Search results for: wireless senor networks

Authors: Shahid M. Ali, Zakiullah

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An individual feed, smooth and smart, completely new shaped, dual band microstrip patch antenna has been proposed in this manuscript. Right here three triangular shape slots are usually presented in the 3 edges on the patch and along with a small feed line has utilized another edge on the patch to find out the dual band. The antenna carries a condensed framework wherever patch is around about 8.5mm by means of 7.96mm by means of 1.905mm leading to excellent bandwidths covering 13. 15 GHz to 13. 72 GHz in addition to 16.04 GHz to 16.58GHz. The return loss(RL) decrease in -19. 00dB and will be attained in the first resonant frequency at 13. 61 GHz and -28.69dB is at second resonance frequency at 16.33GHz. The stable average peak gain that may be observed along the operating band in lower and higher frequency is actually three. 53dB in addition to 5.562dB correspondingly. The radiation designs usually are omni directional along with moderate gain within equally most of these functioning bands. Accomplishment is proven within double frequencies at 13.62GHz since downlink in addition to 16.33GHz since uplink. This kind of low and simple configuration of the proposed antenna shows simplest fabrication and make it ensure that it is adaptable for your application within instant in satellite and as well as for the wireless communication system.

Keywords: dual band, microstrip patch antenna, HFSS, Ku band, satellite

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Authors: Loretta Ichim, Dan Popescu, Grigore Stamatescu

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The paper reports the collaboration between a top university from Romania and three companies for the implementation of a research project in a multidisciplinary domain, focusing on the impact and benefits both for the education and industry. The joint activities were developed under the Space Technology and Advanced Research Program (STAR), funded by the Romanian Space Agency (ROSA) for a university-industry partnership. The context was defined by linking the European Space Agency optional programs, with the development and promotion national research, with the educational and industrial capabilities in the aeronautics, security and related areas by increasing the collaboration between academic and industrial entities as well as by realizing high-level scientific production. The project name is Multisensory Robotic System for Aerial Monitoring of Critical Infrastructure Systems (MUROS), which was carried 2013-2016. The project included the University POLITEHNICA of Bucharest (coordinator) and three companies, which manufacture and market unmanned aerial systems. The project had as main objective the development of an integrated system for combined ground wireless sensor networks and UAV monitoring in various application scenarios for critical infrastructure surveillance. This included specific activities related to fundamental and applied research, technology transfer, prototype implementation and result dissemination. The core area of the contributions laid in distributed data processing and communication mechanisms, advanced image processing and embedded system development. Special focus is given by the paper to analyzing the impact the project implementation in the educational process, directly or indirectly, through the faculty members (professors and students) involved in the research team. Three main directions are discussed: a) enabling students to carry out internships at the partner companies, b) handling advanced topics and industry requirements at the master's level, c) experiments and concept validation for doctoral thesis. The impact of the research work (as the educational component) developed by the faculty members on the increasing performances of the companies’ products is highlighted. The collaboration between university and companies was well balanced both for contributions and results. The paper also presents the outcomes of the project which reveals the efficient collaboration between high education and industry: master thesis, doctoral thesis, conference papers, journal papers, technical documentation for technology transfer, prototype, and patent. The experience can provide useful practices of blending research and education within an academia-industry cooperation framework while the lessons learned represent a starting point in debating the new role of advanced research and development performing companies in association with higher education. This partnership, promoted at UE level, has a broad impact beyond the constrained scope of a single project and can develop into long-lasting collaboration while benefiting all stakeholders: students, universities and the surrounding knowledge-based economic and industrial ecosystem. Due to the exchange of experiences between the university (UPB) and the manufacturing company (AFT Design), a new project, SIMUL, under the Bridge Grant Program (Romanian executive agency UEFISCDI) was started (2016 – 2017). This project will continue the educational research for innovation on master and doctoral studies in MUROS thematic (collaborative multi-UAV application for flood detection).

Keywords: education process, multisensory robotic system, research and innovation project, technology transfer, university-industry partnership

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Authors: Mohsen Shirani, Micaela Demichela

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Food supply chain is one of the most complex supply chain networks due to its perishable nature and customer oriented products, and food safety is the major concern for this industry. IT system could help to minimize the production and consumption of unsafe food by controlling and monitoring the entire system. However, there have been many issues in adoption of IT system in this industry specifically within SMEs sector. With this regard, this study presents a novel approach to use IT and tractability systems in the food supply chain, using application of RFID and central database.

Keywords: food supply chain, IT system, safety, SME

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Authors: K. M. Jananni, R. Nandini

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The proposed wireless, inverter type design of a LPG leakage monitoring system aims to provide a smart and safe kitchen. The system detects the LPG gas leak using Nano-sensors and alerts the concerned individual through GSM system. The system uses two sensors, one attached to the chimney and other to the regulator of the LPG cylinder. Upon a leakage being detected, the sensor at the regulator actuates the system to cut off the gas supply immediately using a solenoid control valve. The sensor at the chimney checks for the permissible level of LPG mix in the air and when the level exceeds the threshold, the system sends an automatic SMS to the numbers saved. Further the sensor actuates the mini suction system fixed at the chimney within 20 seconds of a leakage to suck out the gas until the level falls well below the threshold. As a safety measure, an automatic window opening and alarm feature is also incorporated into the system. The key feature of this design is that the system is provided with a special inverter designed to make the device function effectively even during power failures. In this paper, utilization of sensors in the kitchen area is discussed and this gives the proposed architecture for real time field monitoring with a PIC Micro-controller.

Keywords: nano sensors, global system for mobile communication, GSM, micro controller, inverter

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Kristin Prentice, Carri Hand

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As the Canadian population ages and chronic health conditions continue to escalate, older adults will require various types of housing, such as long term care or retirement homes. Moving to a new home may require a change in leisure activities and social networks, which could be challenging to maintain identity and create a sense of home. Leisure has been known to help older adults maintain or increase their quality of life and life satisfaction and may help older adults in moving to new homes. Sense of home and identity within older adults' transitions to new homes are concepts that may also relate to leisure engagement. Literature is scant regarding the role of leisure in older adults moving to new homes and how the sense of home and identity inter-relate. This study aims to explore how leisure may play a role in older adults' transitioning to new homes, including how sense of home and identity inter-relate. An ethnographic approach will be used to understand the culture of older adults transitioning to new homes. This study will involve older adults who have recently relocated to a mid-sized city in Ontario, Canada. The study will focus on the older adult’s interactions with and connections to their home environment through leisure. Data collection will take place via video-conferencing and will include a narrative interview and two other interviews to discuss an activity diary of leisure engagement pre and post move and mental maps to capture spaces where participants engaged in leisure. Participants will be encouraged to share photographs of leisure engagement taken inside and outside their home to help understand the social spaces the participants refer to in their activity diaries and mental maps. Older adults attempt to adjust to their new homes by maintaining their identity, developing a sense of home through creating attachment to place, and maintaining social networks, all of which have been linked to engaging in leisure. This research will provide insight into the role of leisure in this transition process and the extent that the home and community can contribute to aiding their transition to the new home. This research will contribute to existing literature on the inter-relationships of leisure, sense of home, and identity and how they relate to older adults moving to new homes. This research also has potential for influencing policy and practice for meeting the housing needs of older adults.

Keywords: leisure, older adults, transition, identity

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Authors: R. Rezaeipour, A. R. Mehrabi

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Recent increase of the DGs and microgrids in distribution systems, disturbs the tradition structure of the system. Coordination between protection devices in such a system becomes the concern of the network operators. This paper presents a new method for fault location detection in the active distribution networks, independent of the fault type or its resistance. The method uses synchronized voltage and current measurements at the interconnection of DG units and is able to adapt to changes in the topology of the system. The method has been tested on a 38-bus distribution system, with very encouraging results.

Keywords: fault location detection, active distribution system, micro grids, network operators

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

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Authors: Roberto Cabezas H

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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Ying Li, Yan Li

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A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability.

Keywords: neural network, orthogonal basis extreme learning, function approximation

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Authors: Shazwanis Shukri

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The Mediterranean Sea, at the crossroads of three continents has always been the focus of pan-European and worldwide attention. Over the past decade, the Mediterranean Sea has become a hotbed for irregular migration particularly from the African continent toward the Europe. Among the major transit routes in the Mediterranean Sea include the Strait of Gibraltar, Canary Island and island of Lampedusa. In recent years, Mediterranean Sea has witnessed significant numbers of accidents and shipwrecks involving the irregular migrants and refugees trying to reach Europe via the sea. The shipwrecks and traffickers exploitation of migrants draw most of the attention particularly for the European Union (EU). This incident has been a wakeup call for the EU and become the top political agenda in the EU policy to tackle irregular migration and human smuggling at sea. EU has repeatedly addressed irregular migration as one of the threats the EU and its citizens may be confronted with and therefore immediate measures are crucial to tackle the crisis. In light of this, various initiatives have been adopted by the EU to strengthen external border control and restrict access to irregular migrants, notably through the enforcement of Frontex and Eunavfor Med. This paper analyses current development of counter-migration operations by the EU in response to migration crisis in the Mediterranean Sea. The analysis is threefold. First, this study examines the patterns and trends of irregular migration’s movements from recent perspective. Second, this study concentrates on the evolution of the EU operations that are in place in the Mediterranean Sea, notably by Frontex and Eunavfor Med to curb the influx of irregular migrants to the European countries, including, among others, Greece and Italy. Third, this study investigates the EU approaches to fight against the proliferation of human trafficking networks at sea. This study is essential to determine the roles of the EU in tackling migration crisis and human trafficking in the Mediterranean Sea and the effectiveness of their counter-migration operations to reduce the number of irregular migrants travelling via the sea. Elite interviews and document analysis were used as a methodology in this study. The study discovers that the EU operations have successfully contributed to reduce the numbers of irregular migrant’s arrival to Europe. The study also shows that the operations were effective to disrupt smugglers business models particularly from Libya. This study provides essential understanding about the roles of the EU not limited to tackle the migration crisis and disrupt trafficking networks, but also pledged to prevent further loss of lives at sea.

Keywords: European union, frontex, irregular migration, Mediterranean sea

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Authors: Masami Usui

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After 9/11, the American literary scene was confronted with new perspectives that enabled both writers and readers to recognize the hidden aspects of their political, economic, legal, social, and cultural phenomena. There appeared an argument over new and challenging multicultural aspects after 9/11 and this argument is presented by a tension of space related to 9/11. In Amy Waldman’s the Submission (2011), designing both the memorial park and the family network has a significant meaning in establishing the progress of understanding from multiple perspectives. The most intriguing and controversial topic of racism is reflected in the Submission, where one young architect’s blind entry to the competition for the memorial of Ground Zero is nominated, yet he is confronted with strong objections and hostility as soon as he turns out to be a Muslim named Mohammad Khan. This ‘Khan’ issue, immediately enlarged into a social controversial issue on American soil, causes repeated acts of hostility to Muslim women by ignorant citizens all over America. His idea of the park is to design a new concept of tracing the cultural background of the open space. Against his will, his name is identified as the ‘ingredient’ of the networking of the resistant community with his supporters: on the other hand, the post 9/11 hysteria and victimization is presented in such family associations as the Angry Family Members and Grieving Family Members. These rapidly expanding networks, whether political or not, constructed by the internet, embody the contemporary societal connection and representation. The contemporary quest for the significance of human relationships is recognized as a quest for global peace. Designing both the memorial park and the communication networks strengthens a process of facing the shared conflicts and healing the survivors’ trauma. The tension between the idea and networking of the Garden for the memorial site and the collapse of Ground Zero signifies the double mission of the site: to establish the space to ease the wounded and to remember the catastrophe. Reading the design of these icons of 9/11 in the Submission means that decoding the myth of globalization and its representations in this century.

Keywords: American literature, cultural studies, globalization, literature of catastrophe

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Authors: Eslam Mohammed Abdelkader, Mohamed Marzouk, Tarek Zayed

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Infrastructure systems are crucial to every aspect of life on Earth. Existing Infrastructure is subjected to degradation while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges play a crucial role in urban transportation networks. Moreover, they are subjected to high level of deterioration because of the variable traffic loading, extreme weather conditions, cycles of freeze and thaw, etc. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays especially in the large transportation networks due to the huge variance between the need for maintenance actions, and the available funds to perform such actions. Deterioration models represent a very important aspect for the effective use of BMSs. This paper presents a probabilistic time-based model that is capable of predicting the condition ratings of the concrete bridge decks along its service life. The deterioration process of the concrete bridge decks is modeled using semi-Markov process. One of the main challenges of the Markov Chain Decision Process (MCDP) is the construction of the transition probability matrix. Yet, the proposed model overcomes this issue by modeling the sojourn times based on some probability density functions. The sojourn times of each condition state are fitted to probability density functions based on some goodness of fit tests such as Kolmogorov-Smirnov test, Anderson Darling, and chi-squared test. The parameters of the probability density functions are obtained using maximum likelihood estimation (MLE). The condition ratings obtained from the Ministry of Transportation in Quebec (MTQ) are utilized as a database to construct the deterioration model. Finally, a comparison is conducted between the Markov Chain and semi-Markov chain to select the most feasible prediction model.

Keywords: bridge management system, bridge decks, deterioration model, Semi-Markov chain, sojourn times, maximum likelihood estimation

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Authors: Yuling Cui, Danhao Deng, Chaowei Wang, Weidong Wang

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In recent years, unmanned aerial vehicles (UAVs) have been widely used in wireless communication, attracting more and more attention from researchers. UAVs can not only serve as a relay for auxiliary communication but also serve as an aerial base station for ground users (GUs). However, limited energy means that they cannot work all the time and cover a limited range of services. In this paper, we investigate 2D UAV trajectory design and power allocation in order to maximize the UAV's service time and downlink throughput. Based on deep reinforcement learning, we propose a depth deterministic strategy gradient algorithm for trajectory design and power distribution (TDPA-DDPG) to solve the energy-efficient and communication service quality problem. The simulation results show that TDPA-DDPG can extend the service time of UAV as much as possible, improve the communication service quality, and realize the maximization of downlink throughput, which is significantly improved compared with existing methods.

Keywords: UAV trajectory design, power allocation, energy efficient, downlink throughput, deep reinforcement learning, DDPG

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Robert Lenzie

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Transcranial electrical stimulation (tES) is significant in the research literature. However, the effects of tES on brain activity are still poorly understood at the surface level, the Brodmann Area level, and the impact on neural networks. Using a method like electroencephalography (EEG) in conjunction with tES might make it possible to comprehend the brain response and mechanisms behind published observed alterations in more depth. Using a method to directly see the effect of tES on EEG may offer high temporal resolution data on the brain activity changes/modulations brought on by tES that correlate to various processing stages within the brain. This paper provides unpublished information on a cutting-edge methodology that may reveal details about the dynamics of how the human brain works beyond what is now achievable with existing methods.

Keywords: tACS, frequency, EEG, optimal

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: C. Busuioc, I. Stancu, A. Nicoara, A. Zamfirescu, A. Evanghelidis

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The field of tissue engineering has expanded its potential due to the use of composite biomaterials belonging to increasingly complex systems, leading to bone substitutes with properties that are continuously improving to meet the patient's specific needs. Furthermore, the development of biomaterials based on ceramic and polymeric phases is an unlimited resource for future scientific research, with the final aim of restoring the original tissue functionality. Thus, in the first stage, composite scaffolds based on polycaprolactone (PCL) or polylactic acid (PLA) and inorganic powders were prepared by employing the electrospinning technique. The targeted powders were: commercial and laboratory synthesized hydroxyapatite (HAp), as well as barium titanate (BT). By controlling the concentration of the powder within the precursor solution, together with the processing parameters, different types of three-dimensional architectures were achieved. In the second stage, both the mineral powders and hybrid composites were investigated in terms of composition, crystalline structure, and microstructure so that to demonstrate their suitability for tissue engineering applications. Regarding the scaffolds, these were proven to be homogeneous on large areas and loaded with mineral particles in different proportions. The biological assays demonstrated that the addition of inorganic powders leads to modified responses in the presence of simulated body fluid (SBF) or cell cultures. Through SBF immersion, the biodegradability coupled with bioactivity were highlighted, with fiber fragmentation and surface degradation, as well as apatite layer formation within the testing period. Moreover, the final composites represent supports accepted by the cells, favoring implant integration. Concluding, the purposed fibrous materials based on bioresorbable polymers and mineral powders, produced by the electrospinning technique, represent candidates with considerable potential in the field of tissue engineering. Future improvements can be attained by optimizing the synthesis process or by simultaneous incorporation of multiple inorganic phases with well-defined biological action in order to fabricate multifunctional composites.

Keywords: barium titanate, electrospinning, fibre networks, hydroxyapatite, smart scaffolds

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Authors: M. Tsebia, H. Bentarzi

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In the inter-connected power systems, a phenomenon called inter-area oscillation may be caused by several defects. In this paper, a study of the Maghreb countries inter-area power networks oscillation has been investigated. The inter-area oscillation monitoring can be enhanced by integrating Phasor Measurement Unit (PMU) technology installed in different places. The data provided by PMU and recorded by PDC will be used for the monitoring, analysis, and control purposes. The proposed approach has been validated by simulation using MATLAB/Simulink.

Keywords: PMU, inter-area oscillation, Maghrebian power system, Simulink

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: A. Assad, T. Zayed

Abstract:

Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

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Authors: Amel Ltifi, Ahmed Zouinkhi, Mohamed Salim Bouhlel

Abstract:

Security and trust management in Vehicular Ad-hoc NETworks (VANET) is a crucial research domain which is the scope of many researches and domains. Although, the majority of the proposed trust management systems for VANET are based on specific road infrastructure, which may not be present in all the roads. Therefore, road security should be managed by vehicles themselves. In this paper, we propose a new Self Organized Trust Management system (SOTM). This system has the responsibility to cut with the spread of false warnings in the network through four principal components: cooperation, trust management, communication and security.

Keywords: ative vehicle, cooperation, trust management, VANET

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Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

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Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

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Authors: Alberto Azevedo, Ricardo Martins, André B. Fortunato, Anabela Oliveira

Abstract:

Water is under severe threat today because of the rising population, increased agricultural and industrial needs, and the intensifying effects of climate change. Due to sea-level rise, erosion, and demographic pressure, the coastal regions are of significant concern to the scientific community. The Water Monitoring Sentinel Cloud platform (WORSICA) service is focused on providing new tools for monitoring water in coastal and inland areas, taking advantage of remote sensing, in situ and tidal modeling data. WORSICA is a service that can be used to determine the coastline, coastal inundation areas, and the limits of inland water bodies using remote sensing (satellite and Unmanned Aerial Vehicles - UAVs) and in situ data (from field surveys). It applies to various purposes, from determining flooded areas (from rainfall, storms, hurricanes, or tsunamis) to detecting large water leaks in major water distribution networks. This service was built on components developed in national and European projects, integrated to provide a one-stop-shop service for remote sensing information, integrating data from the Copernicus satellite and drone/unmanned aerial vehicles, validated by existing online in-situ data. Since WORSICA is operational using the European Open Science Cloud (EOSC) computational infrastructures, the service can be accessed via a web browser and is freely available to all European public research groups without additional costs. In addition, the private sector will be able to use the service, but some usage costs may be applied, depending on the type of computational resources needed by each application/user. Although the service has three main sub-services i) coastline detection; ii) inland water detection; iii) water leak detection in irrigation networks, in the present study, an application of the service to Óbidos lagoon in Portugal is shown, where the user can monitor the evolution of the lagoon inlet and estimate the topography of the intertidal areas without any additional costs. The service has several distinct methodologies implemented based on the computations of the water indexes (e.g., NDWI, MNDWI, AWEI, and AWEIsh) retrieved from the satellite image processing. In conjunction with the tidal data obtained from the FES model, the system can estimate a coastline with the corresponding level or even topography of the inter-tidal areas based on the Flood2Topo methodology. The outcomes of the WORSICA service can be helpful for several intervention areas such as i) emergency by providing fast access to inundated areas to support emergency rescue operations; ii) support of management decisions on hydraulic infrastructures operation to minimize damage downstream; iii) climate change mitigation by minimizing water losses and reduce water mains operation costs; iv) early detection of water leakages in difficult-to-access water irrigation networks, promoting their fast repair.

Keywords: remote sensing, coastline detection, water detection, satellite data, sentinel, Copernicus, EOSC

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Niels Grataloup, Micael S. Couceiro, Manousos Valyrakis, Javier Escudero, Patricia A. Vargas

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This paper benchmarks the possible underwater communication technologies that can be integrated into a swarm of underwater robots by proposing an underwater robot simulator named CORSICA (Cross platfORm wireleSs communICation simulator). Underwater exploration relies increasingly on the use of mobile robots, called Autonomous Underwater Vehicles (AUVs). These robots are able to reach goals in harsh underwater environments without resorting to human divers. The introduction of swarm robotics in these scenarios would facilitate the accomplishment of complex tasks with lower costs. However, swarm robotics requires implementation of communication systems to be operational and have a non-deterministic behaviour. Inter-robot communication is one of the key challenges in swarm robotics, especially in underwater scenarios, as communication must cope with severe restrictions and perturbations. This paper starts by presenting a list of the underwater propagation models of acoustic and electromagnetic waves, it also reviews existing transmitters embedded in current robots and simulators. It then proposes CORSICA, which allows validating the choices in terms of protocol and communication strategies, whether they are robot-robot or human-robot interactions. This paper finishes with a presentation of possible integration according to the literature review, and the potential to get CORSICA at an industrial level.

Keywords: underwater simulator, robot-robot underwater communication, swarm robotics, transceiver and communication models

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

Abstract:

Visible light communication is an emerging technology for almost a decade now; there is a growing need for VLC systems to overcome the challenges faced by radio frequency RF communication systems. With the advancement in the development of solid-state sources, in the future would replace incandescent and fluorescent light sources. These solid-state devices are not only to be used for illumination but can also be employed for communication and navigational purposes. The replacement of conventional illumination sources with highly efficient light-emitting diodes (LED's) (generally white light) will reduce energy consumption as well as environmental pollution. White LEDs dissipate very less power as compared to conventional light sources. The use of LED's is not only beneficial in terms of power consumption, but it also has an intrinsic capability for indoor wireless communication as compared to indoor RF communication. It is considerably low in cost to operate than the RF systems such as Wi-Fi routers, allows convenient means of reusing the bandwidth, and there is a huge potential for high data rate transmissions with enhanced data security. This paper provides an overview of some of the current challenges with VLC and proposes a possible solution to deal with these challenges; it also examines some joint protocols to optimize the joint illumination and communication functionality.

Keywords: visible light communication, line of sight, root mean square delay spread, light emitting diodes

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Authors: Yo-Sheng Lin, Chien-Chin Wang, Yun-Wen Lin, Chien-Yo Lee

Abstract:

A wide band power amplifier (PA) for 60 GHz and 77 GHz direct-conversion transceiver using standard 90 nm CMOS technology is reported. The PA comprises a cascode input stage with a wide band T-type input-matching network and inductive interconnection and load, followed by a common-source (CS) gain stage and a CS output stage. To increase the saturated output power (PSAT) and power-added efficiency (PAE), the output stage adopts a two-way power dividing and combining architecture. Instead of the area-consumed Wilkinson power divider and combiner, miniature low-loss transmission-line inductors are used at the input and output terminals of each of the output stages for wide band input and output impedance matching to 100 ohm. This in turn results in further PSAT and PAE enhancement. The PA consumes 92.2 mW and achieves maximum power gain (S21) of 23.3 dB at 56 GHz, and S21 of 21.7 dB and 14 dB, respectively, at 60 GHz and 77 GHz. In addition, the PA achieves excellent saturated output power (PSAT) of 10.6 dB and maximum power added efficiency (PAE) of 12.3% at 60 GHz. At 77 GHz, the PA achieves excellent PSAT of 10.4 dB and maximum PAE of 6%. These results demonstrate the proposed wide band PA architecture is very promising for 60 GHz wireless personal local network (WPAN) and 77 GHz automobile radar systems.

Keywords: 60 GHz, 77 GHz, PA, WPAN, automotive radar

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