Search results for: neural signal recording

Authors: Manjitsingh Rajput

Abstract:

Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Maria Nejjari, Michel Cloutier, Guylaine Talbot, Martin Lanthier

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The fluorescence in situ hybridization (FISH) is a staining technique that allows the identification, detection and quantification of microorganisms without prior cultivation by means of epifluorescence and confocal laser scanning microscopy (CLSM). Oligonucleotide probes have been used to detect bacteria and archaea that colonize the cattle and swine digestive systems. These bacterial strains have been obtained from fecal samples issued from cattle manure and swine slurry. The collection of these samples has been done at 3 different pit’s levels A, B and C with same height. Two collection depth levels have been taken in consideration, one collection level just under the pit’s surface and the second one at the bottom of the pit. Cells were fixed and FISH was performed using oligonucleotides of 15 to 25 nucleotides of length associated with a fluorescent molecule Cy3 or Cy5. The double hybridization using Cy3 probe targeting bacteria (Cy3-EUB338-I) along with a Cy5 probe targeting Archaea (Gy5-ARCH915) gave a better signal. The CLSM images show that there are more bacteria than archaea in swine slurry. However, the choice of fluorescent probes is critical for getting the double hybridization and a unique signature for each microorganism. FISH technique is an easy way to detect pathogens like E. coli O157, Listeria, Salmonella that easily contaminate water streams, agricultural soils and, consequently, food products and endanger human health.

Keywords: archaea, bacteria, detection, FISH, fluorescence

Procedia PDF Downloads 376

Authors: Oktira Roka Aji, Maelita R. Moeis, Ihsanawati, Ernawati A. Giri-Rachman

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Globally, tuberculosis (TB) remains a leading cause of death. The emergence of multidrug-resistant strains and extensively drug-resistant strains have become a major public concern. One of the potential candidates for drug target is the cytoplasmic domain of PhoR Histidine Kinase, a part of the Two Component System (TCS) PhoR-PhoP in Mycobacterium tuberculosis (Mtb). TCS PhoR-PhoP relay extracellular signal to control the expression of 114 virulent associated genes in Mtb. The 3D structure of PhoR cytoplasmic domain is needed to screen novel drugs using structure based drug discovery. The PhoR cytoplasmic domain from Mtb H37Rv was overexpressed in E. coli BL21(DE3), then purified using IMAC Ni-NTA Agarose his-tag affinity column and DEAE-ion exchange column chromatography. The molecular weight of the purified protein was estimated to be 37 kDa after SDS-PAGE analysis. This sample was used for pre-crystallization screening by applying sitting drop vapor diffusion method using Natrix (HR2-116) 48 solutions crystal screen kit at 25ºC. Needle-like crystals were observed after the seventh day of incubation in test solution No.47 (0.1 M KCl, 0.01 M MgCl2.6H2O, 0.05 M Tris-Cl pH 8.5, 30% v/v PEG 4000). Further testing is required for confirming the crystal.

Keywords: tuberculosis, two component system, histidine kinase, needle-like crystals

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Authors: Nana Saralidze, Nino Sharashenidze, Zurab Kevanishvili

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Hidden hearing loss may occur in humans exposed to prolonged high-level sound. It is the loss of ability to hear high-level background noise while having normal hearing in quiet. We compared the hearing of people who regularly listen 3 hours and more to personal music players and those who do not. Forty participants aged 18-30 years were divided into two groups: regular users of music players and people who had never used them. And the third group – elders aged 50-55 years, had 15 participants. Pure-tone audiometry (125-16000 Hz), auditory brainstem response (ABR) (70dB SPL), and ability to identify speech in noise (4-talker babble with a 65-dB signal-to-noise ratio at 80 dB) were measured in all participants. All participants had normal pure-tone audiometry (all thresholds < 25 dB HL). A significant difference between groups was observed in that regular users of personal audio systems correctly identified 53% of words, whereas the non-users identified 74% and the elder group – 63%. This contributes evidence supporting the presence of a hidden hearing loss in humans and demonstrates that speech-in-noise audiometry is an effective method and can be considered as the GOLD standard for detecting hidden hearing loss.

Keywords: mp3 player, hidden hearing loss, speech audiometry, pure tone audiometry

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: Vimanyu Chadha, Ranganath M. Singari

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Drilling composite materials is a frequently practiced machining process during assembling in various industries such as automotive and aerospace. However, drilling of glass fiber reinforced plastic (GFRP) composites is significantly affected by damage tendency of these materials under cutting forces such as thrust force and torque. The aim of this paper is to investigate the influence of the various cutting parameters such as cutting speed and feed rate; subsequently also to study the influence of number of layers on delamination produced while drilling a GFRP composite. A plan of experiments, based on Taguchi techniques, was instituted considering drilling with prefixed cutting parameters in a hand lay-up GFRP material. The damage induced associated with drilling GFRP composites were measured. Moreover, Analysis of Variance (ANOVA) was performed to obtain minimization of delamination influenced by drilling parameters and number layers. The optimum drilling factor combination was obtained by using the analysis of signal-to-noise ratio. The conclusion revealed that feed rate was the most influential factor on the delamination. The best results of the delamination were obtained with composites with a greater number of layers at lower cutting speeds and feed rates.

Keywords: analysis of variance, delamination, design optimization, drilling, glass fiber reinforced plastic composites, Taguchi method

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: S. Lahijanian, M. Mobli, B. Baninasab, N. Etemadi

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Low temperature extremes are amongst the major stresses that adversely affect the plant growth and productivity. Cold stress causes oxidative stress, physiological, morphological and biochemical changes in plant cells. Generally, low temperatures similar to salinity and drought exert their negative effects mainly by disrupting the ionic and osmotic equilibrium of the plant cells. Changes in climatic condition leading to more frequent extreme conditions will require adapted crop species on a larger scale in order to sustain agricultural production. Eucalyptus is a diverse genus of flowering trees (and a few shrubs) in the myrtle family, Myrtaceae. Members of this genus dominate the tree flora of Australia. The eucalyptus genus contains more than 580 species and large number of cultivars, which are native to Australia. Large distribution and diversity of compatible eucalyptus cultivars reflect the fact of ecological flexibility of eucalyptus. Some eucalyptus cultivars can sustain hard environmental conditions like high and low temperature, salinity, high level of PH, drought, chilling and freezing which are intensively effective on crops with tropical and subtropical origin. In this study, we tried to evaluate freezing tolerance of 12 eucalyptus genotypes by means of four different morphological and physiological methods: Chlorophyll fluorescence, electrolyte leakage, proline and stomatal density. The studied cultivars include Eucalyptus camaldulensis, E. coccifera, E. darlympleana, E. erythrocorys, E. glaucescens, E. globulus, E. gunnii, E. macrocorpa, E. microtheca, E. rubida, E. tereticornis, and E. urnigera. Except for stomatal density recording, in other methods, plants were exposed to five gradual temperature drops: zero, -5, -10, -15 and -20 degree of centigrade and they remained in these temperatures for at least one hour. Experiment for measuring chlorophyll fluorescence showed that genotypes E. erythrocorys and E. camaldulensis were the most resistant genotypes and E. gunnii and E.coccifera were more sensitive than other genotypes to freezing stress effects. In electrolyte leakage experiment with regard to significant interaction between cultivar and temperature, genotypes E. erythrocorys and E.macrocorpa were shown to be the most tolerant genotypes and E. gunnii, E. urnigera, E. microtheca and E. tereticornis with the more ionic leakage percentage showed to be more sensitive to low temperatures. Results of Proline experiment approved that the most resistant genotype to freezing stress is E. erythrocorys. In the stomatal density experiment, the numbers of stomata under microscopic field were totally counted and the results showed that the E. erythrocorys and E. macrocorpa genotypes had the maximum and E. coccifera and E. darlympleana genotypes had minimum number of stomata under microscopic field (0.0605 mm2). In conclusion, E. erythrocorys identified as the most tolerant genotype; meanwhile E. gunnii classified as the most freezing susceptible genotype in this investigation. Further, remarkable correlation was not obtained between the stomatal density and other cold stress measures.

Keywords: chlorophyll fluorescence, cold stress, ionic leakage, proline, stomatal density

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Jan Jezek, Zdenek Pilat, Filip Smatlo, Pavel Zemanek

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We present a device that combines fluorescence spectroscopy with fiber optics and dielectrophoretic micromanipulation in PDMS (poly-(dimethylsiloxane)) microfluidic chips. The device allows high speed detection (in the order of kHz) of the fluorescence signal, which is coming from the sample by an inserted optical fiber, e.g. from a micro-droplet flow in a microfluidic chip, or even from the liquid flowing in the transparent capillary, etc. The device uses a laser diode at a wavelength suitable for excitation of fluorescence, excitation and emission filters, optics for focusing the laser radiation into the optical fiber, and a highly sensitive fast photodiode for detection of fluorescence. The device is combined with dielectrophoretic sorting on a chip for sorting of micro-droplets according to their fluorescence intensity. The electrodes are created by lift-off technology on a glass substrate, or by using channels filled with a soft metal alloy or an electrolyte. This device found its use in screening of enzymatic reactions and sorting of individual fluorescently labelled microorganisms. The authors acknowledge the support from the Grant Agency of the Czech Republic (GA16-07965S) and Ministry of Education, Youth and Sports of the Czech Republic (LO1212) together with the European Commission (ALISI No. CZ.1.05/2.1.00/01.0017).

Keywords: dielectrophoretic sorting, fiber optics, laser, microfluidic chips, microdroplets, spectroscopy

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Authors: Hamed Alizadeh Dashagholi, Hossein Yousefi-Banaem, Mina Naeimi

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Current study established for EEG signal analysis in patients with language disorder. Language disorder can be defined as meaningful delay in the use or understanding of spoken or written language. The disorder can include the content or meaning of language, its form, or its use. Here we applied Z-score, power spectrum, and coherence methods to discriminate the language disorder data from healthy ones. Power spectrum of each channel in alpha, beta, gamma, delta, and theta frequency bands was measured. In addition, intra hemispheric Z-score obtained by scoring algorithm. Obtained results showed high Z-score and power spectrum in posterior regions. Therefore, we can conclude that peoples with language disorder have high brain activity in frontal region of brain in comparison with healthy peoples. Results showed that high coherence correlates with irregularities in the ERP and is often found during complex task, whereas low coherence is often found in pathological conditions. The results of the Z-score analysis of the brain dynamics showed higher Z-score peak frequency in delta, theta and beta sub bands of Language Disorder patients. In this analysis there were activity signs in both hemispheres and the left-dominant hemisphere was more active than the right.

Keywords: EEG, electroencephalography, coherence methods, language disorder, power spectrum, z-score

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Authors: C. Fernanda da S. Sampaio, M. Gabriela Sadith Perez Paredes, V. Antonio de O. Martins

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Today the transport sector is undergoing a revolution, with the rise of Advanced Driver Assistance Systems (ADAS), industry and society itself will undergo a major transformation. However, the technological development of these applications is a challenge that requires new techniques and great machine learning and artificial intelligence. The study proposes to develop a vehicular perception system called Veibra, which consists of two front cameras for day/night viewing and an embedded device capable of working with Yolov2 image processing algorithms with low computational cost. The strategic version for the market is to assist the driver on the road with the detection of day/night objects, such as road signs, pedestrians, and animals that will be viewed through the screen of the phone or tablet through an application. The system has the ability to perform real-time driver detection and recognition to identify muscle movements and pupils to determine if the driver is tired or inattentive, analyzing the student's characteristic change and following the subtle movements of the whole face and issuing alerts through beta waves to ensure the concentration and attention of the driver. The system will also be able to perform tracking and monitoring through GSM (Global System for Mobile Communications) technology and the cameras installed in the vehicle.

Keywords: advanced driver assistance systems, tracking, traffic signal detection, vehicle perception system

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Authors: Arka Roy, Chandra Prakash Dubey

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Inverse problem generally used for recovering hidden information from outside available data. Vertical component of gravity field we will be going to use for underneath density structure calculation. Ill-posing nature is main obstacle for any inverse problem. Linear regularization using Tikhonov formulation are used for appropriate choice of SVD and GSVD components. For real time data handle, signal to noise ratios should have to be less for reliable solution. In our study, 2D and 3D synthetic model with rectangular grid are used for gravity field calculation and its corresponding inversion for density reconstruction. Fine grid also we have considered to hold any irregular structure. Keeping in mind of algebraic ambiguity factor number of observation point should be more than that of number of data point. Picard plot is represented here for choosing appropriate or main controlling Eigenvalues for a regularized solution. Another important study is depth resolution plot (DRP). DRP are generally used for studying how the inversion is influenced by regularizing or discretizing. Our further study involves real time gravity data inversion of Vredeforte Dome South Africa. We apply our method to this data. The results include density structure is in good agreement with known formation in that region, which puts an additional support of our method.

Keywords: depth resolution plot, gravity inversion, Picard plot, SVD, Tikhonov formulation

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: H. Weigelt-Marom

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Over the years and up to the arousal of the COVID-19 pandemic, deaf or hard of hearing students studying in higher education institutions, participated lectures on campus using hearing aids and strategies adapted for frontal learning in a classroom. Usually, these aids were well known to them from their earlier study experience in school. However, the transition to online lessons, due to the latest pandemic, led deaf or hard of hearing students to study outside of their physical, well known learning environment. The change of learning environment and structure rose new challenges for these students. The present study examined the learning experience, limitations, challenges and benefits regarding learning online with lecture and classmates via the “Zoom” video conference program, among deaf or hard of hearing students in academia setting. In addition, emotional and social aspects related to learning in general versus the “Zoom” were examined. The study included 18 students diagnosed as deaf or hard of hearing, studying in various higher education institutions in Israel. All students had experienced lessons on the “Zoom”. Following allocation of the group study by the deaf and hard of hearing non-profit organization “Ma’agalei Shema”, and receiving the participants inform of consent, students were requested to answer a google form questioner and participate in an interview. The questioner included background information (e.g., age, year of studying, faculty etc.), level of computer literacy, and level of hearing and forms of communication (e.g., lip reading, sign language etc.). The interviews included a one on one, semi-structured, in-depth interview, conducted by the main researcher of the study (interview duration: up to 60 minutes). The interviews were held on “ZOOM” using specific adaptations for each interviewee: clear face screen of the interviewer for lip and face reading, and/ or professional sign language or live text transcript of the conversation. Additionally, interviewees used their audio devices if needed. Questions regarded: learning experience, difficulties and advantages studying using “Zoom”, learning in a classroom versus on “Zoom”, and questions concerning emotional and social aspects related to learning. Thematic analysis of the interviews revealed severe difficulties regarding the ability of deaf or hard of hearing students to comprehend during ”Zoom“ lessons without adoptive aids. For example, interviewees indicated difficulties understanding “Zoom” lessons due to their inability to use hearing devices commonly used by them in the classroom (e.g., FM systems). 80% indicated that they could not comprehend “Zoom” lessons since they could not see the lectures face, either because lectures did not agree to open their cameras or, either because they did not keep a straight forward clear face appearance while teaching. However, not all descriptions regarded learning via the “zoom” were negative. For example, 20% reported the recording of “Zoom” lessons as a main advantage. Enabling then to repeatedly watch the lessons at their own pace, mostly assisted by friends and family to translate the audio output into an accessible input. These finding and others regarding the learning experience of the group study on the “Zoom”, as well as their recommendation to enable deaf or hard of hearing students to study inclusively online, will be presented at the conference.

Keywords: deaf or hard of hearing, learning experience, Zoom, qualitative research

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Authors: Matthew Edmunds, Andrew Hunter, Clare Littlewood, Wisha Gul, Gabriel Heppenstall-Harris, Thomas Humphries

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Background: Revision courses for medical students undertaking their final examinations are commonplace throughout the UK. Traditionally these take the form of a series of lectures over multiple weeks or a single day of intensive lectures. The COVID-19 pandemic, however, has required medical educators to create new teaching formats to ensure they adhere to social distancing requirements. It has provided an unexpected opportunity to accelerate the development of students proficiency in the use of ‘technology-enabled communication platforms’, as mandated in the 2018 GMC Outcomes of Graduates. Recent advances in technology have made distance learning possible, whilst also providing novel and more engaging learning opportunities for students. Foundation Year 2 doctors at Aintree University Hospital developed an online series of videos to help prepare medical students in the North West and byond for their final medical school examinations. Method: Eight hour-long videos covering the key topics in medicine and surgery were posted on the Peer Learning Liverpool Youtube channel. These videos were created using new technology such as the screen and audio recording platform, Loom. Each video compromised at least 20 single best answer (SBA) questions, in keeping with the format in most medical school finals. Explanations of the answers were provided, and additional important material was covered. Students were able to ask questions by commenting on the videos, with the authors replying as soon as possible. Feedback was collated using an online Google form. Results: An average of 327 people viewed each video, with 113 students filling in the feedback form. 65.5% of respondents were within one month of their final medical school examinations. The average rating for how well prepared the students felt for their finals was 6.21/10 prior to the course and 8.01/10 after the course. A paired t-test demonstrated a mean increase of 1.80 (95% CI 1.66-1.93). Overall, 98.2% said the online format worked well or very well, and 99.1% would recommend the course to a peer. Conclusions: Based on the feedback received, the online revision course was successful both in terms of preparing students for their final examinations, and with regards to how well the online format worked. Free-text qualitative feedback highlighted advantages such as; students could learn at their own pace, revisit key concepts important to them, and practice exam style questions via the case-based format. Limitations identified included inconsistent audiovisual quality, and requests for a live online Q&A session following the conclusion of the course. This course will be relaunched later in the year with increased opportunities for students to access live feedback. The success of this online course has shown the roll that technology can play in medical education. As well as providing novel teaching modes, online learning allows students to access resources that otherwise would not be available locally, and ensure that they do not miss out on teaching that was previously provided face to face, in the current climate of social distancing.

Keywords: COVID-19 pandemic, Medical School, Online learning, Revision course

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

Procedia PDF Downloads 125

Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

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Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Jeronimo Cox, Tomonari Furukawa

Abstract:

Magnetometers have become more popular in inertial measurement units (IMU) for their ability to correct estimations using the earth's magnetic field. Accelerometer and gyroscope-based packages fail with dead-reckoning errors accumulated over time. Localization in robotic applications with magnetometer-inclusive IMUs has become popular as a way to track the odometry of slower-speed robots. With high-speed motions, the accumulated error increases over smaller periods of time, making them difficult to track with IMU. Tracking a high-speed motion is especially difficult with limited observability. Visual obstruction of motion leaves motion-tracking cameras unusable. When motions are too dynamic for estimation techniques reliant on the observability of the gravity vector, the use of magnetometers is further justified. As available magnetometer calibration methods are limited with the assumption that background magnetic fields are uniform, estimation in nonuniform magnetic fields is problematic. Hard iron distortion is a distortion of the magnetic field by other objects that produce magnetic fields. This kind of distortion is often observed as the offset from the origin of the center of data points when a magnetometer is rotated. The magnitude of hard iron distortion is dependent on proximity to distortion sources. Soft iron distortion is more related to the scaling of the axes of magnetometer sensors. Hard iron distortion is more of a contributor to the error of attitude estimation with magnetometers. Indoor environments or spaces inside ferrite-based structures, such as building reinforcements or a vehicle, often cause distortions with proximity. As positions correlate to areas of distortion, methods of magnetometer localization include the production of spatial mapping of magnetic field and collection of distortion signatures to better aid location tracking. The goal of this paper is to compare magnetometer methods that don't need pre-productions of magnetic field maps. Mapping the magnetic field in some spaces can be costly and inefficient. Dynamic measurement fusion is used to track the motion of a multi-link system with us. Conventional calibration by data collection of rotation at a static point, real-time estimation of calibration parameters each time step, and using two magnetometers for determining local hard iron distortion are compared to confirm the robustness and accuracy of each technique. With opposite-facing magnetometers, hard iron distortion can be accounted for regardless of position, Rather than assuming that hard iron distortion is constant regardless of positional change. The motion measured is a repeatable planar motion of a two-link system connected by revolute joints. The links are translated on a moving base to impulse rotation of the links. Equipping the joints with absolute encoders and recording the motion with cameras to enable ground truth comparison to each of the magnetometer methods. While the two-magnetometer method accounts for local hard iron distortion, the method fails where the magnetic field direction in space is inconsistent.

Keywords: motion tracking, sensor fusion, magnetometer, state estimation

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

Abstract:

The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

Abstract:

Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: Margareta Simina Stanc, Aurel Mototolea, Tiberiu Potarniche

Abstract:

Sacidava archeological site is located in Dobrudja region, Romania, on a hill on the right bank of the Danube - the Musait point, located at about 5 km north-east from Dunareni village. The place-name documents the fact that, prior to the Roman conquest, in the area, there was a Getic settlement. The location of the Sacidava was made possible by corroborating the data provided by the ancient sources with the epigraphic documents (the milial pillar during the time of Emperor Decius). The tegular findings attest that an infantry unit, cohors I Cilicum milliaria equitata, as well as detachments from Legio V Macedonica and Legio XI Claudia, were confined to Sacidava. During the period of the Dominion, the garrison of the fortification is the host of a cavalry unit: cuneus equitum scutariorum. In the immediate vicinity to the Roman fortress, to the east, were identified two other fortifications: a Getic settlement (4th-1st century B.C.) and an Early Medieval settlement (9th-10th century A.C.). The archaeological material recovered during the research is represented by ceramic forms such as amphoras, jugs, pots, cups, plates, to which are added oil lamps, some of them typologically new at the time of discovery. Local ceramic shapes were also founded, worked by hand or by wheel, considered un-Romanized or in the course of Romanization. During the time of the Principality, Sacidava it represented an important military outpost serving mainly the city of Tropaeum Traiani, controlling also the supply and transport on the Danube limes in the Moesic sector. This role will determine the development of the fortress and the appearance of extramuros civil structures, thus becoming an important landmark during the 5th-6th centuries A.C., becoming a representation of power of the Roman empire in an area of continuous conflict. During recent archaeological researches, faunal remains were recovered, and their analysis allowed to estimate the animal resources and subsistence practices (animal husbandry, hunting, fishing) in the settlement. The methodology was specific to archaeozoology, mainly consisting of anatomical, taxonomical, and taphonomical identifications, recording, and quantification of the data. The remains of domestic mammals have the highest proportion indicating the importance of animal husbandry; the predominant species are Bos taurus, Ovis aries/Capra hircus, and Sus domesticus. Fishing and hunting were of secondary importance in the subsistence economy of the community. Wild boar and the red deer were the most frequently hunted species. Just a few fish bones were recovered. Thus, the ancient city of Sacidava is proving to be an important element of cultural heritage of the south-eastern part of Romania, for whose conservation and enhancement efforts must be made, especially by landscape reconstruction.

Keywords: archaeozoology, landscape reconstruction, limes, military outpost

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Authors: Nicholas Reinicke, Brennan Borlaug, Matthew Moniot

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The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior, such as travel surveys and travel demand models, are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after the pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky, and Des Moines, Iowa, which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.

Keywords: COVID-19, driver behavior, GPS data, signal analysis, telework

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Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

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Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

Procedia PDF Downloads 282

Authors: Mumtaz Begum Mustafa, Siti Salwah Salim, Feizal Dani Rahman

Abstract:

Recently, Automatic Speech Recognition (ASR) systems were used to assist children in language acquisition as it has the ability to detect human speech signal. Despite the benefits offered by the ASR system, there is a lack of ASR systems for Malay-speaking children. One of the contributing factors for this is the lack of continuous speech database for the target users. Though cross-lingual adaptation is a common solution for developing ASR systems for under-resourced language, it is not viable for children as there are very limited speech databases as a source model. In this research, we propose a two-stage adaptation for the development of ASR system for Malay-speaking children using a very limited database. The two stage adaptation comprises the cross-lingual adaptation (first stage) and cross-age adaptation. For the first stage, a well-known speech database that is phonetically rich and balanced, is adapted to the medium-sized Malay adults using supervised MLLR. The second stage adaptation uses the speech acoustic model generated from the first adaptation, and the target database is a small-sized database of the target users. We have measured the performance of the proposed technique using word error rate, and then compare them with the conventional benchmark adaptation. The two stage adaptation proposed in this research has better recognition accuracy as compared to the benchmark adaptation in recognizing children’s speech.

Keywords: Automatic Speech Recognition System, children speech, adaptation, Malay

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Authors: Ji Hye Im, Nguyen T. T. Phuong, Keon Wook Kang

Abstract:

Estrogens are important for the development and growth of estrogen receptor (ER)-positive breast cancer, for which anti-estrogen therapy is one of the most effective treatments. However, its efficacy can be limited by either de novo or acquired resistance. Aromatase is a key enzyme for the biosynthesis of estrogens, and inhibition of this enzyme leads to profound hypoestrogenism. Here, we found that the basal expression and activity of aromatase were significantly increased in tamoxifen (TAM)-resistant human breast cancer (TAMR-MCF-7) cells compared to control MCF-7 cells. We further revealed that aromatase immunoreactivity in tumor tissues was increased in recurrence group after TAM therapy compared to non-recurrence group after TAM therapy. Phosphorylation of Akt, extracellular signal-regulated kinase (ERK), and p38 kinase were all increased in TAMR-MCF-7 cells. Inhibition of phosphoinositide 3-kinase (PI3K) suppressed the transactivation of the aromatase gene and its enzyme activity. Furthermore, we have also shown that PI3K/Akt-dependent cAMP-response element binding protein (CREB) activation was required for the enhanced expression of aromatase in TAMR-MCF-7 cells. Our findings suggest that aromatase expression is up-regulated in TAM-resistant breast cancer via PI3K/Akt-dependent CREB activation.

Keywords: TAMR-MCF-7, CREB, estrogen receptor, aromatase

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Authors: Zhengkun Yi

Abstract:

Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

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Authors: Hamid Mustafa, Adeela Ajmal, Kim EuiSoo, Noor-ul-Ain

Abstract:

World wild, buffalo production is considered as most important component of food industry. Efficient buffalo production is related with reproductive performance of this species. Lack of knowledge of reproductive efficiency and its related genes in buffalo species is a major constraint for sustainable buffalo production. In this study, we performed some bioinformatics analysis on Follicle Stimulating Hormone Receptor (FSHR) gene and explored the possible relationship of this gene among different buffalo breeds and with other farm animals. We also found the evolution pattern for this gene among these species. We investigate CDS lengths, Stop codon variation, homology search, signal peptide, isoelectic point, tertiary structure, motifs and phylogenetic tree. The results of this study indicate 4 different motif in this gene, which are Activin-recp, GS motif, STYKc Protein kinase and transmembrane. The results also indicate that this gene has very close relationship with cattle, bison, sheep and goat. Multiple alignment (MA) showed high conservation of motif which indicates constancy of this gene during evolution. The results of this study can be used and applied for better understanding of this gene for better characterization of Follicle Stimulating Hormone Receptor (FSHR) gene structure in different farm animals, which would be helpful for efficient breeding plans for animal’s production.

Keywords: buffalo, FSHR gene, bioinformatics, production

Procedia PDF Downloads 516