Miracle-dz/UniFAR-FTUs huggingface dataset:
ROUTINE METHOD FOR DETERMINATION OF THE ACTIVITY OF BAKER'S YEAST

The baking-strength test, which estimates the fermentative activity of yeast in dough by measuring the time for a standard amount of dough to reach a fixed height under standardized conditions, is subject to a random error of about 2.0 min. In 70 min. In addition, systematic errors associated with days, operators and testing stations are appreciable. These errors are mainly due to atmospheric pressure variations and to difficulties of temperature control during mixing, handling and incubation of large volumes of dough. A critical examination of published methods based on the measurement, at constant pressure or constant volume, of carbon dioxide formation, showed that none of these was entirely suitable for routine use in yeast factories. A new method, which has been called the fermentometer test, is described. This test is based on the measurement of gas evolution from a small volume of thin dough which is under constant temperature conditions throughout test. Many thousands of determinations have been carried out by the new test, which has now been adopted by us as the standard method for determination of the fermentative activity of yeast in dough. The average standard deviation of the fermentometer test is 1.0 ml. in 100.0 ml. , and systematic errors are small.
Towards Potential of N-back Task as Protocol and EEGNet for the EEG-based Biometric

Electroencephalogram (EEG) has emerged as a biometric trait potentially with more security benefits compared to its conventional competitors such as fingerprint, iris scan, voice recognition, and face detection. However, there is still a long way to go to make EEG biometrics practical in real-world environments. One of the challenges of the EEG-based biometric systems is time efficiency. The protocols that can evoke individualdependent EEG patterns are usually time consuming. The signal-to-noise ratio (SNR) of the EEG signal is also low, which means a large number of epochs/trials (i.e. long acquisition time) are required to achieve a high accuracy recognition system. In this study we propose an EEG-based biometric model that could achieve high identification accuracy with data instances as short as only 1.1s (single epoch instances). In our biometric model, we propose a new protocol called the N-back task which is based on human working memory. As the nature of working memory is very short, it would be possible to elicit individual-dependent EEG responses within a very short period of time. The single epoch classification was achieved applying a deep neural network called EEGNet. Using 1.1s data instances, the proposed model could identify a pool of 26 subjects with the mean accuracy of 0.95, where recognition rate for majority of subjects was ≥0.99. Different components of this identity recognition model, from the proposed protocol to the classification algorithm, can be a line of research for the future of EEG biometric.
Classification of Fine-Art Paintings with Simulated Partial Damages

Recognition of an artistic style of a painting is a complex task that requires significant knowledge and expertise. This paper is a continuation of our previous studies aiming to develop an automatic style classification method. While our previous research was limited to the analysis of paintings with no visible damages, this study investigates the effect of partial damage to a painting on the accuracy of the automatic style recognition. A two-stage approach was adapted with images being first classified patch by patch by a convolutional neural network (CNN) and then by a shallow neural network (NN) trained to make the final decision based on the outcomes achieved by individual patches (image subregions). The partial damage was simulated by superimposing randomly positioned circles with randomized pixel values. It was found that the combination of damaged and nondamaged painting in the training dataset and the strengthening role of the final decision-making classifier increase the system's robustness to partial damages, while at the same time maintaining a relatively high classification accuracy of non-damaged artworks.
Mild Cognitive Impairment Diagnosis and Detecting Possible Labeling Errors in Alzheimer’s Disease with an Unsupervised Learning-based Approach

The diagnosis of cognitive disabilities like dementia and mild cognitive impairment is challenging because several factors are non-linearly related to these pathologies. Thereby, classification errors committed by a specialist can become more frequent. In this work, we propose a methodology for detecting possible labeling errors. For this, we use a classification technique capable of learning patient profiles in an unsupervised manner, then add semantic value to each profile by applying the majority voting technique. Our goal is to make a tool robust against labeling errors present in the data. We achieved a mean accuracy of 89.33%, which is not an improvement considering this as a standalone tool. Then, we compare the labels with the labels provided by an artificial neural network trained in a supervised manner. We could experimentally find pieces of evidence of possible labeling errors in 9.14% of this dataset samples. It shows that our contribution is valuable since it can indicate possible labeling errors.
Back Surface Reflectors for Thin III-V Multi-junction Space Photovoltaics

In this work, textured back surface reflectors (BSR) produced by inverse progression metal-assisted chemical etching (I-MacEtch) is used to render wavelength-specific (WS) light trapping structures for thin GaAs solar cells. The HAZE reflectance measured on the MacEtch BSR shows 80% HAZE in the GaAs absorbing region indicative of high diffused photon scattering. The MacEtch BSR was implemented in a 500 nm thick GaAs solar cell, and the external quantum efficiency shows a 17% enhancement in the photogenerated carrier collection from the base of the solar cell when compared to the flat BSR device. The Fabry-Pérot EQE fringes show a photon lifetime enhancement factor of 5.7 in the MacEtch BSR device, especially evident near the GaAs band edge. The recovered photoabsorption provides evidence that the WS BSR can be implemented into a dual-junction InGaP/GaAs solar cell to provide high current output in a radiation-tolerant thinned GaAs subcell.
An Improved Method for Identification of LDPC Codes Within a Candidate Set

The identification of low-density parity-check (LDPC) codes with higher rate over a candidate set is more difficult than that of LDPC encoders with lower rate. For the given soft information on a received sequence, we present a method to compute the average Taylor polynomial of the syndrome posterior probability according to the parity-checks of the candidates. Simulation results show that the proposed method performs as well as the existing works when dealing with lower rate encoders. Meanwhile, our method outperforms the existing works in higher rate encoders identification, with the gain being about 0.5 dB.
A Two-Stage Multiscale Residual Attention Network for Light Guide Plate Defect Detection

Aiming at the difference in density between the light guide point distribution of the light guide plate (LGP) images, the different size, shape and brightness of LGP defects, and the limitation of a small number of defect samples, our paper proposes a two-stage multiscale residual attention network based on “segmentation + decision” for LGP defect detection. In the first stage, a segmentation subnet is constructed to achieve accurate location of multiscale defects by using the U-shaped structure and the designed Multiscale Residual Attention Unit (MRAU). In the second stage, a decision subnet is constructed to achieve accurate decision whether the LGP image is defective under the guidance of features extracted by segmentation subnet. The design of the network enables the model to be trained using a small number of samples, which is a significant requirement for practical applications. Finally, a lot of experimental studies were conducted on two datasets, including compared with related deep learning methods, proving the effectiveness of residual attention mechanism and proving the effectiveness of multiscale mechanism. Experimental results show that the detection method in this paper has advantages in both detection accuracy and universality under the limitation of a small number of samples. In particular, the detection indicator F1-Score respectively reached 99.85% and 95.46% on the two datasets. Combined with an image acquisition platform, a surface defect detection system is implemented to realize the real-time production quality inspection on the production lines.
Wake-Up Radio: An Enabler of Wireless Convergence

Nowadays, several wireless communication solutions targeted at low-power devices (e.g. sensors, actuators) compete for market dominance. As a consequence, the deployment of the Internet of Things is slowed by fragmentation. However, Cross-Technology Communication (CTC) has appeared as a solution capable of bridging the compatibility gap between non-interoperable devices. In this way, we propose using Wake-up Radio (WuR) to achieve high rate bidirectional CTC, introducing WuR assisted CTC (WuR-CTC). Although WuR was originally proposed to reduce radio power consumption, it provides a secondary communications channel that can be used for high-speed and bidirectional CTC. With WuR-CTC, we demonstrate that WuR can lead to interoperability between non-compatible wireless devices, therefore, providing a compelling reason for the harmonization of the nascent WuR specifications. This article presents a WuR-CTC solution implemented on an heterogeneous testbed with IEEE 802.11 and IEEE 802.15. 4 devices. The resulting testbed achieves reliable CTC with an effective throughput of 26.7 kbps. Moreover, the addition of WuR to the testbed devices provides energy savings, and, in a clear advantage over traditional duty-cycled solutions, allows devices to asynchronously maintain communications.
A Novel Algorithm to Detect Brain Tumor using Staged-Type-II Fuzzy Classifier

Automatic detection of brain tumor is a crucial step in the domain of medical image processing. Classification of the tumor is a vital part in brain tumor diagnosis to aid accurate treatment. However, manual detection with the help of human interpretation is time taking and also subject to inaccurate diagnosis. Based on these facts, an automated brain tumor classification algorithm is proposed in this work. The present work is divided into the following stages, viz. preprocessing, segmentation, feature extraction, feature selection, ranking of the selected features and finally classification of the segmented tumor. Gray-Level-Co-Occurrence Matrix (GLCM), Law’s Texture and Mass Effect features are extracted from the brain tumor and feature selection is carried out for each individual type followed by the ranking of the individual feature types. The final step comprises of the classification algorithm where a three stage classifier using Interval Type-II Fuzzy Logic System is designed in order to classify the segmented tumor into benign or malignant class. Finally, the work is validated with the help of BRATS 12 dataset and the superiority of the model is showcased in comparison with Type-I Fuzzy Inference System.
Fundamental Aspects of Hot Carrier Solar Cell Operation

The hot carrier solar is a novel photovoltaic concept for exceeding the conventional single band gap limit by reducing thermalization losses. Whilst there are reports of having observed hot carrier based solar power conversion, the performances reported lag well behind the promised limits. In this report we examine the operation of a hot carrier solar cell at its most fundamental level. The extraction process and its impacts on limiting efficiency are examined for the case of a zero band gap absorber with thermionic barriers as the energy selective contacts, with it shown that a heat flow from the absorber to the contact layer is present at open circuit voltage. The operation of this type of hot carrier solar cell is examined for forward bias in the dark, showing marked differences with expected current voltage characteristics for conventional solar cells The impact of carrier cooling, with heat transferred from the carriers to the lattice is investigated for both the dark and illuminated cases.
Methodology for Designing an Optimal Connection Scheme for Applications of Non-Planar Photovoltaics

Non-planar photovoltaics (NP-PV) are an emerging application space enabled by advances in thin-film photovoltaic (PV) materials. However, there are challenges to the practical use due to the complexities introduced with a non-planar PV surface. Curved surfaces have built-in partial shading and even self-shading - phenomenon that leads to non-uniform current densities. The conditioning electronics responsible for the maximizing production is highly dependent on how the cells are connected. Specifically, maximum power point tracking performance is contingent on the impact partial shading has on the circuit. Shaded cells in a series connected systems inhibit the flow of current which reduces the output power and can cause irreparable damage to the material. Since non-planar self-shading can be predicted, an optimal configuration that can reduce current variance while increasing usable output power is possible. By analyzing the production patterns of the surface, this paper presents a methodology, called pixelization, to characterize and design an optimal connection scheme with for non-planar applications. Pixelization is the grouping of similarly producing cells for the purpose of increasing energy conversion efficacy. The results of this work give insight into how geometry and surface symmetry affect how non-planar can be designed to maximize the potential of the geometry.
Analysis Performance Long Term Evolution Network on Route of Subway Tunnel Mass Rapid Transit (MRT) Bundaran HI - Senayan

Celullar phone users in Indonesia are increasing steadily each year, thus demanding providers provide adequate internet access to public transportations. Mass Rapid Transit is one of the most crowded public passenger facilities, so an LTE network needs to be built to support users to be able to communicate even if they are underground. To ensure customers get data access and service coverage, a mature Indoor Building Solution (IBS) design is needed so that when implemented it does not experience significant disruptions and is in accordance with the design expected by the Provider. In this final project prior to the implementation of LTE 1800 and 2100, the authors conducted a drive test for data before then conducted a spot test on the train as data after.
Auto-KPCA: A Two-Step Hybrid Feature Extraction Technique for Quantitative Structure–Activity Relationship Modeling

Quantitative structure–activity relationship (QSAR) modeling is an established approach for drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge that is usually addressed by using dimensionality reduction techniques such as principal component analysis (PCA). However, although linear feature extraction techniques have low computational cost and can handle linear relationships between descriptors, they cannot handle the complex structures found in QSAR data. Hybridization of feature extraction techniques is an effective approach to address the challenges of high-dimensional datasets, and combining the benefits of at least two dimensionality reduction techniques has been successful in many fields. This paper proposes Auto-KPCA, a two-step hybrid feature extraction technique that leverages (i) the fast computational capability of kernel PCA (KPCA) and (ii) the performance of a deep generalized autoencoder in handling complex data structures. Based on classification accuracy, the proposed approach is compared to other feature extraction techniques on the same benchmark dataset. The capability of Auto-KPCA is then investigated further by testing four deep-learning classification models, namely a convolutional neural network, a recurrent neural network, a feedforward deep neural network, and long short-term memory. To the best of the authors’ knowledge, this study is the first to investigate hybridization of KPCA and a deep generalized autoencoder in the context of QSAR. The reported results (i) provide invaluable insights regarding the behavior of different techniques in predicting class labels and (ii) demonstrate increased classification accuracy and noticeably decreased mean square error when compared with KPCA and autoencoders.
Situation-aware Power Electronics in Resilient Distribution Systems with Reconfigurable Cyber-Physical Networks

Modern power grids operate as reconfigurable cyber-physical systems (CPSs), which result in improved system resiliency but also complex operation status. Distributed generators (DGs) could identify system behavior by implementing situation awareness (SA) functionality and in turn provide flexible and autonomous responses to the system events. In this work, a control framework is developed for inverter interfaced DGs operation in distribution systems with reconfigurable cyber-physical networks. The DGs understudy are assumed to be equipped with SA capability. The DG operation modes in response to the system’s major events/states are defined, and the transitions between each mode are linked using formulated situation parameters. A set of distributed controllers are designed for DG operation under each mode. The proposed control framework along with the distributed controller designs have been validated by performing a requested network reconfiguration on a 12-bus test feeder using MATLAB/Simulink.
Road Intersection Model Based Reward Function Design in Deep Q-Learning Network for Traffic Light Control

Reinforcement learning based control is an effective approach to traffic light control. In terms of the Deep Q-learning Network (DQN) based traffic light control method, the reward function should be carefully designed, as it affects the performance of the control method and it also brings the challenge in standardized design. Thus, the control method would fail to perform well if the reward function was established improperly. If the reward function was too complicated, it would be time-consuming for the algorithm. To solve this problem, we proposed an intersection-model based reward function in the DQN algorithm. We investigated the different types of urban road intersection structures and analyzed their features. Then we used those features to formulate the reward function. Our model was evaluated via the simulation dataset under different road situations and got less emergency stop with 14%~32% improvement while the number of passing vehicles was also a bit more than the baseline. There were also 22% and 7% speedup in the training process under the tow training process.
Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

This paper studies the problem of <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filtering for nonlinear Markov jump systems based on Takagi-Sugeno model. Firstly, we propose a hybrid event-triggered mechanism with an adjustable threshold, which not only helps to save more limited communication resources, but also excludes Zeno behavior while preserving the merits of continuous triggering. Secondly, given the threat of cyber-attacks to network security, a stochastic variable is introduced to describe the considered deception attacks in filter design. Thirdly, a less restrictive Lyapunov-Krasovskii functional (LKF), which is not required to be continuous and positive definite in a triggering interval, is constructed to establish sufficient condition on the exponential mean-square stability for the filtering error system with a weighted <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance. Meanwhile, co-design of the desired filter and event-triggered mechanism is achieved. Finally, a tunnel diode circuit system is provided to illustrate the effectiveness and advantage of the obtained results.
Left-Right-Front Caching Strategy for Vehicular Networks in ICN-Based Internet of Things

In Vehicular Ad-hoc Networks (VANET), vehicles act like mobile nodes for fetching, sharing, and disseminating important information related to vehicle safety, warning messages, emergency events, and passenger infotainment. Due to continuous information sharing of vehicles with their surrounding nodes, Road Side Units (RSUs), and infrastructures, the existing host-centric IP-based network cannot fulfill the requirements of VANETs. Therefore, Information Centric Networking (ICN) architectures are the introduced to comprehensively address the problems of Internet of Things (IoT)-based VANETs, known as VANET-IoT. This paper introduces a new ICN-based proactive left-right-front (LRF) caching strategy for VANETs, which maximizes the performance of VANETs by placing content proactively at the right nodes. The proposed strategy also provides a mechanism for the timely dissemination of safety-related messages. LRF is compared with other caching strategies in the NS-3 simulator, which outperforms those schemes in terms of cache utilization, hop ratios, and resolved interest ratios with respect to 100 MB, 500 MB, and 1 GB cache sizes.
Solubility Size Effect in Pb/Ag Binary Films

The temperature dependence of the crystal lattice parameter of the components of bilayer films of the PbAg eutectic system with the use of in situ electron diffraction technology is studied in the work. These data were used to assess the solubility in the studied system. It was found that the solubility of solid lead in silver reaches 6 and 11 at. % for films of 22 and 8 nm thickness, respectively, at the temperature of $300 ^{\circ}C$. Such solubility significantly exceeds the values which are typical for the massive state. The obtained values indicate that the main contours of the phase diagram in the area of practically pure lead should shift towards its bigger concentrations
Fundamentals of gas‐liquid‐solid fluidization

The successful design and operation of a gas-liquid-solid fluidized-bed system depend on the ability to accurately predict the fundamental properties of the system, specifically, the hydrodynamics, the mixing of individual phases, and the heat and mass transfer properties. Identification of the flow regimes under which the system operates is crucial to an understanding of both the variations of these properties and overall system performance. This timely, comprehensive review describes in a systematic manner the status of fundamental gas-liquid-solid fluidization behavior. This review also discusses the areas in which current knowledge is deficient and further research is needed.