Miracle-dz/UniFAR-FTUs huggingface dataset:
Process Design and Economics for the Conversion of Algal Biomass to Biofuels: Algal Biomass Fractionation to Lipid- and Carbohydrate-Derived Fuel Products

NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Executive Summary The U.S. Department of Energy (DOE) promotes the production of a range of liquid fuels and fuel blendstocks from biomass feedstocks by funding fundamental and applied research that advances the state of technology in biomass production, conversion, and sustainability. As part of its involvement in this program, the National Renewable Energy Laboratory (NREL) investigates the conceptual production economics of these fuels. This includes fuel pathways from lignocellulosic (terrestrial) biomass, as well as from algal (aquatic) biomass systems. Over the past decade, NREL conducted a campaign to quantify the economic implications associated with observed and aspirational performance for the conversion of corn stover to ethanol through techno-economic modeling. This effort served two important purposes: (1) to establish a benchmark representing the initial " state of technology " at the time, and (2) to set goals for near-term R&D and cost targets, as well as to track progress toward achieving these targets by periodically updating the models based on the latest research improvements. Beginning in 2013, NREL began transitioning from the singular focus on ethanol to a broad slate of products and conversion pathways, ultimately to establish similar benchmarking and targeting efforts. One of these pathways is the conversion of algal biomass to fuels via extraction of lipids (and potentially other components), termed the " algal lipid upgrading " or ALU pathway. This report describes in detail one potential ALU approach based on a biochemical processing strategy to selectively recover and convert select algal biomass components to fuels, namely carbohydrates to ethanol and lipids to a renewable diesel blendstock (RDB) product. The overarching process design converts algal biomass …
Low-complexity variable loading for robust adaptive beamforming

Although the majority of the existing robust adaptive beamformers focus on how to choose an identical loading factor for all eigenvalues, relatively few investigations on variable loading (VL) have been conducted. A low-complexity VL beamformer is proposed in which the beamformer weight vector is deliberately prevented from converging to the noise components. Then inspired by the traditional identical loading method, the loading factor can be set in an ad hoc manner. Numerical results demonstrate the superior performance of the proposed beamformer relative to other existing approaches such as ‘identical diagonal loading’ and ‘robust Capon’ beamformers.
Reflections on reflection in action research: notes from a field study

There is wide agreement in the literature that “reflection” is a critical component in the methodology of action research (AR). This paper argues that there is little practical guidance for information systems researchers on how to carry out the process of reflection. The work is presented in the context of a case study of innovation in APC-MGE Ireland, a subsidiary of the critical power and cooling services division of the Schneider Electric Corporation. The paper describes the utilization of a novel form of action research recently proposed to the IS community by Martensson & Lee which they call dialogical AR. The study proposes to make a contribution by providing a questionnaire to assist the process on reflection in the course of AR cycles. The instrument was developed from engagement with the psychology literature and is suggested as a methodological plug-in to the Principles of Canonical Action Research. 1 I am currently undertaking a PhD in MIS at the Centre for Innovation and Structural Change, National University of Ireland, Galway. My supervisor is Dr. Brian Donnellan brian.donnellan@nuigalway.ie
Moving Object Detection in HEVC Video

Video surveillance has drawn much interest in monitoring physical assets, spaces and events over time for detection of threats as well as business and process monitoring purposes. However, the rising number of recorded videos has significantly increased the time and effort in manual event analysis and video content management. Therefore, automatic moving object detection is of great importance. Nowadays, for storage and transmission purposes, video usually appears in the compressed form. Therefore, in this paper, an automatic moving object detection method is proposed for HEVC video. Specifically, the number of bits spent on coding a frame, which can be extracted during encoding or retrieved from an encoded video bit stream, is exploited as the key feature for moving object detection. In addition, temporal sub-layering feature of HEVC is utilized to reduce the number of frame to be processed, which in turn magnifies the energy of the coded video frames without losing most of the predicted information. A coarse background / foreground mask is then formed based on bit consumption, and it is further refined via post processing to remove noise and to smooth the mask image. The proposed method achieves encouraging results in detecting slow moving objects, even with dynamic background.
Dielectric properties modelling of cellular structures with PDMS for micro-sensor applications

Electro-active polymers are emerging in the fields of actuators and micro-sensors because their good dielectric and mechanical properties makes them suitable for such applications. In this work, we focus on micro-structured (cellular) polymer materials (referred as piezoelectrets or ferroelectrets) that need prior charging to attain piezoelectric behaviour. The development of such applications requires an in-depth knowledge of the intrinsic dielectric properties of such structures and models to enable the accurate prediction of a given micro-structured material’s dielectric properties. Various polymers including polypropylene, polytetrafluoroethylene, fluoroethylenepropylene, cyclo-olefines and poly(ethylene terephthalate) in a cellular form have been studied by researchers over the last fifteen years. However, there is still a lack of information on the intrinsic dielectric properties of the most recently used dielectric polymer (polydimethylsiloxane, PDMS) over wide frequency and temperature ranges. In this work, we shall propose an exhaustive equivalent electrical circuit model and explain how it can be used to predict the micro-structured PDMS complex permittivity versus frequency and temperature. The results obtained from the model were found to be in good agreement with experimental data for various micro-structured PDMS materials. Typically, for micro-sensor applications, the dielectric constant and dielectric losses are key factors which need to be minimized. We have developed a configuration which enables both to be strongly reduced with a reduction of 16% in the dielectric constant of a micro-structured PDMS compared with the bulk material. In addition, the phenomena responsible for dielectric losses variations with frequency and temperature are discussed and correlated with the theoretical model. Our model is thus proved to be a powerful tool for the control of the dielectric properties of micro-structured PDMS material for micro-sensor applications.
A Resonator-type Sensor with Enhanced Sensitivity For Noninvasively Detecting the Variation of Permittivity of Liquids

A microwave sensor based on double slotted split-ring resonators (SSRs) for noninvasively detecting the permittivity variation is investigated in this work. To enhance the sensitivity, a quarter open-end microstrip line as a parallel RLC resonator coupling to SSR is proposed. In addition, we show that the distance between SSRs is significant, which enables to provide critical coupling to improve Q-factor of the sensor. Using the permittivity of different concentrations of liquids, i.e., glucose and saline solutions, measured, we show that the sensor has the sensitivity in terms of resonant frequency shift $\Delta f$ and magnitude variation $\Delta S_{11}$ of $\Delta f=10 \mathrm{MHz}$ and $\Delta\left|S_{11}\right|=5 \mathrm{~dB}$ for the glucose solution of which the concentration is from 0 to $100 \mathrm{mg} / \mathrm{mL}$ (permittivity variation is $\Delta \varepsilon=0.02-j 0.0002$); for the saline solution, $\Delta f=12 \mathrm{MHz}$ and $\Delta\left|S_{11}\right|=15 \mathrm{~dB}$ (permittivity variation is $\Delta \varepsilon=0.1106-j 0.0357$). Compared with state-of the-art microwave concentration sensors, the sensor exhibits brilliant performance.
Fusing UWB and Depth Sensors for Passive and Context-Aware Vital Signs Monitoring

This demonstration presents a working prototype of VitalHub, a practical solution for longitudinal in-home vital signs monitoring. To balance the trade-offs between the challenges related to an individual's efforts thus compliance, and robustness with vital signs monitoring, we introduce a passive monitoring solution, which is free of any on-body device or cooperative efforts from the user. By fusing the inputs from a pair of co-located UWB and depth sensors, VitalHub achieves robust, passive, context-aware and privacy-preserving sensing. We use a COTS UWB sensor to detect chest wall displacement due to the respiration and heartbeat for vital signs extraction. We use the depth information from Microsoft Kinect to detect and locate the users in the field of view and recognize the activities of the respective users for further analysis. We have tested the prototype extensively in engineering and medical lab environments. We will demonstrate the features and performance of VitalHub using real-world data in comparison with an FDA approved medical device.
Extracting Threat Intelligence Related IoT Botnet From Latest Dark Web Data Collection

As it is easy to ensure the confidentiality of users on the Dark Web, malware and exploit kits are sold on the market, and attack methods are discussed in forums. Some services provide IoT Botnet to perform distributed denial-of-service (DDoS as a Service: DaaS), and it is speculated that the purchase of these services is made on the Dark Web. By crawling such information and storing it in a database, threat intelligence can be obtained that cannot otherwise be obtained from information on the Surface Web. However, crawling sites on the Dark Web present technical challenges. For this paper, we implemented a crawler that can solve these challenges. We also collected information on markets and forums on the Dark Web by operating the implemented crawler. Results confirmed that the dataset collected by crawling contains threat intelligence that is useful for analyzing cyber attacks, particularly those related to IoT Botnet and DaaS. Moreover, by uncovering the relationship with security reports, we demonstrated that the use of data collected from the Dark Web can provide more extensive threat intelligence than using information collected only on the Surface Web.
A SYSTEM COMBINATION FOR MALAY BROADCAST NEWS TRANSCRIPTION

In this paper, we propose a post decoding system combination approach for automatic transcribing Malay broadcast news. This approach combines the hypotheses produced by parallel automatic speech recognition (ASR) systems. Each ASR system uses different language models, one which is generic domain model and another is domain specific model. The main idea is to take advantage of different ASR knowledge to improve ASR decoding result. It uses the language score and time information to produce a 1-best lattice, and then rescore the 1-best lattice to get the most likely word sequence as the final output. The proposed approach was compared with conventional combination approach, the recognizer output voting error reduction (ROVER). Our proposed approach improved the word error rate (WER) from 33.9% to 30.6% with an average relative WER improvement of 9.74%, and it is better than the conventional ROVER approach.
Comparative Study of Transfer Learning techniques for Lung Disease prediction

Lung Disease is one of the most common healthcare issues in the entire world in all age groups of humans due to the cause of smoking, infection and contaminated air. Pneumonia, Coronavirus disease 2019 (COVID-19) and Tuberculosis are the common types of lung disease. To reduce the diagnosis of the Lung Disease, several research works have been done with the help of Artificial Intelligence based-technologies. Prediction and classification of Lung Disease are the common tasks using Chest X-ray images, CT scan images, and MRI images with the help of machine learning, deep learning, and transfer learning models. The main idea of this paper is to show the comparative analysis of ResNet-50, MobileNet-V2, VGG-19 and DenseNet201 CNN-based Transfer Learning techniques and suggesting which image classification algorithm is more recommended to predicting and classifying Lung Disease classification problem. Those four algorithms have been trained on the X-ray images and evaluated using performance metrics techniques. The results have been shown 94.64% accuracy obtained using ResNet50, 97.43% accuracy obtained using VGG19, 98.49% accuracy obtained using MobileNetV2, and 98.05% accuracy obtained using DenseNet201 pre-trained model. Based on the experimental results MobileNet model has been outperformed as compared to other models. Training time per epoch and ROC-AUC values of MobileNet was better than other models. Based on our studies, MobileNet transfer learning is recommended pre-trained model for Lung Disease prediction and classification problem.
Statistical Modeling and Inference of the Effectiveness of Preventive Maintenance for Repairable Systems

The random effectiveness of preventive maintenance (PM) is not negligible when enacting the periodic PM policy. Hence, it is necessary to define an index that characterizes the random effectiveness of PM in the recurrent failure data analysis. Nevertheless, with purely the recurrent failure data, it is unreasonable to preset the index by a constant without knowing the specific physical mechanism of maintenance. In this paper, we propose a model based on the non-homogeneous Poisson process to account for both the inherent wear-out and the random effectiveness of PM for repairable systems. After each PM, the rate of occurrence of failures (ROCOF) of the system is multiplied by the index, which is modeled by a random variable following a gamma distribution. The Expectation-Maximum (EM) algorithm is then leveraged to estimate the parameters of the gamma distribution and the ROCOF. We apply the quasi-Monte Carlo method to approximate the multidimensional integration in the EM algorithm. The proposed model is validated by numerical simulations based on a real-world recurrent failure dataset of ambulances.
SAFSN: A Self-Attention Based Neural Network for Encrypted Mobile Traffic Classification

Network traffic classification is the foundation of many network security services. As the widespread use of encrypted protocols makes packet payload invisible, it becomes impossible for traditional rule-based methods to classify network traffic correctly. To solve this problem, recent researches use deep learning algorithms to extract features of flows and classify the traffic data. In this paper, we propose the Self-Attention based Flow Sequence Network (SAFSN), an end-to-end encrypted traf-fic classification model. We employ both the elements' dependence within a single flow and the dependence between flows, which can be well combined with the self-attention mechanism. We evaluate SAFSN on a real-world dataset covering 40 apps, and the results indicate that SAFSN does well in multi-classification tasks and outperforms all the other methods. Specially SAFSN can achieve an 8.83 % higher F -score and an 8.45 % higher accuracy than the state-of-the-art machine learning classifier.
Speech Emotion Recognition System for Human Interaction Applications

Speech emotion recognition systems are needed nowadays in many human interaction applications such as call-centers, e-learning, autonomous driver emotion detection, and physiological diseases analysis. The existing speech emotion recognition systems focus mainly on a single corpus. On the other hand, the speech emotion recognition performance on cross-corpus is still an ongoing challenge in the research domain. This paper presents a study for speech emotion recognition tested on 3 widely used languages (English, German and French) using RAVDESS, EmoDB and CaFE datasets. Four Machine learning classifiers: Multi-Layer Perceptron, Support Vector Machine, Random Forest and Logistic Regression have been used. A newly developed feature set was introduced consisting of main speech features as prosodic features, spectral features and energy. Very promising results were obtained using this feature set even when compared with the benchmarked feature set "Interspeech 2009". Furthermore, Feature importance techniques were used to study the feature importance per each classifier across each corpus. From our results, it was found that SVM is the best classifier from recognition rates and running performance point of view. The model achieved an accuracy of 70.56% on RADVESS, 85.97% on Emodb and 70.61% on CaFE.
CSTD-Telugu Corpus: Crowd-Sourced Approach for Large-Scale Speech data collection

Speech is a natural mode of communication among all beings. India is a densely populated country, and people are diverse throughout the globe. The spoken language is the medium of instruction to interact among the people. The majority of Indian languages are spoken globally. The unavailability of larger volumes of transcribed and annotated speech data is often a hurdle for building reliable speech recognition (ASR) systems for Indian languages. Crowdsourcing strategies are effective in collaboratively collecting speech data resources. This paper describes the experience of large-scale speech data collection for the Telugu language through mobile and web-based applications. With this crowd contributed speech, the performance of the baseline ASR system is shown for clean speech. ASR performance for pink and white noises is also compared for various deep neural network (DNN) based acoustic models. The details regarding the usage of frameworks and their challenges during their implementation are part of this paper. The framework adopted for collecting the speech data is rapid, cost-saving, and offers the advantage of extending it to all the other Indian languages.
Coprime Microphone Arrays for Estimating Speech Direction of Arrival Using Deep Learning

This paper investigates deep neural network (DNNs) applied to coprime microphone arrays (CPMAs) and semi-coprime microphone arrays (SCPMAs) for direction of arrival (DOA) estimation of speech signals. Existing research has shown that the coprime arrangement increases the operating frequency of conventional uniform linear arrays (ULAs) by interleaving two uniform sub-arrays with different spacing. The SCPMA extends this arrangement and further increases the operating frequency, above which interfering signals are largely amplified in the recording and lead to confusion with the desired source. As a result, both types of coprime geometries improve the beampattern, array gain and DOA estimation results compared to the ULA. However, large side lobes may still occur in the beampattern of the two coprime arrangements, resulting in degraded DOA estimates using conventional beamforming-based approaches in an adverse environment. The proposed approach alternatively utilises deep learning (DL) to estimate speech DOAs using coprime microphone arrays. Experimental results evaluating the accuracy under different levels of noise using the mean absolute error (MAE) and root mean square error (RMSE) of the DOA estimate indicate satisfactory performance of the proposed method.
A Dual-board Substrate Integrated Waveguide for Large Phase Delay Application

A novel large phase-delay transmission line based on dual printed circuit boards (PCBs) for substrate integrated waveguide (SIW) is proposed. Rectangular slots and metallic vias are used to enhance the phase delay feature. The simulated results show the phase difference of the proposed phase-delayed SIW with dual PCBs is -577° at the concerned frequency of 12 GHz. The phase-delay characteristic of proposed design has been analyzed through simulations compared with both micro-strip line and single-layer SIW which using the same definite physical length. The simulated results show that phase-delay performance of our design is far superior to that of the traditional microstrip or the single-layer SIW.
Product Quantization to Reduce Entropy of Labels for Fast and Accurate Image Retrieval

Product quantization (PQ) is a popular technique for fast image retrieval from a large-scale database. PQ methods quantize image features into short codes and realize fast retrieval using lookup tables based on the codes. Although the entropy of labels (i.e., ground truths for retrieval) is crucial for the retrieval performance, existing PQ methods focus only on the quantization errors. This paper proposes a novel PQ method that reduces the entropy of labels to improve the retrieval performance. We assume that correct labels for each training sample are known; then, we train the codes so that we can minimize the label errors as well as the quantization errors to reduce the entropy of labels. This enables fast and accurate retrieval when queries (i.e., images whose labels are unknown) are given.
Track-association Algorithm Based on Fuzzy C-Means Clustering with PSO

Track-association has been recognized as one of the major problems in distributed multi-sensor information fusion systems. This paper mainly discusses the track-association problem for the distributed multi-target tracking system. A novel fuzzy clustering algorithm which uses PSO algorithm and combines with Fuzzy C-means(FCM) is proposed. And then apply this algorithm in the problem of the track-association. At last, the simulation results under the three sensor environment are given. Compared with the FCM method, the result show it enhances the correct association rate efficiently in the different disturbs environment.