown that the two techniques present some similarities related to very large plastic deformations of bonded materials. This helps us to develop a bonding theory to thoroughly explain the mechanism of the Aluminum/Aluminum bonds formation during the insertion process, as well as its kinematic scenario.
The Automatic Detection of Abusive Language in Dota 2 Chat Messages

This study addresses the pervasive issue of abusive language in online video game communication channels, focusing on Dota 2 chat messages. The aim was to employ diverse traditional machine learning algorithms and advanced deep learning architectures to identify and classify toxic and abusive language effectively. Leveraging TF-IDF, GloVe word embeddings, and self-trained embeddings, the research compared various classical machine learning models such as Naïve Bayes, Logistic Regression, and Support Vector Machine with convolutional and recurrent neural network models. The results revealed a consistent trend where deep learning models, particularly those employing GRUs and LSTMs, outperformed classical machine learning models. Experiments also demonstrated that self-trained embeddings generally outperformed GloVe embeddings in the domain of online video game chat messages.
AR Intelligent Real-time Method for Cultural Heritage Object Recognition

In a world of rapid technological changes and growing interest in cultural heritage, the development of innovative tools for travelers and history enthusiasts becomes incredibly important. Even in the digital age, interactive ways of exploring historical landmarks remain relevant and attractive. In this research, an AR Intelligent Real-time Method for Cultural Heritage Object Recognition has been developed. The method is implemented through a chatbot, which, when using a smartphone's camera, recognizes objects of cultural heritage and provides descriptions of these objects. The chatbot opens up new possibilities for travelers, allowing them to enjoy history and cultural heritage using intelligent tools and augmented reality.
High-endurance hybrid cache design in CMP architecture with cache partitioning and access-aware policy

In recent years, NVM (non-volatile memory) technologies, such as STT-RAM (spin transfer torque RAM) and PRAM (phase change RAM), have drawn a lot of attention due to their low leakage and high density. However, both NVMs suffer from high write latency and limited endurance problems. To overcome these problems, the SRAM/NVM hybrid cache architecture has been proposed, and the write pressure on NVM can be mitigated with appropriate write management policy. Moreover, many wear leveling techniques have been proposed to extend the lifetime of NVM in the hybrid cache. In this paper, we proposed a hybrid cache design that includes SRAM cache, STT-RAM cache, and STT-RAM/SRAM hybrid cache banks for CMP (chip multi-processors) architecture. We also propose a partition-level wear leveling scheme and access-aware policies to mitigate unbalanced wear-out of STT-RAM lines within a partition and among different cache partitions. Experimental results show that, our proposed scheme and policies can achieve an average of 89 times improvement in cache lifetime and are able to save 58% power consumption compared to SRAM cache.
Design and Simulation of a Compact, High Gain and High P1dB MMIC Based Low Noise Amplifier at V-Band

This paper presents the design and simulation of a I-Band MMIC based broadband low noise amplifier using a GaAs based pHEMT of 0.10 μm gate length. A 4-stage cascaded amplifier configuration has been chosen. Source inductive degeneration and feedback have been used simultaneously in order to achieve improved stability and gain flatness over the required bandwidth from 50 to 60 GHz. A flat gain of 23±1dB and a maximum noise figure of 3.2 dB have been achieved over the band within a chip size of 1.4 mm x 2.4 mm. The output 1dB compression point is also significantly high and is simulated to be 11.2 dBm. The 60-GHz band offers wide license-free bandwidth. The amplifier finds its application in point-to-point links for telecommunications back hauls, Wireless Personal Area Networks (WPAN) and Wireless HDMI.
A Vector Splicing-Based Multi-Source Heterogeneous Data Fusion For Quality Fluctuation Characterization

Industrial process data often presents a characteristic with a wide range of sources and diverse structures, which leads to the problem of quality fluctuation characterizing and accurately modeling. This paper proposes a vector splicing-based multi-source heterogeneous data fusion for quality fluctuation characterization method. Firstly, the gray level co-occurrence matrix (GLCM) is used to calculate the feature parameters of image data information to construct the image feature vector. Then, vector concatenation technology is used to combine structured text data with image data to achieve heterogeneous data feature fusion and quality fluctuation characterization. Finally, a case study is conducted to evaluate the classification accuracy of the proposed heterogeneous data fusion method for different operating conditions, result reveals that proposed method shows a better performance compared to single structured-quality analysis method.
Correlation-based frequency warping for voice conversion

Frequency warping (FW) based voice conversion aims to modify the frequency axis of source spectra towards that of the target. In previous works, the optimal warping function was calculated by minimizing the spectral distance of converted and target spectra without considering the spectral shape. Nevertheless, speaker timbre and identity greatly depend on vocal tract peaks and valleys of spectrum. In this paper, we propose a method to define the warping function by maximizing the correlation between the converted and target spectra. Different from the conventional warping methods, the correlation-based optimization is not determined by the magnitude of the spectra. Instead, both spectral peaks and valleys are considered in the optimization process, which also improves the performance of amplitude scaling. Experiments were conducted on VOICES database, and the results show that after amplitude scaling our proposed method reduced the mel-spectral distortion from 5.85 dB to 5.60 dB. The subjective listening tests also confirmed the effectiveness of the proposed method.
Real-Time Human Activity Classification Using Gait Cycle Averaging and Biometric Heuristics

The classification of human activities in real-time is an essential task of Human Activity Recognition (HAR). For the deployment of HAR systems on devices like smartphones and smartwatches, it is crucial to ensure their efficiency in terms of time and space. Within the realm of human activity classification, a specific focus lies on the categorization of ambulatory activities, including walking, jogging, and ascending and descending stairs. Activity shapelets, which are geometric patterns representing the dominant pattern in ambulation, are extracted and used for accurate and efficient classification of an incoming time series signal from mobile devices. A trade-off must be made between an extensive training period for extracting shapelets tailored to each individual and the deployment of shapelets trained on a broader population at the expense of accuracy. We propose a novel approach for activity shapelet creation using gait cycle averaging, coupled with a method to partition subjects into training clusters based on biometric similarity. A systematic improvement in accuracy is shown when classifying activity data by leveraging biometric partitioning compared to randomly assigned training clusters. Our findings demonstrate that our methods can be used to deploy pre-trained shapelet libraries, eliminating the need for expensive individual training while maintaining high accuracy.