Miracle-dz/UniFAR-FTUs huggingface dataset:
Optics-Simplified DSP for 50 Gb/s PON Downstream Transmission using 10 Gb/s Optical Devices

Directly-modulated laser (DML) is widely employed in intensity modulation and direct detection (IMDD) system due to its low cost and high output power. However, the corresponding frequency chirp is regarded as one of the main disadvantages for its application in passive optical networks (PONs). In this paper, we theoretically analyze the frequency response evolution of DML based system under different chirp and dispersion conditions, proving that the system bandwidth can be improved by interactions between negative dispersion and DML chirp. Based on this concept, we experimentally demonstrated downstream 50 Gb/s PAM4 signal transmission over 20 km single-mode fiber (SMF) access based on the 10 Gb/s DML operating at 1310 nm and avalanche photodiode (APD). A dispersion-shifted fiber (DSF) providing −150 ps/nm dispersion at 1310 nm in the optical line terminal (OLT) is used to pre-equalize the frequency response of bandwidth-limited directly modulated signals in the optical domain. Thanks to our proposed dispersion-supported equalization (DSE) technique, the system bandwidth can be improved by 5 GHz. Feed-forward equalization (FFE), decision feedback equalization (DFE) and Volterra filter are employed to evaluate the signal performance improvement, respectively. By evaluating the receiver sensitivity, the DSE combined with FFE scheme shows 2 dB improvement than the complex Volterra algorithm, indicating its potential to reduce the complexity of digital signal processing (DSP) and therefore a lower cost and power consumption in PON.
Modeling and Analyzing Factors Affecting Project Delays Using an Integrated Social Network-Fuzzy MICMAC Approach

Abstract Factors that cause project delays are directly and indirectly interrelated. Despite this, the majority of prior studies that have been conducted to identify and rank project delay factors have failed to account for interrelationships between factors. Arguably, without taking these interrelationships and their cumulative effect into account, it is unlikely that the challenges associated with project delays will be fully addressed. With this in mind, this article presents an integrated Social Network-Fuzzy Integrated Network-fuzzy Cross-impact matrix multiplication analysis (MICMAC) approach that considers the largely imprecise interrelationships between multiple project delay factors. The effectiveness of the developed approach is shown through an illustrative demonstration that indicates the approach is effective in determining and analyzing the direct and indirect relational structures between the individual project delay factors. The value of the approach lies in its use for developing an effective risk-mitigation plan for minimizing the severity of project delays.
Improve in CO2 and CH4 Adsorption Capacity on Carbon Microfibers Synthesized by Electrospinning of PAN

Carbon microfibers (CMF) has been used as an adsorbent material for CO2 and CH4 capture. The gas adsorption capacity depends on the chemical and morphological structure of CMF. The CMF physicochemical properties change according to the applied stabilization and carbonization temperatures. With the aim of studying the effect of stabilization temperature on the structural properties of the carbon microfibers and their CO2 and CH4 adsorption capacity, four different stabilization temperatures (250, 270, 280, and 300 °C) were explored, maintaining a constant carbonization temperature (900 °C). In materials stabilized at 250 and 270 °C, the cyclization was incomplete, in that, the nitrile groups (triple-bond structure, e.g., C≡N) were not converted to a double-bond structure (e.g., C=N), to form a six-membered cyclic pyridine ring, as a consequence the material stabilized at 300 °C resulting in fragile microfibers; therefore, the most appropriate stabilization temperature was 280 °C. Finally, to corroborate that the specific surface area (microporosity) is not the determining factor that influences the adsorption capacity of the materials, carbonization of polyacrylonitrile microfibers (PANMFs) at five different temperatures (600, 700, 800, 900, and 1000 °C) is carried, maintaining a constant temperature of 280 °C for the stabilization process. As a result, the CMF chemical composition directly affects the CO2 and CH4 adsorption capacity, even more directly than the specific surface area. Thus, the chemical variety can be useful to develop carbon microfibers with a high adsorption capacity and selectivity in materials with a low specific surface area. The amount adsorbed at 25 °C and 1.0 bar oscillate between 2.0 and 2.9 mmol/g adsorbent for CO2 and between 0.8 and 2.0 mmol/g adsorbent for CH4, depending on the calcination treatment applicated; these values are comparable with other material adsorbents of greenhouse gases.
Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers

Road traffic safety is one of the major challenges for the future of smart cities and transportation networks. Despite several solutions exist to reduce the number of fatalities and severe accidents happening daily in our roads, this reduction is smaller than expected and new methods and intelligent systems are needed. The emergency Call is an initiative of the European Commission aimed at providing rapid assistance to motorists thanks to the implementation of a unique emergency number. In this work, we study the problem of classifying the severity of accidents involving Powered Two Wheelers, by exploiting machine learning systems based on features that could be reasonably collected at the moment of the accident. An extended study on the set of features allows to identify the most important factors that enable to distinguish accident severity. The system we develop achieves around 90% of precision and recall on a large, publicly available corpus, using only a set of eleven features.
Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.
Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.
An Offset-Free Composite Model Predictive Control Strategy for DC/DC Buck Converter Feeding Constant Power Loads

The high penetration of power electronic converters into dc microgrids may cause the constant power load stability issues, which could lead to large voltage oscillations or even system collapse. On the other hand, dynamic performance should be satisfied in the control of power electronic converter systems with small overshoot, less oscillations, and smooth transient performance. This article proposes an offset-free model predictive controller for a dc/dc buck converter feeding constant power loads with guaranteed dynamic performance and stability. First, a receding horizon optimization problem is formulated for optimal voltage tracking. To deal with the unknown load variation and system uncertainties, a higher order sliding mode observer is designed and integrated into the optimization problem. Then an explicit closed-loop solution is obtained by solving the receding horizon optimization problem offline. A rigorous stability analysis is performed to ensure the system large signal stability. The proposed controller achieves optimized transient dynamics and accurate tracking with simple implementation. The effectiveness of the proposed controller is validated by simulation and experimental results.
Statistical Mobility Pattern Of Solo-Yogyakarta Commuter Workers By Prambanan Express Train

This research was done in two cities; Solo and Yogyakarta which is connected by Prambanan Express (Prameks) Train. The objectives of this research are as follows: (1) Reviewing the characteristic of Solo-Yogyakarta Prameks Train users, (2) Reviewing the mobility pattern of Solo-Yogyakarta Prameks Train users. The character of workers that are being reviewed are based on age, education background, job status, income, and the number of family dependents, while the worker‘s mobility pattern that will be studied are: the usage intensity of the Prameks, transportation modes used to go to the departure station and work office, departure and arrival station, the worker‘s reason in choosing the departure and arrival station, origin and destination area of the worker. Questionnaire is used as a survey method in this study. Purposive Proportional Quota Sampling is also used as the sampling technique to gathered samples. Respondent are limited to 100 workers and divided into two categories; 50 men and 50 women and assumed capable to represent the workers‘ characteristic. The research results show that the workers are still in a productive age that are dominated by 21-25-year-old worker, bachelor degree as the highest education level, permanent employees with salary around 3-6 million Rupiah per month, and do not have any family dependent. Most of respondent‘s origin area are Laweyan, Kartosuro, and Banjarsari district, where the locations are closer to the Purwosari and Solobalapan station. The district that were mostly mentioned as the destination in Yogyakarta includes Gondokusuman and Depok area, which are close to Lempuyangan and Maguwoharjo station.
Cluster Tracking Performance Analysis of Linear Heterogeneous Multi-Agent Networks: A Complex Frequency Domain Approach

This paper presents a complex frequency domain approach to investigate the cluster tracking performance of linear heterogeneous multi-agent networks. Firstly, a designed graph matrix is given to eliminate the interaction influence between coupled subnetworks with different tracking signals. On this basis, the cluster tracking performance analysis problem is decomposed into two parts, i.e., stability analysis and steady-state performance analysis of multi-agent networks. Secondly, based on the network structure, a minimum separable subgraph set is proposed to transform the stability of the multi-agent network into the stability of the separable subnetworks. Further, a signal flow diagram is introduced to solve the stability of the separable subnetworks. Thirdly, in view of Final-value theorem, a necessary and sufficient condition is proposed for achieving the cluster tracking control for the closed-loop stable multi-agent network. This result provides a strong theoretical basis for the flexible design of controllers due to that it quantitatively reveals the inherent law of steady-state performance for multi-agent networks. Finally, an algorithm is established to calculate the steady-state error of a general directed multi-agent network. Some simulations are provided to demonstrate the effectiveness of the theoretical results.
Shipborne Acquisition, Tracking, and Pointing Experimental Verifications Towards Satellite-to-Sea Laser Communication

Acquisition, tracking, and pointing (ATP) is a key technology in free space laser communication that has a characteristically high precision. In this paper, we report the acquisition and tracking of low-Earth-orbit satellites using shipborne ATP and verify the feasibility of establishing optical links between laser communication satellites and ships in the future. In particular, we developed a shipborne ATP system for satellite-to-sea applications in laser communications. We also designed an acquisition strategy for satellite-to-sea laser communication. In addition, a method was proposed for improving shipborne ATP pointing error. We tracked some stars at sea, achieving a pointing accuracy of less than 180μrad. We then acquired and tracked some low-Earth-orbit satellites at sea, achieving a tracking accuracy of about 20μrad. The results achieved in this work experimentally demonstrate the feasibility of ATP in satellite-to-sea laser communications.
Compressed Sensing Channel Estimation for OFDM With Non-Gaussian Multipath Gains

This paper analyzes the impact of non-Gaussian multipath component (MPC) amplitude distributions on the performance of Compressed Sensing (CS) channel estimators for OFDM systems. The number of dominant MPCs that any CS algorithm needs to estimate in order to accurately represent the channel is characterized. This number relates to a Compressibility Index (CI) of the channel that depends on the fourth moment of the MPC amplitude distribution. A connection between the Mean Squared Error (MSE) of any CS estimation algorithm and the MPC amplitude distribution fourth moment is revealed that shows a smaller number of MPCs is needed to well-estimate channels when these components have large fourth moment amplitude gains. The analytical results are validated via simulations for channels with lognormal MPCs such as the NYU mmWave channel model. These simulations show that when the MPC amplitude distribution has a high fourth moment, the well known CS algorithm of Orthogonal Matching Pursuit performs almost identically to the Basis Pursuit De-Noising algorithm with a much lower computational cost.
Lean manufacturing practices and performance: the role of social and technical factors

PurposeModern manufacturing systems require tools and techniques that take cognizance of the social (concerning people and relations) as well as the technical environment. The purpose of this paper is to explore the relationship between the social and technical aspects of lean manufacturing practices and their effects on business performance outcomes.Design/methodology/approachThe hypothesized relationships for this study are tested with data collected from 148 Indian manufacturing firms by using SPSS and AMOS statistical software.FindingsThe analysis of the study was conducted using structural equation modeling (SEM) technique, which indicated that both “soft” and “hard” lean practices are positively related to business performance parameters. The findings also demonstrated that “hard” lean practices fully mediate the relationship between “soft” lean practices and business performance parameters.Research limitations/implicationsThere are some limitations of this study. Although a cross-sectional survey has been applied, the research does not permit us to account for the lag between implementation and performance. It also brings the opinion of a limited number of Indian experts about lean manufacturing systems; hence, the sample size could be increased and the nationality of the respondent could be expanded for future research.Practical implicationsThe paper would be of interest to Lean practitioners, and the results of this study can be used in organizations to put a focus on social-cultural changes while applying lean technical tools when it comes to practices as well as importance.Originality/valueThis paper extends theoretical contribution in production and operations management literature, highlighting how social and technical practices have to interact to enable a successful lean manufacturing implementation.
Improving Conductivity in Nano-Conduit Flows by Using Thermal Pulse-Induced Brownian Motion: A Spectral Impulse Intensity Approach

Inter-particle and particle-wall connectivity in suspension flow has profound effects on thermal and electrical conductivity. The spectral impulse generation and the imparting of kinetic energy on the particles is shown through a mathematical analysis to be effective as a means of achieving an approximate equivalent of a Langevin thermostat. However, with dilute suspensions, the quadratic form of the thermal pulse spectra is modified with a damping coefficient to achieve the desired Langevin value. With the dense suspension system, the relaxation time is calculated from the non-linear differential equation, and the fluid properties were supported by the viscosity coefficient. A “smoothed” pulse is used for each time-step of the flow simulation to take care of the near-neighbor interactions of the adjacent particles. An approximate optimal thermostat is achieved when the number of extra pulses introduced within each time step is found to be nearly equal to the co-ordination number of each particle within the assembly. Furthermore, the ratio of the particle kinetic energy and the thermal energy imparted is found to be never quite equal to unity, as they both depend upon the finite values of the pulse duration and the relaxation time.
Phase-Based Human Target 2-D Identification With a Mobile FMCW Radar Platform

Identifying and locating human targets in a complex environment with a mobile radar platform are important for urban rescue missions. This article introduces a precise phase-based human target 2-D synthetic-aperture radar (SAR) imaging and recognition system based on vital sign tracking with a mobile frequency-modulated continuous-wave (FMCW) radar. The solution first relies on FMCW phase detection to extract the vital sign signal of the human target. It then applies the SAR imaging method to obtain the 2-D imaging of the scene and labels human targets in the detected 2-D map without manual selection. Experiments are carried out for the azimuth human target recognition, multiple human targets recognition, and 2-D mapping in a complex outdoor environment. The results show that the proposed solution can capture not only the vital signs of human subjects but also the precise position information for 2-D mapping, demonstrating great potential for search and rescue applications.
On Spacetime Curvature and Gravity Probe

General relativity explains gravity as distortion of the structure of spacetime by matter , affecting the inertial motion of the other matter. General relativity predicts that the path of light is bent in a gravitational field. Light passing a massive body is deflected towards that body. This effect has been confirmed by observing the light of stars or distant quasars being deflected as it passes the sun. In this work , the author experimentally attempts to show that it is not mass which curves spacetime but the attractive force of the mass that warps the spacetime.
Context-Aware Informative Sample Selection and Image Forgery Detection

Author(s): Bappy, Md Jawadul Hasan | Advisor(s): Roy-Chowdhury, Amit K | Abstract: Most of the computer vision methods assume that data will be labeled and available beforehand in order to train a good recognition model. However, it becomes infeasible and unrealistic to know all the labels beforehand with the huge corpus of visual data being generated on a daily basis. In most image and video analysis tasks, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annotation cost, as it is time-consuming to annotate unlabeled samples. In this thesis, we aim to design information-theoretic approaches which exploit inter-relationships between data instances in order to find informative samples in image or videos. Moreover, in recent years, the advent of high-tech journaling tools facilitates an image to be forged in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high-confidence detection framework which can localize forged/manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image forgery focuses only the possible tampered region which makes the problem even more challenging.We present two distinct information-theoretic approaches for selecting samples to learn recognition models, and a deep learning based method for localizing manipulation from images. In first approach, we show how models for joint scene and object classification can be learned online. A major motivation for this approach is to exploit the hierarchical relationships between scenes and objects, represented as a graphical model, in an active learning framework. To select the samples on graph, which need to be labeled by a human, we formulate an optimization function that reduces the joint entropy of scene and object variables. The second approach we propose is motivated by the theories in data compression, which exploits the concept of typicality from the domain of information theory in order to find informative samples in videos. Typicality is a simple and powerful technique which can be applied to compress the training data to learn a good classification model. Both of the approaches lead to a significant reduction in the amount of manual labeling effort for similar or better performance when compared with a model trained with the full dataset. In the final chapter, we explore a deep learning architecture to localize manipulated regions from an image. Our proposed framework utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. The overall framework is capable of detecting different types of image forgeries.