Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Power Lane Detection Unit With Sliding-Based Parallel Segment Detection Accelerator for FPGA

Recently, with the development of semiconductors and VLSI (Very Large Scale Integrated Circuit), the technology required for autonomous driving is rapidly developing. One of the technologies that cannot be left out is the lane detection function. Lane recognition requires a lot of data from the camera sensor. As a result, the data size increases, making it difficult to process on a lightweight embedded board. This paper proposes a sliding-based parallel segment image processing method to solve this problem. Most boards in autonomous vehicles are lightweight, so the technique has been designed to reduce computation and power consumption. After fetching the image’s pixel data, grayscale conversion, Gaussian smoothing, Sobel operator, non-maximum suppression, and hysteresis are performed in parallel. Lanes were detected by performing a Hough transform operation on an image for which edge detection was completed in parallel. Due to the nature of parallel processing, it is more effective when image input is continuous and numerous than single image processing. This algorithm is written in C language and VHDL (VHSIC Hardware Description Language) for two parts in the board, DE1-SoC, FPGA (Field Programmable Gate Array) and HPS (Hard Processor System. Due to the use of the C language and VHDL, parallel programming uses 3.1 times less time, twice as much memory and slightly more power than sequential programming. For hardware languages such as Verilog, the computation algorithms have been converted to a fixed point. When comparing HPS and FPGA, the FPGA consumed significantly fewer resources, with 18 times shorter run time, 50 times fewer clock cycles, 3 times less power, and 183 times less energy. This provides a substantial benefit.
Implementing Torque Control-Based Biped Walking of Humanoid Robots with High Reduction Gear and No Joint Torque Feedback

Most adult-sized bipedal and humanoid robots primarily use high-reduction gears to achieve high torque output of their joints. However, these high-reduction gears have problems such as high joint friction and low backdrivability, which can make accurate torque control difficult and potentially adversely impact system stability. Hence, most robots attempt to overcome these problems related to reduction gears by using torque sensors. In this study, we demonstrated the feasibility of torque-controlled walking without joint torque sensors with the bipedal robot RoK-3, which is equipped with high-reduction gears known as Harmonic Drive. The torque command generation adopts an integrated approach [1], [2] of model predictive control (MPC) and whole-body control (WBC), and a friction compensator is introduced to compensate for high joint friction. The experiments include balancing on a moving slope, up and down motions during a single foot support, and walking in place scenarios. Finally, we have demonstrated the implementation of torque control for robots using high-reduction gears and the feasibility of walking based on torque control, even without the use of torque sensors.
Optimizing Gossiping for Asynchronous Fault-Prone IoT Networks With Memory and Battery Constraints

A gossip protocol is a procedure by which a device disseminates its rumor to all devices on a network. The traditional definition of the gossip problem states that, in a system with $n$ interconnected devices where each of them only knows initially its own rumor, after a known finite time of message exchange, all devices learn the $n$ rumors. This definition considers synchronous systems where devices cannot fail. Gossip-based protocols can be very useful in Internet of Things (IoT) networks as a means of disseminating information. Thus, we have reformulated the problem to expand the spectrum of networks in which the gossip technique can be applied, including asynchrony and device failures, very common features in current IoT newtwoks. Gossiping consumes valuable memory and battery resources in devices. This can be particularly problematic for small IoT devices that operate with reduced memory in remote or inaccessible locations, where battery replacement or recharging is difficult. To address these issues, two efficient gossip protocols are presented, so that the efficiency and longevity of IoT devices is not compromised. The first protocol (battery-efficient protocol) allows to reduce battery consumption while still having a good time performance, but needs quadratic memory, like traditional versions. The second protocol (memory-optimal protocol) only needs linear memory to store and manage the rumors, and is capable of significantly reducing the number of messages sent, while obtaining similar (or, in many cases, better) performance than the first protocol. Both protocols are formally proved to be correct, and upper and lower limits of their number of messages sent are determined. These theoretical limits, however, differ so much among them that they do not permit to guess the number of messages that a real case may require. Hence, the practical performance of both protocols is assessed with experiments.
A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

With the expected colonization of human daily life by artificial intelligence, including in industry productivity, the deployment of Industry 4.0 (I4) in the food agriculture industry (FAI) is expected to revolutionize and galvanize food production to increase the efficiency of the industry’s production and to match, in tandem, a country’s gross domestic productivity. Based on a literature review, there have been almost no direct relationships between the I4—Food-Agriculture (I4FA) Nexus and the agroecosystem. This study aimed to evaluate the state-of-the-art relationships between the I4FA Nexus and the agroecosystem and to discuss the challenges in the sustainable FAI that can be assisted by the I4 technologies. This objective was fulfilled by (a) reviewing all the relevant publications and (b) drawing a conceptual relationship between the I4FA Nexus and the agroecosystem, in which the I4FA Nexus is categorized into socio-economic and environmental (SEE) perspectives. Four points are highlighted in the present review. First, I4 technology is projected to grow in the agricultural and food sectors today and in the future. Second, food agriculture output may benefit from I4 by considering the SEE benefits. Third, implementing I4 is a challenging journey for the sustainable FAI, especially for the small to medium enterprises (SMEs). Fourth, environmental, social, and governance (ESG) principles can help to manage I4’s implementation in agriculture and food. The advantages of I4 deployment include (a) social benefits like increased occupational safety, workers’ health, and food quality, security, and safety; (b) economic benefits, like using sensors to reduce agricultural food production costs, and the food supply chain; and (c) environmental benefits like reducing chemical leaching and fertilizer use. However, more studies are needed to address social adaptability, trust, privacy, and economic income uncertainty, especially in SMEs or in businesses or nations with lower resources; this will require time for adaptation to make the transition away from human ecology. For agriculture to be ESG-sustainable, the deployment of I4FA could be an answer with the support of an open-minded dialogue platform with ESG-minded leaders to complement sustainable agroecosystems on a global scale.
A novel Traffic Sign Dataset with Condition Annotations

To develop robust and secure automated transportation systems, Traffic Sign Detection and Recognition (TSDR) is a key part. It plays a crucial role in Advanced Driver Assistance Systems (ADAS), self-driving vehicles and traffic safety. However, the task of TSDR can be challenging due to traffic signs being subject to damages, discoloration, vandalism and occlusion. Even though a lot of progress is made in both research areas of Traffic Sign Detection (TSD) and Traffic Sign Recognition (TSR), no study explicitly deals with the problem of qualitative poor traffic signs appearing in real-world scenarios. This can be assigned to the lack of an extensive traffic sign dataset containing flawless signs as well as imperfect signs. Neural networks trained exclusively on untainted data might fail at detecting flawed signs as they occur in real-world scenarios. Therefore, in this paper, a novel traffic sign dataset with condition annotations is proposed, indicating if a sign is good, discolored, vandalized, dirty or occluded. The custom dataset is created with a semi-supervised approach, in which machine learning models are trained to classify traffic signs in the condition categories. The resulting dataset can be used as basis for more precise traffic sign recognition as well as traffic sign condition classification which can be useful for maintenance planning. The dataset includes approx. 20.000 images of 10 sign classes, where 70% of data is incorporated in the training set, 10% in the validation set and 20% in the test set.
Fast imaging model of periodic patterns in plasmonic lithography

As a new and alternative lithography technology, plasmonic lithography can break through the diffraction limit of traditional lithography by exciting the surface plasmon polaritons (SPPs) to make the evanescent wave at the mask participate in imaging. The photoresist aerial image distribution of different mask patterns can be calculated by establishing an imaging model, which is the basis for understanding and further optimizing imaging. Based on the idea of machine learning and linear fitting, a fast imaging model for plasmonic lithography is established, including periodic rectangular hole pattern. Compared with the rigorous numerical method, the fast imaging model can greatly improve the calculation speed with high accuracy, which creates conditions for the development of computational lithography technology.
Informed Clustering for Encounter Type Categorization Based on AIS Data

Maritime situational awareness is crucial for on-board decision-making, particularly during encounter scenarios. To enhance situation awareness, the understanding of ship encounters is necessary. Given the importance of ship encounter classification, this paper presents a novel method to categorize encounter types using Automatic Identification System (AIS) data. Within this study, encounter types are categorized based on expertise. Then, the data preprocessing and influencing parameters selection are introduced to assist encounter type classification. Subsequently, the K-Shape, and K-Means with Euclidean and Dynamic Time Warping (DTW) distances, are applied for ship encounter classification. The determination of the cluster number is guided by the characterizations of encounter types. Experiments are conducted by utilizing real AIS data from ships navigating through Oslofjord, Norway. The classification results on the training dataset achieve a purity of up to 0.95, and a case study on the testing data underscores the effectiveness of the proposed approach.
Critical Analysis of Machine Learning Technique for Discriminating the Similar Vegetation Pattern

Although the existing literature has classified the landcover by different techniques for change detection, the classified images obtained by different methods still deserve further discussion. For this reason, by analyzing Machine Learning technique, this study offers a comparison between the Tall vegetation (TV) change in Roorkee region of India due to Highway construction during the period of 2017–2018. In literature researchers attempted the optical and synthetic aperture radar (SAR) image classification with varying degrees of success. The challenge in this type of classifications is to identify urban and dense vegetation (tall vegetation) classes separately. Both these classes have very small amount of variations in the scatter power. A Random forest (RF) algorithm is trained on the combination for efficient classification for urban, dense vegetation and other classes. Random forest classification algorithm is belonging to the ensemble learning category, is the one of the best machine learning algorithms. It combines the predictions generated from multiple decision trees to make accurate predictions or classify data points. In this study the number of tall vegetation pixels decreased from 1977 to 31 due to construction of Highway and tall vegetation removal at other study regions taken at different stretches of Highway, results in significant decrease of tall vegetation area.
Quantitative Quality Assessment for EEG Data: A Mini Review

Electroencephalography (EEG) is an essential neuromonitoring modality, deeply integrated across scientific disciplines such as psychology, cognitive science, computational neuroscience, neurology, and psychiatry. Its relevance has surged with the rise of brain-computer interfaces. However, the potential of non-invasive EEG is hindered by compromised signal quality compared to invasive methods. The distinction between the modest EEG source amplitudes and the pronounced magnitudes of non-EEG physiological signals and environmental interferences complicates the analysis. The coexistence of subtle neural signals and prominent artifacts, both intrinsic and acquired, characterizes EEG signal processing. Various artifact management techniques have been proposed, yet the pursuit of EEG signal quality assessment remains underexplored. This mini-review addresses this gap by emphasizing the vital role of quality assessment in EEG recordings. The article highlights the significance of rigorous signal evaluation, emphasizing reliable EEG data. It also encapsulates evolving quantitative methodologies that bolster signal fidelity assessment. By delving into these aspects, the article presents a compact overview of ongoing advancements in quantitative EEG quality assessment techniques in the research field of EEG analysis and applications.
Pooled-parent exome sequencing to prioritise de novo variants in genetic disease

In the clinical setting, exome sequencing has become standard-of-care in diagnosing rare genetic disorders, however many patients remain unsolved. Trio sequencing has been demonstrated to produce a higher diagnostic yield than singleton (proband-only) sequencing. Parental sequencing is especially useful when a disease is suspected to be caused by a de novo variant in the proband, because parental data provide a strong filter for the majority of variants that are shared by the proband and their parents. However the additional cost of sequencing the parents makes the trio strategy uneconomical for many clinical situations. With two thirds of the sequencing budget being spent on parents, these are funds that could be used to sequence more probands. For this reason many clinics are reluctant to sequence parents. Here we propose a pooled-parent strategy for exome sequencing of individuals with likely de novo disease. In this strategy, DNA from all the parents of a cohort of unrelated probands is pooled together into a single exome capture and sequencing run. Variants called in the proband can then be filtered if they are also found in the parent pool, resulting in a shorter list of prioritised variants. To evaluate the pooled-parent strategy we performed a series of simulations by combining reads from individual exomes to imitate sample pooling. We assessed the recall and false positive rate and investigated the trade-off between pool size and recall rate. We compared the performance of GATK HaplotypeCaller individual and joint calling, and FreeBayes to genotype pooled samples. Finally, we applied a pooled-parent strategy to a set of real unsolved cases and showed that the parent pool is a powerful filter that is complementary to other commonly used variant filters such as population variant frequencies.
Two-Dimensional Circular Signal Systems

In this article, a two-dimensional signal system with amplitude-phase modulation is considered, for which elementary signals are placed on concentric circles. The procedures for constructing such a system are presented and their simplified receiver is proposed. In this case, the circle to which the signal may belong is first determined, and then, the detection procedure is carried out on this circle. New signals are constructed, including those for simplified receivers and simulation results are presented for a scheme with one input and multiple outputs, in which fading is represented by the Nakagami-m process.
Enhancing Gesture Recognition for Musical Conducting: A Study on Diverse Data Classification and Stacked Neural Network Architectures

This study addresses the limitations of many gesture recognition algorithms, which predominantly employ machine learning-based approaches tailored to specific types of gestures, leaving niche gestures such as musical conducting gestures largely unexplored. To advance the research in musical conducting gesture recognition, we focus on two key aspects: (1) broadening the dataset to encompass various conducting speeds and investigating its impact on performance, and (2) introducing a stacked neural network architecture to explore performance improvements beyond conventional node increase. The study demonstrates that incorporating diverse data signifi- cantly enhances performance and that stacking neural network layers yields notable performance gains.
Fundamental, Technical and Sentiment Analysis for Algorithmic Trading with Genetic Programming

Algorithmic trading is a topic with major developments in the last years. Investors rely mostly on indicators derived from fundamental (FA) or technical analysis (TA), while sentiment analysis (SA) has also received attention in the last decade. This has led to great financial advantages with algorithms being the main tool to create pre-programmed trading strategies. Although the three analysis types have been mainly considered individually, their combination has not been studied as much. Given the ability of each individual analysis type in identifying profitable trading strategies, we are motivated to investigate if we can increase the profitability of such strategies by combining their indicators. Thus, in this paper we propose a novel Genetic Programming (GP) algorithm that combines the three analysis types and we showcase the advantages of their combination in terms of three financial metrics, namely Sharpe ratio, rate of return and risk. We conduct experiments on 30 companies and based on the results, the combination of the three analysis types statistically and significantly outperforms their individual results, as well as their pairwise combinations. More specifically, the proposed GP algorithm has the highest mean and median values for Sharpe ratio and rate of return, and the lowest (best) mean value for risk. Moreover, we benchmark our GP algorithm against multilayer perceptron and support vector machine, and show that it statistically outperforms both algorithms in terms of Sharpe ratio and risk.
Thermoelectric Sensor with CuI Supported on Rough Glass

Thermoelectric generators convert heat into a potential difference with arrays of p- and n-type materials, a process that allows thermal energy harvesting and temperature detection. Thermoelectric sensors have attracted interest in relation to the creation of temperature and combustible gas sensors due to their simple operation principle and self-powering ability. CuI is an efficient p-type thermoelectric material that can be readily produced from a Cu layer by an iodination method. However, the vapor iodination of Cu has the disadvantage of weak adhesion on a bare glass substrate due to stress caused by crystal growth, limiting microfabrication applications of this process. This work presents a rough soda-lime glass substrate with nanoscale cavities to support the growth of a CuI layer, showing good adhesion and enhanced thermoelectric sensitivity. A rough glass sample with nanocavities is developed by reactive ion etching of a photoresist-coated glass sample in which aggregates of carbon residuals and the accumulation of NaF catalyze variable etching rates to produce local isotropic etching and roughening. A thermoelectric sensor consists of 41 CuI/In-CoSb3 thermoelectric leg pairs with gold electrodes for electrical interconnection. A thermoelectric leg has a width of 25 μm, a length of 3 mm, and a thickness of 1 μm. The thermoelectric response results in an open-circuit voltage of 13.7 mV/K on rough glass and 0.9 mV/K on bare glass under ambient conditions. Rough glass provides good mechanical interlocking and introduces important variations of the crystallinity and composition in the supported thermoelectric layers, leading to enhanced thermopower.
Analysis of species distribution in time and space for wildlife conservation

We analysed population trends and distributions of breeding birds in Lombardy (Italy), focusing on agro-ecosystem species. We used data derived from a long-term monitoring program and other projects by means of point counts method. Species distribution and abundance were estimated using generalized additive models (GAMs). We assessed the mean annual variation rate for 20 breeding bird species, between 1992 and 2016, applying the discrete population growth model. Moreover, we performed spatial predictions using fitted models in order to build potential distribution maps. Over the whole period, ten species showed a significant decline, while five species showed a significant increased. As extreme values at regional scale, the Skylark showed a significant population decline, losing about the 90% of starting population, while the Common Wood Pigeon population increased about 2000% from 1992.
A framework for measuring sustainable green information technology practices in Universities of South Africa

iii TABLE OF CONTENTS . v LIST OF ACADEMIC OUTPUTS BASED ON THIS STUDY . xii LIST OF FIGURES . xiii LIST OF TABLES . xiv LIST OF ABBREVIATIONS AND ACRONYMS . xv CHAPTER 1 . 1 GENERAL OVERVIEW OF STUDY . 1 1.
Bayesian Optimization for the Inverse Problem in Electrocardiography

The inverse problem in electrocardiography is an ill-posed problem where the objective is to reconstruct the electrical activity of the epicardial surface of the heart, given the electrical activity on the thorax’ surface. In the forward problem, the electrical propagation from heart to thorax is modeled by the volume conductor equation with Dirichlet boundary conditions in the heart's surface, and null flux coming from the thorax. The inverse problem, however, does not have a unique solution. In order to find solutions for the inverse problem, techniques such as Tikhonov regularization are classically used, but they often deliver unrealistic solutions. As an alternative, we propose a novel approach, where a fixed solution of the volume conductor model with a source in a forward scheme is used to solve the inverse problem. The unknown values for parameters of the fixed solution can be found using optimization techniques. Due to the characteristics of the problem, where each single evaluation of the cost function is expensive, we use a specialized CMA-ES-based Bayesian optimization technique, that can deliver good results even with a reduced number of function evaluations. Experiments show that the proposed approach can deliver improved results for in-silico simulations.
In Order to Live: A North Korean Girl's Journey to Freedom

Park’s family was loving and close-knit, but life in North Korea was brutal, practically medieval. Park would regularly go without food and was made to believe that, Kim Jong Il, the country’s dictator, could read her mind. After her father was imprisoned and tortured by the regime for trading on the blackmarket, a risk he took in order to provide for his wife and two young daughters, Yeonmi and her family were branded as criminals and forced to the cruel margins of North Korean society. With thirteen-year-old Park suffering from a botched appendectomy and weighing a mere sixty pounds, she and her mother were smuggled across the border into China.