Search results for: feature detection

Authors: Goodluck Akaoma Ordu

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Ruthium (Rth) represents a theoretical superheavy element with an atomic number of 119, proposed within the context of advanced materials science and nuclear physics. The conceptualization of Rth involves theoretical frameworks that anticipate its atomic structure, including a hypothesized stable isotope, Rth-320, characterized by 119 protons and 201 neutrons. The synthesis of Ruthium (Rth) hinges on intricate nuclear fusion processes conducted in state-of-the-art particle accelerators, notably utilizing Calcium-48 (Ca-48) as a projectile nucleus and Einsteinium-253 (Es-253) as a target nucleus. These experiments aim to induce fusion reactions that yield Ruthium isotopes, such as Rth-301, accompanied by neutron emission. Theoretical predictions outline various physical and chemical properties attributed to Ruthium (Rth). It is envisaged to possess a high density, estimated at around 25 g/cm³, with melting and boiling points anticipated to be exceptionally high, approximately 4000 K and 6000 K, respectively. Chemical studies suggest potential oxidation states of +2, +3, and +4, indicating a versatile reactivity, particularly with halogens and chalcogens. The atomic structure of Ruthium (Rth) is postulated to feature an electron configuration of [Rn] 5f^14 6d^10 7s^2 7p^2, reflecting its position in the periodic table as a superheavy element. However, the creation and study of superheavy elements like Ruthium (Rth) pose significant challenges. These elements typically exhibit very short half-lives, posing difficulties in their stabilization and detection. Research efforts are focused on identifying the most stable isotopes of Ruthium (Rth) and developing advanced detection methodologies to confirm their existence and properties. Specialized detectors are essential in observing decay patterns unique to Ruthium (Rth), such as alpha decay or fission signatures, which serve as key indicators of its presence and characteristics. The potential applications of Ruthium (Rth) span across diverse technological domains, promising innovations in energy production, material strength enhancement, and sensor technology. Incorporating Ruthium (Rth) into advanced energy systems, such as the Arc Reactor concept, could potentially amplify energy output efficiencies. Similarly, integrating Ruthium (Rth) into structural materials, exemplified by projects like the NanoArc gauntlet, could bolster mechanical properties and resilience. Furthermore, Ruthium (Rth)--based sensors hold promise for achieving heightened sensitivity and performance in various sensing applications. Looking ahead, the study of Ruthium (Rth) represents a frontier in both fundamental science and applied research. It underscores the quest to expand the periodic table and explore the limits of atomic stability and reactivity. Future research directions aim to delve deeper into Ruthium (Rth)'s atomic properties under varying conditions, paving the way for innovations in nanotechnology, quantum materials, and beyond. The synthesis and characterization of Ruthium (Rth) stand as a testament to human ingenuity and technological advancement, pushing the boundaries of scientific understanding and engineering capabilities. In conclusion, Ruthium (Rth) embodies the intersection of theoretical speculation and experimental pursuit in the realm of superheavy elements. It symbolizes the relentless pursuit of scientific excellence and the potential for transformative technological breakthroughs. As research continues to unravel the mysteries of Ruthium (Rth), it holds the promise of reshaping materials science and opening new frontiers in technological innovation.

Keywords: superheavy element, nuclear fusion, bombardment, particle accelerator, nuclear physics, particle physics

Procedia PDF Downloads 37

Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

Procedia PDF Downloads 67

Authors: Rabia Munsaf Khan, Eshrat Fatima

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The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences. Pakistan, being an agricultural country depends on this resource as it makes 70% of the GDP. The case study is of Lal Suhanra National Park, which is not only the biggest forest reserve of Pakistan but also of Asia. The study is performed using different temporal images of Landsat. Also, the results of Landsat are cross-checked by using Sentinel-2 imagery as it has both higher spectral and spatial resolution. Vegetation can easily be detected using NDVI which is a common and widely used index. It is an important vegetation index, widely applied in research on global environmental and climatic change. The images are then classified to observe the change occurred over 15 years. Vegetation cover maps of 2000 and 2016 are used to generate the map of vegetation change detection for the respective years and to find out the changing pattern of vegetation cover. Also, the NDVI values aided in the detection of percentage decrease in vegetation cover. The study reveals that vegetation cover of the area has decreased significantly during the year 2000 and 2016.

Keywords: Landsat, normalized difference vegetation index (NDVI), sentinel 2, Greenland monitoring

Procedia PDF Downloads 309

Authors: Abdulwahab Kammon, Tan Sheau Wei, Abdul Rahman Omar, Abdunaser Dayhum, Ibrahim Eldghayes, Monier Sharif

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Infectious bronchitis virus (IBV) is a very dynamic and evolving virus which causing major economic losses to the global poultry industry. Recently, the Libyan poultry industry faced severe outbreak of respiratory distress associated with high mortality and dramatic drop in egg production. Tracheal and cloacal swabs were analyzed for several poultry viruses. IBV was detected using SYBR Green I real-time PCR detection based on the nucleocapsid (N) gene. Sequence analysis of the partial N gene indicated high similarity (~ 94%) to IBV strain 3382/06 that was isolated from Taiwan. Even though the IBV strain 3382/06 is more similar to that of the Mass type H120, the isolate has been implicated associated with intertypic recombinant of 3 putative parental IBV strains namely H120, Taiwan strain 1171/92 and China strain CK/CH/LDL/97I. Complete sequencing and antigenicity studies of the Libya IBV strains are currently underway to determine the evolution of the virus and its importance in vaccine induced immunity. In this paper, we documented for the first time the presence of possibly variant IBV strain from Libya which required a dramatic change in the vaccination program.

Keywords: Libya, infectious bronchitis, molecular characterization, viruses, vaccine

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Authors: Somayeh Khanjani, Samaneh Nabavi, Shirin Jalili, Afshin Khara

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Fingerprints are area source of ubiquitous evidence and consequential for establishing identity. The detection and subsequent development of fingerprints are thus inevitable in criminal investigations. This becomes a difficult task in the case of certain extreme conditions like fire. A fire scene may be accidental or arson. The evidence subjected to fire is generally overlooked as there is a misconception that they are damaged. There are several scientific approaches to determine whether the fire was deliberate or not. In such as scenario, fingerprints may be most critical to link the perpetrator to the crime. The reason for this may be the destructive nature of fire. Fingerprints subjected to fire are exposed to high temperatures, soot deposition, electromagnetic radiation, and subsequent water force. It is believed that these phenomena damage the fingerprint. A novel fluorescent and a pre existing small particle reagent were investigated for the same. Zinc carbonates based fluorescent small particle reagent was capable of developing latent fingerprints exposed to a maximum temperature of 800 ̊C. Fluorescent SPR may prove very useful in such cases. Fluorescent SPR reagent based on zinc carbonate is a potential method for developing fingerprints from arson sites. The method is cost effective and non hazardous. This formulation is suitable for developing fingerprints exposed to fire/ arson.

Keywords: fingerprint, small particle reagent (SPR), arson, novel fluorescent

Procedia PDF Downloads 472

Authors: Mohamed A. Alhammad, Hadia A. Abou-Donia, Mona H. Hashish, Mohamed N. Massoud

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Background: Rotavirus is the leading etiologic agent of severe diarrheal disease in infants and young children worldwide. The present study was aimed 1) to detect rotavirus infection as a cause of diarrhoea among children under 5 years of age using the two serological methods (ELISA and LA) and the PCR technique (2) to evaluate the three methodologies used for human RV detection in stool samples. Materials and Methods: This study was carried out on 247 children less than 5 years old, diagnosed clinically as acute gastroenteritis and attending Alexandria University Children Hospital at EL-Shatby. Rotavirus antigen was screened by ELISA and LA tests in all stool samples, whereas only 100 samples were subjected to RT-PCR method for detection of rotavirus RNA. Results: Out of the 247 studied cases with diarrhoea, rotavirus antigen was detected in 83 (33.6%) by ELISA and 73 (29.6%) by LA, while the 100 cases tested by RT-PCR showed that 44% of them had rotavirus RNA. Rotavirus diarrhoea was significantly presented with a marked seasonal peak during autumn and winter (61.4%). Conclusion: The present study confirms the huge burden of rotavirus as a major cause of acute diarrhoea in Egyptian infants and young children. It was concluded that; LA is equal in sensitivity to ELISA, ELISA is more specific than LA, and RT-PCR is more specific than ELISA and LA in diagnosis of rotavirus infection.

Keywords: rotavirus, diarrhea, immunoenzyme techniques, latex fixation tests, RT-PCR

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Authors: Jérémie Ochin

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Soccer analytics are built on two data sources: the frame-by-frame position of each player on the terrain and the sequences of events, such as ball drive, pass, cross, shot, throw-in... With more than 2000 ball-events per soccer game, their precise and exhaustive annotation, based on a monocular video stream such as a TV broadcast, remains a tedious and costly manual task. State-of-the-art methods for spatio-temporal action detection from a monocular video stream, often based on 3D convolutional neural networks, are close to reach levels of performances in mean Average Precision (mAP) compatibles with the automation of such task. Nevertheless, to meet their expectation of exhaustiveness in the context of data analytics, such methods must be applied in a regime of high recall – low precision, using low confidence score thresholds. This setting unavoidably leads to the detection of false positives that are the product of the well documented overconfidence behaviour of neural networks and, in this case, their limited access to contextual information and understanding of the game: their predictions are highly unstructured. Based on the assumption that professional soccer players’ behaviour, pose, positions and velocity are highly interrelated and locally driven by the player performing a ball-action, it is hypothesized that the addition of information regarding surrounding player’s appearance, positions and velocity in the prediction methods can improve their metrics. Several methods are compared to build a proper representation of the game surrounding a player, from handcrafted features of the local graph, based on domain knowledge, to the use of Graph Neural Networks trained in an end-to-end fashion with existing state-of-the-art 3D convolutional neural networks. It is shown that the inclusion of information regarding surrounding players helps reaching higher metrics.

Keywords: fine-grained action recognition, human action recognition, convolutional neural networks, graph neural networks, spatio-temporal action recognition

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Authors: Balaji Ganesh Rajagopal, Subramanian Appavu alias Balamurugan, Ayyalraj Midhun Kumar, Krishnan Nallaperumal

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Intelligent Transportation System is an Emerging area to solve multiple transportation problems. Several forms of inputs are needed in order to solve ITS problems. Advanced Traveler Information System (ATIS) is a core and important ITS area of this modern era. This involves travel time forecasting, efficient road map analysis and cost based path selection, Detection of the vehicle in the dynamic conditions and Traffic congestion state forecasting. This Article designs and provides an algorithm for traffic data generation which can be used for the above said ATIS application. By inputting the real world traffic situation in the form of video sequences, the algorithm determines the Traffic density in terms of congestion, number of vehicles in a given path which can be fed for various ATIS applications. The Algorithm deduces the key frame from the video sequences and follows the Blob detection, Identification and Tracking using connected components algorithm to determine the correlation between the vehicles moving in the real road scene.

Keywords: traffic transportation, traffic density estimation, blob identification and tracking, relative velocity of vehicles, correlation between vehicles

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Authors: Ashneel Ajay Singh, Anish Maharaj, Havish Naidu, Michelle Kumar

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Mangrove patches are the foundation species located in the estuarine land areas. These patches provide a nursery, food source and protection for numerous aquatic, intertidal and well as land-based organisms. Mangroves also help in coastal protection, maintain water clarity and are one of the biggest sinks for blue carbon sequestration. In the Pacific Island countries, numerous coastal communities have a heavy socioeconomic dependence on coastal resources and mangroves play a key ecological and economical role in structuring the availability of these resources. Fiji has a large mangrove patch located in the Votua area of the Ba province. Globally, mangrove population continues to decline with the changes in climatic conditions and anthropogenic activities. Baseline information through wetland maps and time series change are essential references for development of effective mangrove management plans. These maps reveal the status of the resource and the effects arising from anthropogenic activities and climate change. In this study, we used remote sensing and GIS tools for mapping and temporal change detection over a period of >20 years in Votua, Fiji using Landsat imagery. Landsat program started in 1972 initially as Earth Resources Technology Satellite. Since then it has acquired millions of images of Earth. This archive allows mapping of temporal changes in mangrove forests. Mangrove plants consisted of the species Rhizophora stylosa, Rhizophora samoensis, Bruguiera gymnorrhiza, Lumnitzera littorea, Heritiera littoralis, Excoecaria agallocha and Xylocarpus granatum. Change detection analysis revealed significant reduction in the mangrove patch over the years. This information serves as a baseline for the development and implementation of effective management plans for one of Fiji’s biggest mangrove patches.

Keywords: climate change, GIS, Landsat, mangrove, temporal change

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Authors: Jorge R. Santos, Pedro Sebastiao

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In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.

Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js

Procedia PDF Downloads 148

Authors: Edmaritz Hernandez-Pagan, Kanjana Laosuntisuk, Alex Harris, Allison Haynes, David Buitrago, Michael Kudenov, Colleen Doherty

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Rare Earth Elements (REEs) are critical metals for modern electronics, green technologies, and defense systems. However, due to their dispersed nature in the Earth’s crust, frequent co-occurrence with radioactive materials, and similar chemical properties, acquiring and purifying REEs is costly and environmentally damaging, restricting access to these metals. Plants could serve as resources for bioengineering REE mining systems. Although there is limited information on how REEs affect plants at a cellular and molecular level, plants with high REE tolerance and hyperaccumulation have been identified. This dissertation aims to develop a plant-based system for harvesting REEs from industrial waste material with a focus on Acid Mine Drainage (AMD), a toxic coal mining product. The objectives are 1) to develop a non-destructive, in vivo detection method for REE detection in Phytolacca plants (REE hyperaccumulator) plants utilizing fluorescence spectroscopy and with a primary focus on dysprosium, 2) to characterize the uptake of REE and Heavy Metals in Phytolacca americana and Phytolacca acinosa (REE hyperaccumulator) in AMD for potential implementation in the plant-based system, 3) to implement the REE detection method to identify REE-binding proteins and peptides for potential enhancement of uptake and selectivity for targeted REEs in the plants implemented in the plant-based system. The candidates are known REE-binding peptides or proteins, orthologs of known metal-binding proteins from REE hyperaccumulator plants, and novel proteins and peptides identified by comparative plant transcriptomics. Lanmodulin, a high-affinity REE-binding protein from methylotrophic bacteria, is used as a benchmark for the REE-protein binding fluorescence assays and expression in A. thaliana to test for changes in REE plant tolerance and uptake.

Keywords: phytomining, agromining, rare earth elements, pokeweed, phytolacca

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Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

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Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

Procedia PDF Downloads 277

Authors: Eleonora Di Salvo, Antonio Panebianco, Graziella Ziino

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Background: Vibrio parahaemolyticus is a human pathogen that is widely distributed in marine environments. It is frequently isolated from raw seafood, particularly shellfish. Consumption of raw or undercooked seafood contaminated with V. parahaemolyticus may lead to acute gastroenteritis. Vibrio spp. has excellent resistance to low temperatures so it can be found in frozen products for a long time. Recently, the viable but non-culturable state (VBNC) of bacteria has attracted great attention, and more than 85 species of bacteria have been demonstrated to be capable of entering this state. VBNC cells cannot grow in conventional culture medium but are viable and maintain metabolic activity, which may constitute an unrecognized source of food contamination and infection. Also V. parahaemolyticus could exist in VBNC state under nutrient starvation or low-temperature conditions. Aim: The aim of the present study was to optimize methods and investigate V. parahaemolyticus VBNC cells and their presence in frozen bivalve molluscs, regularly marketed. Materials and Methods: propidium monoazide (PMA) was integrated with real-time polymerase chain reaction (qPCR) targeting the tl gene to detect and quantify V. parahaemolyticus in the VBNC state. PMA-qPCR resulted highly specific to V. parahaemolyticus with a limit of detection (LOD) of 10-1 log CFU/mL in pure bacterial culture. A standard curve for V. parahaemolyticus cell concentrations was established with the correlation coefficient of 0.9999 at the linear range of 1.0 to 8.0 log CFU/mL. A total of 77 samples of frozen bivalve molluscs (35 mussels; 42 clams) were subsequently subjected to the qualitative (on alkaline phosphate buffer solution) and quantitative research of V. parahaemolyticus on thiosulfate-citrate-bile salts-sucrose (TCBS) agar (DIFCO) NaCl 2.5%, and incubation at 30°C for 24-48 hours. Real-time PCR was conducted on homogenate samples, in duplicate, with and without propidium monoazide (PMA) dye, and exposed for 45 min under halogen lights (650 W). Total DNA was extracted from cell suspension in homogenate samples according to bolliture protocol. The Real-time PCR was conducted with species-specific primers for V. parahaemolitycus. The RT-PCR was performed in a final volume of 20 µL, containing 10 µL of SYBR Green Mixture (Applied Biosystems), 2 µL of template DNA, 2 µL of each primer (final concentration 0.6 mM), and H2O 4 µL. The qPCR was carried out on CFX96 TouchTM (Bio-Rad, USA). Results: All samples were negative both to the quantitative and qualitative detection of V. parahaemolyticus by the classical culturing technique. The PMA-qPCR let us individuating VBNC V. parahaemolyticus in the 20,78% of the samples evaluated with a value between the Log 10-1 and Log 10-3 CFU/g. Only clams samples were positive for PMA-qPCR detection. Conclusion: The present research is the first evaluating PMA-qPCR assay for detection of VBNC V. parahaemolyticus in bivalve molluscs samples, and the used method was applicable to the rapid control of marketed bivalve molluscs. We strongly recommend to use of PMA-qPCR in order to identify VBNC forms, undetectable by the classic microbiological methods. A precise knowledge of the V.parahaemolyticus in a VBNC form is fundamental for the correct risk assessment not only in bivalve molluscs but also in other seafood.

Keywords: food safety, frozen bivalve molluscs, PMA dye, Real-time PCR, VBNC state, Vibrio parahaemolyticus

Procedia PDF Downloads 139

Authors: Chetan Gupta, Ramesh Gupta

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Unmanned aerial vehicles, of all sizes, are prime targets of the wing morphing concept as their lightweight structures demand high aerodynamic stability while traversing unsteady atmospheric conditions. In this research study, a hybrid morphing technology is developed to aid the trailing edge of the aircraft wing to alter its camber as a monolithic element rather than functioning as conventional appendages like flaps. Kinematic tailoring, actuation techniques involving shape memory alloys (SMA), piezoelectrics – individually fall short of providing a simplistic solution to the conundrum of morphing aircraft wings. On the other hand, the feature of negligible hysteresis while actuating using compliant mechanisms has shown higher levels of applicability and deliverability in morphing wings of even large aircrafts. This research paper delves into designing a wing section model with a periodic, multi-stable compliant structure requiring lower orders of topological optimization. The design is sub-divided into three smaller domains with external hyperelastic connections to achieve deflections ranging from -15° to +15° at the trailing edge of the wing. To facilitate this functioning, a hybrid actuation system by combining the larger bandwidth feature of piezoelectric macro-fibre composites and relatively higher work densities of shape memory alloy wires are used. Finite element analysis is applied to optimize piezoelectric actuation of the internal compliant structure. A coupled fluid-surface interaction analysis is conducted on the wing section during morphing to study the development of the velocity boundary layer at low Reynold’s numbers of airflow.

Keywords: compliant mechanism, hybrid morphing, piezoelectrics, shape memory alloys

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Authors: Niki K. Burns, James R. Pearson, Paul G. Stevenson, Xavier A. Conlan

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In Australian state Victoria, synthetic cannabinoids have recently been made illegal under an amendment to the drugs, poisons and controlled substances act 1981. Identification of synthetic cannabinoids in popular brands of ‘incense’ and ‘potpourri’ has been a difficult and challenging task due to the sample complexity and changes observed in the chemical composition of the cannabinoids of interest. This study has developed analytical methodology for the targeted extraction and determination of synthetic cannabinoids available pre-ban. A simple solvent extraction and solid phase extraction methodology was developed that selectively extracted the cannabinoid of interest. High performance liquid chromatography coupled with UV‐visible and chemiluminescence detection (acidic potassium permanganate and tris (2,2‐bipyridine) ruthenium(III)) were used to interrogate the synthetic cannabinoid products. Mass spectrometry and nuclear magnetic resonance spectroscopy were used for structural elucidation of the synthetic cannabinoids. The tris(2,2‐bipyridine)ruthenium(III) detection was found to offer better sensitivity than the permanganate based reagents. In twelve different brands of herbal incense, cannabinoids were extracted and identified including UR‐144, XLR 11, AM2201, 5‐F‐AKB48 and A796‐260.

Keywords: electrospray mass spectrometry, high performance liquid chromatography, solid phase extraction, synthetic cannabinoids

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Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

Procedia PDF Downloads 125

Authors: Laheeb M. Ibrahim, Ibrahim A. Salih

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Biometrics is a technology that has been widely used in many official and commercial identification applications. The increased concerns in security during recent years (especially during the last decades) have essentially resulted in more attention being given to biometric-based verification techniques. Here, a novel fusion approach of palmprint, dental traits has been suggested. These traits which are authentication techniques have been employed in a range of biometric applications that can identify any postmortem PM person and antemortem AM. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing, deterring spoofing activities and reducing enrolment failure. In this paper, a first unimodel biometric system has been made by using (palmprint and dental) traits, for each one classification applying an artificial neural network and a hybrid technique that combines swarm intelligence and neural network together, then attempt has been made to combine palmprint and dental biometrics. Principally, the fusion of palmprint and dental biometrics and their potential application has been explored as biometric identifiers. To address this issue, investigations have been carried out about the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. Also the results of the multimodal approach have been compared with each one of these two traits authentication approaches. This paper studies the features and decision fusion levels in multimodal biometrics. To determine the accuracy of GAR to parallel system decision-fusion including (AND, OR, Majority fating) has been used. The backpropagation method has been used for classification and has come out with result (92%, 99%, 97%) respectively for GAR, while the GAR) for this algorithm using hybrid technique for classification (95%, 99%, 98%) respectively. To determine the accuracy of the multibiometric system for feature level fusion has been used, while the same preceding methods have been used for classification. The results have been (98%, 99%) respectively while to determine the GAR of feature level different methods have been used and have come out with (98%).

Keywords: back propagation neural network BP ANN, multibiometric system, parallel system decision-fusion, practical swarm intelligent PSO

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Authors: Alena Semeradtova, Marcel Stofik, Lucie Mareckova, Petr Maly, Ondrej Stanek, Jan Maly

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Recently, novel strategies based on so-called molecular evolution were shown to be effective for the production of various peptide ligand libraries with high affinities to molecular targets of interest comparable or even better than monoclonal antibodies. The major advantage of these peptide scaffolds is mainly their prevailing low molecular weight and simple structure. This study describes a new high-affinity binding molecules based immunesensor using a simple optical system for human serum albumin (HSA) detection as a model molecule. We present a comparison of two variants of recombinant binders based on albumin binding domain of the protein G (ABD) performed on micropatterned glass chip. Binding domains may be tailored to any specific target of interest by molecular evolution. Micropatterened glass chips were prepared using UV-photolithography on chromium sputtered glasses. Glass surface was modified by (3-aminopropyl)trietoxysilane and biotin-PEG-acid using EDC/NHS chemistry. Two variants of high-affinity binding molecules were used to detect target molecule. Firstly, a variant is based on ABD domain fused with TolA chain. This molecule is in vivo biotinylated and each molecule contains one molecule of biotin and one ABD domain. Secondly, the variant is ABD domain based on streptavidin molecule and contains four gaps for biotin and four ABD domains. These high-affinity molecules were immobilized to the chip surface via biotin-streptavidin chemistry. To eliminate nonspecific binding 1% bovine serum albumin (BSA) or 6% fetal bovine serum (FBS) were used in every step. For both variants range of measured concentrations of fluorescently labelled HSA was 0 – 30 µg/ml. As a control, we performed a simultaneous assay without high-affinity binding molecules. Fluorescent signal was measured using inverse fluorescent microscope Olympus IX 70 with COOL LED pE 4000 as a light source, related filters, and camera Retiga 2000R as a detector. The fluorescent signal from non-modified areas was substracted from the signal of the fluorescent areas. Results were presented in graphs showing the dependence of measured grayscale value on the log-scale of HSA concentration. For the TolA variant the limit of detection (LOD) of the optical immunosensor proposed in this study is calculated to be 0,20 µg/ml for HSA detection in 1% BSA and 0,24 µg/ml in 6% FBS. In the case of streptavidin-based molecule, it was 0,04 µg/ml and 0,07 µg/ml respectively. The dynamical range of the immunosensor was possible to estimate just in the case of TolA variant and it was calculated to be 0,49 – 3,75 µg/ml and 0,73-1,88 µg/ml respectively. In the case of the streptavidin-based the variant we didn´t reach the surface saturation even with the 480 ug/ml concentration and the upper value of dynamical range was not estimated. Lower value was calculated to be 0,14 µg/ml and 0,17 µg/ml respectively. Based on the obtained results, it´s clear that both variants are useful for creating the bio-recognizing layer on immunosensors. For this particular system, it is obvious that the variant based on streptavidin molecule is more useful for biosensing on glass planar surfaces. Immunosensors based on this variant would exhibit better limit of detection and wide dynamical range.

Keywords: high affinity binding molecules, human serum albumin, optical immunosensor, protein G, UV-photolitography

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Authors: Nor Zaidi Haron, Fauziyah Salehuddin, Norsuhaidah Arshad, Sani Irwan Salim

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Resistive random access memories (RRAMs) are one of the main candidates for future computer memories. However, due to their tiny size and immature device technology, the quality of the outgoing RRAM chips is seen as a serious issue. Defective RRAM cells might behave differently than existing semiconductor memories (Dynamic RAM, Static RAM, and Flash), meaning that they are difficult to be detected using existing test schemes. This paper presents an enhanced test scheme, referred to as Programmable Short Write Time (PSWT) that is able to improve the detection of faulty RRAM cells. It is developed by applying multiple weak write operations, each with different time durations. The test circuit embedded in the RRAM chip is made programmable in order to supply different weak write times during testing. The RRAM electrical model is described using Verilog-AMS language and is simulated using HSPICE simulation tools. Simulation results show that the proposed test scheme offers better open-resistive fault detection compared to existing test schemes.

Keywords: memory fault, memory test, design-for-testability, resistive random access memory

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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: García López

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The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account CDCgov, National Institutes of Health, NIH, the government agency HHSGov, and the professional organization PublicHealth. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have.

Keywords: emotions in tweets, emotion detection in the text, health information on Twitter, American health official accounts, emotions on Twitter, emotions and content

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Authors: Coline Bretz, Andrea Vaccaro, Dario Leumann

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The food, personal care, and cosmetic industries are seeing increased consumer demand for more sustainable and innovative ingredients. When developing new formulations incorporating such ingredients, stability is one of the first criteria that must be assessed, and it is thus of great importance to have a method that can detect instabilities early and quickly. Diffusing Wave Spectroscopy (DWS) is a light scattering technique that probes the motion,i.e., the mean square displacement (MSD), of colloids, such as nanoparticles in a suspension or droplets in emulsions. From the MSD, the rheological properties of the surrounding medium can be determined via the so-called microrheology approach. In the case of purely viscous media, it is also possible to obtain information about particle size. DWS can thus be used to monitor the size evolution of particles, droplets, or bubbles in aging dispersions, emulsions, or foams. In the context of early instability detection in emulsions, DWS offers considerable advantages, as the samples are measured in a contact-free manner, using only small quantities of samples loaded in a sealable cuvette. The sensitivity and rapidity of the technique are key to detecting and following the ageing of emulsions reliably. We present applications of DWS focused on the characterization of emulsions. In particular, we demonstrate the ability to record very subtle changes in the structural properties early on. We also discuss the various mechanisms at play in the destabilization of emulsions, such as coalescence or Ostwald ripening, and how to identify them through this technique.

Keywords: instrumentation, emulsions, stability, DWS

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Authors: N. Seemuang, T. McLeay, T. Slatter

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Worn tools have a direct effect on the surface finish and part accuracy. Tool condition monitoring systems have been developed over a long period and used to avoid a loss of productivity resulting from using a worn tool. However, the majority of tool monitoring research has applied expensive sensing systems not suitable for production. In this work, the cutting sound in turning machine was studied using microphone. Machining trials using seven cutting conditions were conducted until the observable flank wear width (FWW) on the main cutting edge exceeded 0.4 mm. The cutting inserts were removed from the tool holder and the flank wear width was measured optically. A microphone with built-in preamplifier was used to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The sound was sampled at 50 kS/s and all sound signals recorded from microphone were transformed into the frequency domain by FFT in order to establish the frequency content in the audio signature that could be then used for tool condition monitoring. The extracted feature from audio signal was compared to the flank wear progression on the cutting inserts. The spectrogram reveals a promising feature, named as ‘spindle noise’, which emits from the main spindle motor of turning machine. The spindle noise frequency was detected at 5.86 kHz of regardless of cutting conditions used on this particular CNC lathe. Varying cutting speed and feed rate have an influence on the magnitude of power spectrum of spindle noise. The magnitude of spindle noise frequency alters in conjunction with the tool wear progression. The magnitude increases significantly in the transition state between steady-state wear and severe wear. This could be used as a warning signal to prepare for tool replacement or adapt cutting parameters to extend tool life.

Keywords: tool wear, flank wear, condition monitoring, spindle noise

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Authors: Clara Tramuta, Lucia Decastelli, Elisa Barcucci, Sandra Fragassi, Samantha Lupi, Enrico Arletti, Melissa Bizzarri, Daniela Manila Bianchi

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Introdution: Food allergies occurs, in the Western World, 2% of adults and up to 8% of children. The protection of allergic consumers is guaranted, in Eurrope, by Regulation (EU) No 1169/2011 of the European Parliament which governs the consumer's right to information and identifies 14 food allergens to be mandatory indicated on the label. Among these, mustard is a popular spice added to enhance the flavour and taste of foods. It is frequently present as an ingredient in spice blends, marinades, salad dressings, sausages, and other products. Hypersensitivity to mustard is a public health problem since the ingestion of even low amounts can trigger severe allergic reactions. In order to protect the allergic consumer, high performance methods are required for the detection of allergenic ingredients. Food safety laboratories rely on validated methods that detect hidden allergens in food to ensure the safety and health of allergic consumers. Here we present the test results for the validation and accreditation of a Real time PCR assay (RT-PCR: SPECIALfinder MC Mustard, Generon), for the detection of mustard traces in food. Materials and Methods. The method was tested on five classes of food matrices: bakery and pastry products (chocolate cookies), meats (ragù), ready-to-eat (mixed salad), dairy products (yogurt), grains, and milling products (rice and barley flour). Blank samples were spiked starting with the mustard samples (Sinapis Alba), lyophilized and stored at -18 °C, at a concentration of 1000 ppm. Serial dilutions were then prepared to a final concentration of 0.5 ppm, using the DNA extracted by ION Force FAST (Generon) from the blank samples. The Real Time PCR reaction was performed by RT-PCR SPECIALfinder MC Mustard (Generon), using CFX96 System (BioRad). Results. Real Time PCR showed a limit of detection (LOD) of 0.5 ppm in grains and milling products, ready-to-eat, meats, bakery, pastry products, and dairy products (range Ct 25-34). To determine the exclusivity parameter of the method, the ragù matrix was contaminated with Prunus dulcis (almonds), peanut (Arachis hypogaea), Glycine max (soy), Apium graveolens (celery), Allium cepa (onion), Pisum sativum (peas), Daucus carota (carrots), and Theobroma cacao (cocoa) and no cross-reactions were observed. Discussion. In terms of sensitivity, the Real Time PCR confirmed, even in complex matrix, a LOD of 0.5 ppm in five classes of food matrices tested; these values are compatible with the current regulatory situation that does not consider, at international level, to establish a quantitative criterion for the allergen considered in this study. The Real Time PCR SPECIALfinder kit for the detection of mustard proved to be easy to use and particularly appreciated for the rapid response times considering that the amplification and detection phase has a duration of less than 50 minutes. Method accuracy was rated satisfactory for sensitivity (100%) and specificity (100%) and was fully validated and accreditated. It was found adequate for the needs of the laboratory as it met the purpose for which it was applied. This study was funded in part within a project of the Italian Ministry of Health (IZS PLV 02/19 RC).

Keywords: allergens, food, mustard, real time PCR

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: Md. Abdul Aziz

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Sulfide ion is water soluble, highly corrosive, toxic and harmful to the human beings. As a result, knowing the exact concentration of sulfide in water is very important. However, the existing detection and quantification methods have several shortcomings, such as high cost, low sensitivity, and massive instrumentation. Consequently, the development of novel sulfide sensor is relevant. Nevertheless, electrochemical methods gained enormous popularity due to a vast improvement in the technique and instrumentation, portability, low cost, rapid analysis and simplicity of design. Successful field application of electrochemical devices still requires vast improvement, which depends on the physical, chemical and electrochemical aspects of the working electrode. The working electrode made of bulk gold (Au) and platinum (Pt) are quite common, being very robust and endowed with good electrocatalytic properties. High cost, and electrode poisoning, however, have so far hindered their practical application in many industries. To overcome these obstacles, we developed a sulfide sensor based on an indium tin oxide nanoparticle (ITONP)-modified ITO electrode. To prepare ITONP-modified ITO, various methods were tested. Drop-drying of ITONPs (aq.) on aminopropyltrimethoxysilane-functionalized ITO (APTMS/ITO) was found to be the best method on the basis of voltammetric analysis of the sulfide ion. ITONP-modified APTMS/ITO (ITONP/APTMS/ITO) yielded much better electrocatalytic properties toward sulfide electro-οxidation than did bare or APTMS/ITO electrodes. The ITONPs and ITONP-modified ITO were also characterized using transmission electron microscopy and field emission scanning electron microscopy, respectively. Optimization of the type of inert electrolyte and pH yielded an ITONP/APTMS/ITO detector whose amperometrically and chronocoulοmetrically determined limits of detection for sulfide in aqueous solution were 3.0 µM and 0.90 µM, respectively. ITONP/APTMS/ITO electrodes which displayed reproducible performances were highly stable and were not susceptible to interference by common contaminants. Thus, the developed electrode can be considered as a promising tool for sensing sulfide.

Keywords: amperometry, chronocoulometry, electrocatalytic properties, ITO-nanoparticle-modified ITO, sulfide sensor

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Authors: Abdullah Bal

Abstract:

This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Moftah BinHasan

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Induction motors (IM) are vital components in industrial processes whose failure may yield to an unexpected interruption at the industrial plant, with highly incurred consequences in costs, product quality, and safety. Among different detection approaches proposed in the literature, that based on stator current monitoring termed as Motor Current Signature Analysis (MCSA) is the most preferred. MCSA is advantageous due to its non-invasive properties. The popularity of motor current signature analysis comes from being that the current consists of motor harmonics, around the supply frequency, which show some properties related to different situations of healthy and faulty conditions. One of the techniques used with machine line current resorts to spectrum analysis. Besides discussing the fundamentals of MCSA and its applications in the condition monitoring arena, this paper shows a summary of the most frequent faults and their consequence signatures on the stator current spectrum of an induction motor. In addition, this article presents different case studies of induction motor fault diagnosis. These faults were seeded in the machine which was run for more than an hour for each test before the results were recorded for the faulty situations. These results are then compared with those for the healthy cases that were recorded earlier.

Keywords: induction motor, condition monitoring, fault diagnosis, MCSA, rotor, stator, bearing, eccentricity

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