Search results for: temporal convolutional net

Authors: Sulaiman Al Amro

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There are currently considerable challenges concerning data security and privacy, particularly in relation to modern technologies. This includes the virtual world known as the Metaverse, which consists of a virtual space that integrates various technologies and is therefore susceptible to cyber threats such as malware, phishing, and identity theft. This has led recent studies to propose the development of Metaverse forensic frameworks and the integration of advanced technologies, including machine learning for intrusion detection and security. In this context, the application of first-order logic offers a formal and systematic approach to defining the conditions of cyberattacks, thereby contributing to the development of effective detection mechanisms. In addition, formalizing the rules and patterns of cyber threats has the potential to enhance the overall security posture of the Metaverse and, thus, the integrity and safety of this virtual environment. The current paper focuses on the primary actions employed by avatars for potential attacks, including Interval Temporal Logic (ITL) and behavior-based detection to detect an avatar’s abnormal activities within the Metaverse. The research established that the proposed framework attained an accuracy of 92.307%, resulting in the experimental results demonstrating the efficacy of ITL, including its superior performance in addressing the threats posed by avatars within the Metaverse domain.

Keywords: security, privacy, metaverse, cyberattacks, detection, first-order logic

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Jörg Lenhardt, Wolfram Schiffmann, Jörg Keller

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While the feature sizes of recent Complementary Metal Oxid Semiconductor (CMOS) devices decrease the influence of static power prevails their energy consumption. Thus, power savings that benefit from Dynamic Frequency and Voltage Scaling (DVFS) are diminishing and temporal shutdown of cores or other microchip components become more worthwhile. A consequence of powering off unused parts of a chip is that the relative difference between idle and fully loaded power consumption is increased. That means, future chips and whole server systems gain more power saving potential through power-aware load balancing, whereas in former times this power saving approach had only limited effect, and thus, was not widely adopted. While powering off complete servers was used to save energy, it will be superfluous in many cases when cores can be powered down. An important advantage that comes with that is a largely reduced time to respond to increased computational demand. We include the above developments in a server power model and quantify the advantage. Our conclusion is that strategies from datacenters when to power off server systems might be used in the future on core level, while load balancing mechanisms previously used at core level might be used in the future at server level.

Keywords: power efficiency, static power consumption, dynamic power consumption, CMOS

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Kazi Md. Shifun Newaz, S. Miaji, Shahnewaz Hazanat-E-Rabbi

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In this study, the kernel density estimation tool has been used to identify most crash prone locations in a national highway of Bangladesh. Like other developing countries, in Bangladesh road traffic crashes (RTC) have now become a great social alarm and the situation is deteriorating day by day. Today’s black spot identification process is not based on modern technical tools and most of the cases provide wrong output. In this situation, characteristic analysis and black spot identification by spatial analysis would be an effective and low cost approach in ensuring road safety. The methodology of this study incorporates a framework on the basis of spatial-temporal study to identify most RTC occurrence locations. In this study, a very important and economic corridor like Dhaka to Sylhet highway has been chosen to apply the method. This research proposes that KDE method for identification of Hazardous Road Location (HRL) could be used for all other National highways in Bangladesh and also for other developing countries. Some recommendations have been suggested for policy maker to reduce RTC in Dhaka-Sylhet especially in black spots.

Keywords: hazardous road location (HRL), crash, GIS, kernel density

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Authors: Luna Moni Das

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This paper is an attempt to study the channel dynamics in the area bounded by the foothills of the eastern Himalayas in the north, the Brahmaputra in the south and southeast and eastern side and the Subansiri River in the west. There are many streams in this region and only a few are perennial. There are two major anabranches of the Brahmaputra called Kharkutia Xuti and Charikoria. All of these makes it a very dynamic area. The analysis done in this paper is based on the remote sensing data and mapping of the channel planforms in GIS environment. The temporal trend of the change in channel planform has been produced. This study shows that, during the period from 1973 to 2013, the streams/rivers originating in the north have experienced a reduction in the total length. The other most important result is that even though the western edge of Majuli Island is eroding faster there is a formation of a larger island in between Charikoria and Brahmaputra, that comprises of Majuli island and parts of Dhakuakhana subdivision of Lakhimpur District along the south of Charikoria river. The field study shows that the Kharkutia Xuti, that divides Majuli from Dhakuakhana, do not experience any flow from the Brahmaputra for the major portion of the year and Charikoria has developed as a major anabranch of the Brahmaputra.

Keywords: channel dynamics, Brahmaputra river, Majuli Island, sinuosity

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Authors: Mansha Kotwani

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The specific spatio-temporal calcium concentration patterns are required by the fibroblasts to maintain its structure and functions. Thus, calcium concentration is regulated in cell at different levels in various activities of the cell. The variations in cytosolic calcium concentration largely depend on the buffers present in cytosol and influx of calcium into cytosol from ER through IP3Rs or Raynodine receptors followed by reuptake of calcium into ER through sarcoplasmic/endoplasmic reticulum ATPs (SERCA) pump. In order to understand the mechanisms of wound repair, tissue remodeling and growth performed by fibroblasts, it is of crucial importance to understand the mechanisms of calcium concentration regulation in fibroblasts. In this paper, a model has been developed to study calcium distribution in NRK fibroblast in the presence of buffers and ER flux with SERCA pump. The model has been developed for two dimensional unsteady state case. Appropriate initial and boundary conditions have been framed along with physiology of the cell. Finite element technique has been employed to obtain the solution. The numerical results have been used to study the effect of buffers, ER flux and source amplitude on calcium distribution in fibroblast cell.

Keywords: buffers, IP3R, ER flux, SERCA pump, source amplitude

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Roland Benedict Reyes, Marc Edsel Ayes, Regina Berba, Cybele Lara Abad

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Background: Three AIDS-defining malignancies have been associated with the human immunodeficiency virus (HIV): Kaposi’s sarcoma, non-Hodgkin’s lymphoma, and cervical carcinoma. However, new cases of non-AIDS defining malignancies also have been increasingly associated with HIV. One of these is a rare intracranial malignancy, meningeal hemangiopericyotma. Case Description: A 32-year old HIV-positive male, not on highly active antiretroviral therapy, was admitted to our hospital due to generalized weakness and sudden onset hearing loss. Cranial MRI was done, which revealed a temporal nodule with the following considerations: granuloma, meningioma or metastases. A craniotomy was performed and the mass excised. Results from the biopsy showed meningeal hemangiopericytoma. The patient was then started on antiretroviral therapy (Lamivudine, Tenofovir, and Efavirenz) and was discharged for radiation therapy and metastatic work-up as an outpatient. On follow-up seven months later, metastatic work up revealed multiple hepatic foci not previously documented suggestive of metastasis short of biopsy sampling. Conclusions: This case of an intracranial hemangiopericytoma in an HIV-positive patient is the second case thus far presented, based on our systematic and extensive search of the literature.

Keywords: Hemangiopericytoma, Human Immunodeficiency Virus, Meningeal hemangiopericytoma, Neoplasm

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Authors: Arunas Stirke, Neringa Bakute, Gatis Mozolevskis

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A technology of microfluidics is an emerging tool in the field of biology, medicine and chemistry. Microfluidic device is also known as ‘lab-on-a-chip’ technology [1]. In moving from macro- to microscale, there is unprecedented control over spatial and temporal gradients and patterns that cannot be captured in conventional Petri dishes and well plates [2]. However, there is not a single standard microfluidic chip designated for all purposes – every different field of studies needs a specific microchip with certain geometries, inlet/outlet, channel depth and other parameters to precisely regulate the required function. Since our group is studying an effect of pulsed electric field (PEF) to the cells, we have manufactured a microfluidic chip designated for high-throughput electroporation of cells. In our microchip, a cell culture chamber is divided into two parallel channels by a membrane, meanwhile electrodes for electroporation are attached to the wall of the channels. Both microchannels have their own inlet and outlet, enabling injection of transfection material separately. Our perspective is to perform electroporation of mammalian cells in two different ways: (1) plasmid and cells are injected in the same microchannel and (2) injected into separate microchannels. Moreover, oxygen and pH sensors are integrated on order to analyse cell viability parameters after PEF treatment.

Keywords: microfluidics, chip, fabrication, electroporation

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Authors: Sara Patricia Ibarra-Zavaleta, Rabindranarth Romero-Lopez, Rosario Langrave, Annie Poulin, Gerald Corzo, Mathias Glaus, Ricardo Vega-Azamar, Norma Angelica Oropeza

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The progressive change in climatic conditions worldwide has increased frequency and severity of extreme hydrometeorological events (EHE). Mexico is an example; this has been affected by the presence of EHE leaving economic, social and environmental losses. The objective of this research was to apply a Canadian distributed hydrological model (DHM) to tropical conditions and to evaluate its capacity to predict flows in a basin in the central Gulf of Mexico. In addition, the DHM (once calibrated and validated) was used to calculate the theoretical hydraulic power and the performance to predict streamflow before the presence of an EHE. The results of the DHM show that the goodness of fit indicators between the observed and simulated flows in the calibration process (NSE=0.83, RSR=0.021 and BIAS=-4.3) and validation: temporal was assessed at two points: point one (NSE=0.78, RSR=0.113 and BIAS=0.054) and point two (NSE=0.825, RSR=0.103 and BIAS=0.063) are satisfactory. The DHM showed its applicability in tropical environments and its ability to characterize the rainfall-runoff relationship in the study area. This work can serve as a tool for identifying vulnerabilities before floods and for the rational and sustainable management of water resources.

Keywords: HYDROTEL, hydraulic power, extreme hydrometeorological events, streamflow

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, prior distribution and approximate posterior distribution, KTH dataset

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Authors: Musadiq Idris, Sajjad Ali, Syed A. Khaliq, Umer Farooq

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Postmortem record of 217 captive ungulates including Black-buck (n=31), Chinkara (n=20), Hog deer (n=116), Spotted deer (n=35), Red Deer n=(04), and Rusa deer (n=11) submitted to the Veterinary Research Institute, Lahore, Pakistan was analyzed to determine the primary cause of mortality in these animals. The submissions included temporal distribution from Government wildlife captive farms, zoo, and private ownerships, over a three year period (2007-2009). The most common cause of death was found to be trauma (20.27%), followed by parasitic diseases (15.67%), bacterial diseases (11.98%), stillbirths (9.21%), snakebites (2.76%), gut affections (2.30%), neoplasia (1.38%) and starvation (0.92%). The exact cause of death could not be determined in 77 of 217 animals. Pneumonia (8.29%) and tuberculosis (3.69%) were the most common bacterial diseases. Analyses for parasitic infestation revealed tapeworms to be highest (11.05%), followed by roundworms (8.29%) and hemoparasitism (5.07%) (babesiosis and theileriosis). The mortality rate in young ungulates was lower as compared to adults (32.26% and 67.74%). Gender wise data presented higher mortality in females (55.30%) compared to males (44.70%). In conclusion, highest mortality factor in captive ungulates was trauma, followed by parasitic and bacterial infestations/infections of tapeworms and pneumonia, respectively. Furthermore, necropsies provided substantial information on etiology of death and other related epidemiological aspects.

Keywords: age, epidemiology, gender, mortality, ungulates

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Yingchen Yang

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In the present work, development of a new vertical-axis unidirectional wave rotor is reported. The wave rotor is a key component of a wave energy converter (WEC), which harvests energy from ocean waves. Differing from the huge majority of WEC designs that perform reciprocating motions (heaving up and down, swaying back and forth, etc.), our wave rotor performs unidirectional rotation about a vertical axis when directly exposed in waves. The unidirectional feature of the rotor makes the rotor respond well in a wide range of the wave frequency. The vertical axis arrangement of the rotor makes the rotor insensitive to the wave propagation direction. The rotor employs blades with a cross-section in an airfoil shape and a span curled into a semi-oval shape. Two sets of blades, with one nested inside the other, constitute the rotor. In waves, water particles perform an omnidirectional motion that constantly changes in both spatial and temporal domains. The blade nesting permits a compact rotor configuration that ‘sees’ a relatively uniform local flow in the spatial domain. The rotor was experimentally tested in simulated waves in a wave flume under various conditions. The testing results show a promising unidirectional rotor that is capable of extracting energy from waves at a capture width ratio of 0.08 to 0.15, depending on detailed wave conditions.

Keywords: unidirectional, vertical axis, wave energy converter, wave rotor

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Authors: Ritesh Verma, Manisha Rathi, Chand Singh Dhull, Sumit Sachdeva, Jitender Phogat

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A Rathke's cleft cyst is a benign growth found on the pituitary gland in the brain, specifically a fluid-filled cyst in the posterior portion of the anterior pituitary gland. It occurs when the Rathke's pouch does not develop properly and ranges in size from 2 to 40mm in diameter. A 38-year-old male presented to the outpatient department with loss of vision in the inferior quadrant of the left eye since 15 days. Visual acuity was 6/6 in the right eye and 6/9 in the left eye. Visual field analysis by HFA-24-2 revealed an inferior field defect extending to the supero-temporal quadrant in the left eye. MRI brain and orbit was advised to the patient and it revealed a well defined cystic pituitary adenoma indenting left optic nerve near optic chiasm consistent with the diagnosis of Rathke’s cleft cyst (RCC). The patient was referred to neurosurgery department for further management. Symptoms vary greatly between individuals having RCCs. RCCs can be non-functioning, functioning, or both. Besides headaches, neurocognitive deficits are almost always present but have a high rate of immediate reversal if the cyst is properly treated or drained.

Keywords: pituitary tumors, rathke’s cleft cyst, visual field defects, vision loss

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Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

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Authors: Andrew R. Winters, Gregor J. Gassner

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A high-order numerical magentohydrodynamics (MHD) solver built upon a non-linear entropy stable numerical flux function that supports eight traveling wave solutions will be described. The method is designed to treat the divergence-free constraint on the magnetic field in a similar fashion to a hyperbolic divergence cleaning technique. The solver is especially well-suited for flows involving strong discontinuities due to its strong stability without the need to enforce artificial low density or energy limits. Furthermore, a new formulation of the numerical algorithm to guarantee positivity of the pressure during the simulation is described and presented. By construction, the solver conserves mass, momentum, and energy and is entropy stable. High spatial order is obtained through the use of a third order limiting technique. High temporal order is achieved by utilizing the family of strong stability preserving (SSP) Runge-Kutta methods. Main attributes of the solver are presented as well as details on an implementation of the new solver into the multi-physics, multi-scale simulation code FLASH. The accuracy, robustness, and computational efficiency is demonstrated with a variety of numerical tests. Comparisons are also made between the new solver and existing methods already present in FLASH framework.

Keywords: entropy stability, finite volume scheme, magnetohydrodynamics, pressure positivity

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Authors: Mohammad Bakhshi, Firas Al Janabi

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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.

Keywords: DiMoN Tool, disaggregation, exceedance probability, Kolmogorov-Smirnov test, rainfall

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Authors: Hesham Osman

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The challenges facing aging urban infrastructure systems require a more holistic and comprehensive approach to their management. The large number of urban infrastructure renewal activities occurring in cities throughout the world leads to social, economic and environmental impacts on the communities in its vicinity. As such, a coordinated effort is required to streamline these activities. This paper presents a framework to enable temporal (time-based) coordination of water, sewer and road intervention activities. Intervention activities include routine maintenance, renewal, and replacement of physical assets. The coordination framework considers 1) Life-cycle costs, 2) Infrastructure level-of-service, and 3) Risk exposure to system operators. The model enables infrastructure asset managers to trade-off options of delaying versus bringing forward intervention activities of one system in order to be executed in conjunction with another co-located system in the right-of-way. The framework relies on a combination of meta-heuristics and goal-based optimization. In order to demonstrate the applicability of the framework, a case study for a major infrastructure corridor in Cairo, Egypt is taken as an example. Results show that the framework can be scaled-up to include other infrastructure systems located in the right-of-way like electricity, gas and telecom, provided that information can be shared among these entities.

Keywords: infrastructure, rehabilitation, construction, optimization

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Khalida S. Rijab, Mohammed E. Safi, Ayad A. Ibrahim

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When electrical electrodes are mounted on the skin surface of the muscle, a signal is detected when a skeletal muscle undergoes contraction; the signal is known as surface electromyographic signal (EMG). This signal has a noise-like interference pattern resulting from the temporal and spatial summation of action potentials (AP) of all active motor units (MU) near electrode detection. By appropriate processing (Decomposition), the surface EMG signal may be used to give an estimate of motor unit action potential. In this work, a denoising technique is applied to the MUAP signals extracted from the spatial filter (IB2). A set of signals from a non-invasive two-dimensional grid of 16 electrodes from different types of subjects, muscles, and sex are recorded. These signals will acquire noise during recording and detection. A digital fourth order band- pass Butterworth filter is used for denoising, with a tuned band-pass frequency of suitable choice of cutoff frequencies is investigated, with the aim of obtaining a suitable band pass frequency. Results show an improvement of (1-3 dB) in the signal to noise ratio (SNR) have been achieved, relative to the raw spatial filter output signals for all cases that were under investigation. Furthermore, the research’s goal included also estimation and reconstruction of the mean shape of the MUAP.

Keywords: EMG, Motor Unit, Digital Filter, Denoising

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Authors: Shaker Alsharif, Xavier Banquy

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Controlled drug delivery technology represents one of the most rapidly advancing areas of science in which chemists and chemical engineers are contributing to human health care. Such delivery systems provide numerous advantages compared to conventional dosage forms including improved efficacy, and improved patient compliance and convenience. Such systems often use synthetic polymers as carriers for the drugs. As a result, treatments that would not otherwise be possible are now in conventional use. The role of bilayered vesicles as efficient carriers for drugs, vaccines, diagnostic agents and other bioactive agents have led to a rapid advancement in the liposomal drug delivery system. Moreover, the site avoidance and site-specific drug targeting therapy could be achieved by formulating a liposomal product, so as to reduce the cytotoxicity of many potent therapeutic agents. Our project focuses on developing and building hydrogel with nanoinclusion of liposomes loaded with active compounds such as proteins and growth factors able to release them in a controlled fashion. In order to achieve that, we synthesize several liposomes of two different phospholipids concentrations encapsulating model drug. Then, formulating hydrogel with specific mechanical properties embedding the liposomes to manage the release of active compound.

Keywords: controlled release, hydrogel, liposomes, active compounds

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Wenting Zhang, Shishi Liu, Peihong Fu

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As an indicator of air quality and directly related to concentration of ground PM2.5, the spatial-temporal variation and impact factor analysis of Aerosol Optical Depth (AOD) have been a hot spot in air pollution. This paper concerns the non-stationarity and the autocorrelation (with Moran’s I index of 0.75) of the AOD in Wuhan agglomeration (WHA), in central China, uses the geographically weighted regression (GRW) to identify the spatial relationship of AOD and its impact factors. The 3 km AOD product of Moderate Resolution Imaging Spectrometer (MODIS) is used in this study. Beyond the economic-social factor, land use density factors, vegetable cover, and elevation, the landscape metric is also considered as one factor. The results suggest that the GWR model is capable of dealing with spatial varying relationship, with R square, corrected Akaike Information Criterion (AICc) and standard residual better than that of ordinary least square (OLS) model. The results of GWR suggest that the urban developing, forest, landscape metric, and elevation are the major driving factors of AOD. Generally, the higher AOD trends to located in the place with higher urban developing, less forest, and flat area.

Keywords: aerosol optical depth, geographically weighted regression, land use change, Wuhan agglomeration

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Authors: Chunhong Zhao

Abstract:

Remotely sensed land surface temperature (LST) is vital to understand the land-atmosphere energy balance, hydrological cycle, and thus is widely used to describe the urban heat island (UHI) phenomenon. However, due to technical constraints, satellite thermal sensors are unable to provide LST measurement with both high spatial and high temporal resolution. Despite different downscaling techniques and algorithms to generate high spatiotemporal resolution LST. Four major metropolitan areas in Texas, USA: Dallas-Fort Worth, Houston, San Antonio, and Austin all demonstrate UHI effects. Different cities are expected to have varying SUHI effect during the urban development trajectory. With the help of the Landsat, ASTER, and MODIS archives, this study focuses on the spatial patterns of UHIs and the seasonal and annual variation of these metropolitan areas. With Gaussian model, and Local Indicators of Spatial Autocorrelations (LISA), as well as data fusion methods, this study identifies the hotspots and the trajectory of the UHI phenomenon of the four cities. By making comparison analysis, the result can help to alleviate the advent effect of UHI and formulate rational urban planning in the long run.

Keywords: spatiotemporal analysis, land surface temperature, urban heat island evaluation, metropolitan areas of Texas, USA

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Authors: Leila Momenzadeh, Alexander V. Evteev, Elena V. Levchenko, Tanvir Ahmed, Irina Belova, Graeme Murch

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In this work, the phonon thermal conductivity of f.c.c. Al is investigated in detail in the temperature range 100 – 900 K within the framework of equilibrium molecular dynamics simulations making use of the Green-Kubo formalism and one of the most reliable embedded-atom method potentials. It is found that the heat current auto-correlation function of the f.c.c. Al model demonstrates a two-stage temporal decay similar to the previously observed for f.c.c Cu model. After the first stage of decay, the heat current auto-correlation function of the f.c.c. Al model demonstrates a peak in the temperature range 100-800 K. The intensity of the peak decreases as the temperature increases. At 900 K, it transforms to a shoulder. To describe the observed two-stage decay of the heat current auto-correlation function of the f.c.c. Al model, we employ decomposition model recently developed for phonon-mediated thermal transport in a monoatomic lattice. We found that the electronic contribution to the total thermal conductivity of f.c.c. Al dominates over the whole studied temperature range. However, the phonon contribution to the total thermal conductivity of f.c.c. Al increases as temperature decreases. It is about 1.05% at 900 K and about 12.5% at 100 K.

Keywords: aluminum, gGreen-Kubo formalism, molecular dynamics, phonon thermal conductivity

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

Abstract:

The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

Procedia PDF Downloads 293