Abstract: Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.
Abstract: As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.
Abstract: In the context of sustainable development and climate change, the adaptation of buildings to the climatic context in hot climates is a necessity if we want to improve living conditions in housing and reduce the risks to the health and productivity of occupants due to thermal discomfort in buildings. One can find a wide variety of efficient solutions but with high costs. In developing countries, especially tropical countries, we need to appreciate a technology with a very limited cost that is affordable for everyone, energy efficient and protects the environment. Biosourced insulation is a product based on plant fibers, animal products or products from recyclable paper or clothing. Their development meets the objectives of maintaining biodiversity, reducing waste and protecting the environment. In tropical or hot countries, the aim is to protect the building from solar thermal radiation, a source of discomfort. The aim of this work is in line with the logic of energy control and environmental protection, the approach is to make the occupants of buildings comfortable, reduce their carbon dioxide emissions (CO2) and decrease their energy consumption (energy efficiency). We have chosen to study the thermo-physical properties of banana leaves and sawdust, especially their thermal conductivities, direct measurements were made using the flash method and the hot plate method. We also measured the heat flow on both sides of each sample by the hot box method. The results from these different experiences show that these materials are very efficient used as insulation. We have also conducted a building thermal simulation using banana leaves as one of the materials under Design Builder software. Air-conditioning load as well as CO2 release was used as performance indicator. When the air-conditioned building cell is protected on the roof by banana leaves and integrated into the walls with solar protection of the glazing, it saves up to 64.3% of energy and avoids 57% of CO2 emissions.
Abstract: Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.
Abstract: Phosphorus is an essential nutrient for plant life which is most frequently found as phosphate in water. Once phosphate is found in abundance in surface water, a series of adverse effects on an ecosystem can be initiated. Therefore, a portable and reliable method is needed to monitor the phosphate concentrations in the field. In this paper, an inexpensive dip strip device with the ascorbic acid/antimony reagent dried on blotting paper along with wet chemistry is developed for the detection of low concentrations of phosphate in water. Ammonium molybdate and sulfuric acid are separately stored in liquid form so as to improve significantly the lifetime of the device and enhance the reproducibility of the device’s performance. The limit of detection and quantification for the optimized device are 0.134 ppm and 0.472 ppm for phosphate in water, respectively. The device’s shelf life, storage conditions, and limit of detection are superior to what has been previously reported for the paper-based phosphate detection devices.
Abstract: This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters.
Abstract: Radiant chilled ceiling (RCC) has been perceived to save more energy and provide better thermal comfort than the traditional air conditioning system. However, its application has been rather limited by some reasons e.g., the scarce information about the thermal characteristic in the radiant room and the local climate influence on the system performance, etc. To bridge such gap, an office-like experiment room with a RCC was constructed in the hot and humid climate of Thailand. This paper presents exemplarily results from the RCC experiments to give an insight into the thermal environment in a radiant room and the cooling load associated to maintain the room's comfort condition. It gave a demonstration of the RCC system operation for its application to achieve thermal comfort in offices in a hot humid climate, as well.
Abstract: This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.
Abstract: Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.
Abstract: In this paper, a non-cooperative game method is
formulated where all players compete to transmit at higher
power. Every base station represents a player in the game.
The game is solved by obtaining the Nash equilibrium (NE)
where the game converges to optimality. The proposed method,
named Power Efficient Handover Game Theoretic (PEHO-GT)
approach, aims to control the handover in dense small cell
networks. Players optimize their payoff by adjusting the
transmission power to improve the performance in terms of
throughput, handover, power consumption and load balancing.
To select the desired transmission power for a player, the payoff
function considers the gain of increasing the transmission power.
Then, the cell selection takes place by deploying Technique for
Order Preference by Similarity to an Ideal Solution (TOPSIS).
A game theoretical method is implemented for heterogeneous
networks to validate the improvement obtained. Results reveal
that the proposed method gives a throughput improvement while
reducing the power consumption and minimizing the frequent
handover.
Abstract: The dense deployment of small cells is a promising
solution to enhance the coverage and capacity of the
heterogeneous networks (HetNets). However, the unplanned
deployment could bring new challenges to the network ranging
from interference, unnecessary handovers and handover failures.
This will cause a degradation in the quality of service (QoS)
delivered to the end user. In this paper, we propose an integrated
Grey Rational Analysis Standard Deviation based handover
method (GRA-SD) for HetNet. The proposed method integrates
the Standard Deviation (SD) technique to acquire the weight of
the handover metrics and the GRA method to select the best
handover base station. The performance of the GRA-SD method
is evaluated and compared with the traditional Multiple Attribute
Decision Making (MADM) methods including Simple Additive
Weighting (SAW) and VIKOR methods. Results reveal that the
proposed method has outperformed the other methods in terms of
minimizing the number of frequent unnecessary handovers and
handover failures, in addition to improving the energy efficiency.
Abstract: Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.
Abstract: The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation.
Abstract: Face is a non-intrusive strong biometrics for
identification of original and dummy facial by different artificial
means. Face recognition is extremely important in the contexts of
computer vision, psychology, surveillance, pattern recognition,
neural network, content based video processing. The availability of a
widespread face database is crucial to test the performance of these
face recognition algorithms. The openly available face databases
include face images with a wide range of poses, illumination, gestures
and face occlusions but there is no dummy face database accessible in
public domain. This paper presents a face detection algorithm based on
the image segmentation in terms of distance from a fixed point and
template matching methods. This proposed work is having the most
appropriate number of nodal points resulting in most appropriate
outcomes in terms of face recognition and detection. The time taken to
identify and extract distinctive facial features is improved in the range
of 90 to 110 sec. with the increment of efficiency by 3%.
Abstract: The hillside building shows different behavior as a flat ground building in lateral loading. Especially the step back building in the sloping ground has different seismic behavior. The hillside building 3D model having different types of structural elements is introduced and analyzed with a seismic effect. The structural elements such as the shear wall, steel, and concrete bracing are used to resist the earthquake load and compared with without using any shear wall and bracing system. The X, inverted V, and diagonal bracing are used. The total nine models are prepared in ETABs finite element coding software. The linear dynamic analysis is the response spectrum analysis (RSA) carried out to study dynamic behaviors in means of top story displacement, story drift, fundamental time period, story stiffness, and story shear. The results are analyzed and made some decisions based on seismic performance. It is also observed that it is better to use the X bracing system for lateral load resisting elements.
Abstract: International guidelines recommend removing any
artificial body in Low Earth Orbit (LEO) within 25 years from
mission completion. Among disposal strategies, electrodynamic
tethers appear to be a promising option for LEO, thanks to the
limited storage mass and the minimum interface requirements to the
host spacecraft. In particular, recent technological advances make it
feasible to deorbit large objects with tether lengths of a few kilometers
or less. To further investigate such an innovative passive system,
the European Union is currently funding the project E.T.PACK
– Electrodynamic Tether Technology for Passive Consumable-less
Deorbit Kit in the framework of the H2020 Future Emerging
Technologies (FET) Open program. The project focuses on the design
of an end of life disposal kit for LEO satellites. This kit aims to
deploy a taped tether that can be activated at the spacecraft end of life
to perform autonomous deorbit within the international guidelines.
In this paper, the orbital performance of the E.T.PACK deorbiting
kit is compared to other disposal methods. Besides, the orbital decay
prediction is parametrized as a function of spacecraft mass and tether
system performance. Different values of length, width, and thickness
of the tether will be evaluated for various scenarios (i.e., different
initial orbital parameters). The results will be compared to other
end-of-life disposal methods with similar allocated resources. The
analysis of the more innovative system’s performance with the tape
coated with a thermionic material, which has a low work-function
(LWT), for which no active component for the cathode is required,
will also be briefly discussed. The results show that the electrodynamic tether option can be a
competitive and performant solution for satellite disposal compared
to other deorbit technologies.
Abstract: In this study, experiments were carried out to achieve a promising multifunctional and modified silicate based bioactive glass (BG). The main aim of the study was investigating the effect of lithium (Li) and magnesium (Mg) substitution, on in vitro bioactivity of substituted-58S BG. Moreover, it is noteworthy to state that modified BGs were synthesized in 60SiO2–(36-x)CaO–4P2O5–(x)Li2O and 60SiO2–(36-x)CaO–4P2O5–(x)MgO (where x = 0, 5, 10 mol.%) quaternary systems, by sol-gel method. Their performance was investigated through different aspects such as biocompatibility, antibacterial activity as well as their effect on alkaline phosphatase (ALP) activity, and proliferation of MC3T3 cells. The antibacterial efficiency was evaluated against methicillin-resistant Staphylococcus aureus bacteria. To do so, CaO was substituted with Li2O and MgO up to 10 mol % in 58S-BGs and then samples were immersed in simulated body fluid up to 14 days and then, characterized by X-ray diffraction, Fourier transform infrared spectroscopy, inductively coupled plasma atomic emission spectrometry, and scanning electron microscopy. Results indicated that this modification led to a retarding effect on in vitro hydroxyapatite (HA) formation due to the lower supersaturation degree for nucleation of HA compared with 58s-BG. Meanwhile, magnesium revealed further pronounced effect. The 3-(4,5 dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) and ALP analysis illustrated that substitutions of both Li2O and MgO, up to 5 mol %, had increasing effect on biocompatibility and stimulating proliferation of the pre-osteoblast MC3T3 cells in comparison to the control specimen. Regarding to bactericidal efficiency, the substitution of either Li or Mg for Ca in the 58s BG composition led to statistically significant difference in antibacterial behaviors of substituted-BGs. Meanwhile, the sample containing 5 mol % CaO/Li2O substitution (BG-5L) was selected as a multifunctional biomaterial in bone repair/regeneration due to the improved biocompatibility, enhanced ALP activity and antibacterial efficiency among all of the synthesized L-BGs and M-BGs.
Abstract: The objective of this paper is to demonstrate and describe eight different types of power generation technologies and to understand the history and future trends of each technology. In addition, a comparative analysis between these technologies will be presented with respect to their cost analysis and associated performance.
Abstract: The operation of grid-connected inverters necessity a
single-phase phase locked loop (PLL) is proposed in this article to
accurately and quickly estimate and detect the grid phase angle. This
article presents the improvement of a method of phase-locked loop.
The novelty is to generate a method (PLL) of synchronizing the grid
with a Notch filter based on adaptive fuzzy logic for inverter systems
connected to the grid. The performance of the proposed method was
tested under normal and abnormal operating conditions (amplitude,
frequency and phase shift variations). In addition, simulation results
with ISPM software are developed to verify the effectiveness of the
proposed method strategy. Finally, the experimental test will be used
to extract the result and discuss the validity of the proposed algorithm.
Abstract: Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.