Search results for: object detection

Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Enqing Chen, Jianbo Wang

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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.

Keywords: edge detection, NSCT, shift invariant, modulus maxima

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Authors: Hao Cheng, Zhiwu Wang, Guozheng Yan, Pingping Jiang, Shijia Qin, Shuai Kuang

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Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm.

Keywords: AFI, edge detection, adaptive threshold, morphological processing, OV6930, FPGA

Procedia PDF Downloads 197

Authors: Juneseok You, Chanho Park, Kuehwan Jang, Sungsoo Na

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Sensing of nanotoxic materials is strongly important, as their engineering applications are growing recently and results in that nanotoxic material can harmfully influence human health and environment. In current study we report the quartz crystal microbalance (QCM)-based, in situ and real-time sensing of nanotoxic-material by frequency shift. We propose the in situ detection of nanotoxic material of zinc oxice by using QCM functionalized with a taget-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz electrode is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated the in-situ and fast detection of zinc oxide using the quartz crystal microbalance (QCM). The detection was derived from the DNA hybridization between the DNA on the quartz electrode. The results suggest that QCM-based detection opens a new avenue for the development of a practical water-testing sensor.

Keywords: nanotoxic material, qcm, frequency, in situ sensing

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Authors: Oluwole Opeyemi Dina, Saheed E. Elugoke, Peter Olutope Fayemi, Omolola E. Fayemi

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This study presents the detection of epinephrine (EP) at Fe₃O₄ modified screen printed silver electrode (SPSE). The iron oxide (Fe₃O₄) nanoparticles were characterized with UV-visible spectroscopy, Fourier-Transform infrared spectroscopy (FT-IR) and Scanning electron microscopy (SEM) prior to the modification of the SPSE. The EP oxidation peak current (Iap) increased with an increase in the concentration of EP as well as the scan rate (from 25 - 400 mVs⁻¹). Using cyclic voltammetry (CV), the relationship between Iap and EP concentration was linear over a range of 3.8 -118.9 µM and 118.9-175 µM with a detection limit of 41.99 µM and 83.16 µM, respectively. Selective detection of EP in the presence of ascorbic acid was also achieved at this electrode.

Keywords: screenprint electrode, iron oxide nanoparticle, epinephrine, serum, cyclic voltametry

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Authors: Ergun Sakalar, Kubra Bilgic

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Faster detection and characterization of pathogens are the basis of the evoid from foodborne pathogens. Salmonella spp., Shigella spp. and Listeria monocytogenes are common foodborne bacteria that are among the most life-threatining. It is important to rapid and accurate detection of these pathogens to prevent food poisoning and outbreaks or to manage food chains. The present work promise to develop a sensitive, species specific and reliable PCR based detection system for simultaneous detection of Salmonella spp., Shigella spp. and Listeria monocytogenes. For this purpose, three genes were picked out, ompC for Salmonella spp., ipaH for Shigella spp. and hlyA for L. monocytogenes. After short pre-enrichment of milk was passed through a vacuum filter and bacterial DNA was exracted using commercially available kit GIDAGEN®(Turkey, İstanbul). Detection of amplicons was verified by examination of the melting temperature (Tm) that are 72° C, 78° C, 82° C for Salmonella spp., Shigella spp. and L. monocytogenes, respectively. The method specificity was checked against a group of bacteria strains, and also carried out sensitivity test resulting in under 10² CFU mL⁻¹ of milk for each bacteria strain. Our results show that the flourescence based triplex qPCR method can be used routinely to detect Salmonella spp., Shigella spp. and L. monocytogenes during the milk processing procedures in order to reduce cost, time of analysis and the risk of foodborne disease outbreaks.

Keywords: evagreen, food-born bacteria, pathogen detection, real-time pcr

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Authors: Lidia N. Hernández-Piña, Carlos R. Jaimez-González

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Interoperability in distributed systems is an important feature that refers to the communication of two applications written in different programming languages. This paper presents a serializer and a de-serializer of PHP objects to and from XML, which is an independent library written in the PHP programming language. The XML generated by this serializer is independent of the programming language, and can be used by other existing Web Objects in XML (WOX) serializers and de-serializers, which allow interoperability with other object-oriented programming languages.

Keywords: interoperability, PHP object serialization, PHP to XML, web objects in XML, WOX

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Bendiabdellah Azzeddine, Cherif Bilal Djamal Eddine

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The three-phase power converter voltage structure is widely used in many power applications but its failure can lead to partial or total loss of control of the phase currents and can cause serious system malfunctions or even a complete system shutdown. To ensure continuity of service in all circumstances, effective and rapid techniques of detection and location of inverter fault is to be implemented. The present paper is dedicated to open-circuit fault detection in a three-phase two-level inverter fed induction motor. For detection purpose, the proposed contribution addresses the Park’s current vectors associated to a polar coordinates calculation tool to compute the exact value of the fault angle corresponding directly to the faulty IGBT switch. The merit of the proposed contribution is illustrated by experimental results.

Keywords: diagnosis, detection, Park’s vectors, polar coordinates, open-circuit fault, inverter, IGBT switch

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Authors: Rana Gill, Chandandeep Kaur

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Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts.

Keywords: segmentation, edge detection, text, extracting characters

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Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

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Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

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Authors: K. A. Tharanga, K. H. H. Damayanthi

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Perception is an epistemological concept discussed in Philosophy. Perception, in other word, vision, is one of the ways that human beings get empirical knowledge after five senses. However, we face innumerable problems when achieving knowledge from perception, and therefore the knowledge gained through perception is uncertain. what we see in the external world is not real. These are the major issues that we face when receiving knowledge through perception. Sometimes there is no physical existence of what we really see. In such cases, the perception is relative. The following frames will be taken into consideration when perception is analyzed illusions and delusions, the figure of a physical object, appearance and the reality of a physical object, time factor, and colour of a physical object.seeing and knowing become vary according to the above conceptual frames. We cannot come to a proper conclusion of what we see in the empirical world. Because the things that we see are not really there. Hence the scientific knowledge which is gained from observation is doubtful. All the factors discussed in science remain in the physical world. There is a leap from ones existence to the existence of a world outside his/her mind. Indeed, one can suppose that what he/she takes to be real is just anmassive deception. However, depending on the above facts, if someone begins to doubt about the whole world, it is unavoidable to become his/her view a scepticism or nihilism. This is a certain reality.

Keywords: empirical, perception, sceptisism, nihilism

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Authors: Peter U. Eze, P. Udaya, Robin J. Evans

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Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, p. The constant correlation p, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from p. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost.

Keywords: Constant Correlation, Medical Image, Spread Spectrum, Tamper Detection, Watermarking

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Authors: Md. Moniruzzaman

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The energy of ocean waves across a large part of the earth is inexhaustible. The whole world will benefit if this endless energy can be used in an easy way. The coastal countries will easily be able to meet their own energy needs. The purpose of this article is to use the infinite energy of the ocean wave in a simple way. i.e. a method of efficient use of wave energy. The paper starts by discussing various forces acting on a floating object and, afterward, about the method. And then a calculation for a 73.39MW hydropower from the tidal wave. Used some sketches/pictures. Finally, the conclusion states the possibilities and advantages.

Keywords: anchor, electricity, floating object, pump, ship city, wave energy

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Authors: Ji Su Kim, Bo Ram Choi, Ju Yeon Cho, Hyukjin Lee

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Epidermal growth factor receptor (EGFR) 19 deletion mutation gene is over-expressed in carcinoma patient. EGFR 19 deletion mutation is known as typical biomarker of non-small cell lung cancer (NSCLC), which one section in the coding exon 19 of EGFR is deleted. Therefore, there have been many attempts over the years to detect EGFR 19 deletion mutation for replacing conventional diagnostic method such as PCR and tissue biopsy. We developed a simple and facile detection platform based on Rolling Circle Amplification (RCA), which provides highly amplified products in isothermal amplification of the ligated DNA template. Limit of detection (~50 nM) and a faster detection time (~30 min) could be achieved by introducing RCA.

Keywords: EGFR19, cancer, diagnosis, rolling circle amplification (RCA), hydrogel

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Mehravar Javid, Rohma Hassan

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Understanding a patient’s hallucinations requires a degree of cultural humility when these experiences are consistent with beliefs that are normative to the patient’s respective culture and religion, and in this exploratory case study, the treatment history of a 32-year-old female Iranian patient who sought psychoanalytic treatment in Iran is explored, who stated that she had been witnessing jinn since she was fifteen-years-old. She experienced considerable disempowerment and lack of support in her upbringing and curiously believes that the jinn provide her with comfort and power, yet simultaneously create a sense of fear and horror. When her analyst wonders about a possible link between the jinn and the patient’s self-object needs and what was denied for her in her youth, the patient becomes resistant to treatment, especially when the possibility of the jinn existing as hallucinations is raised. Throughout the course of therapy, the patient discusses her tumultuous marriage, her strained relationship with her family, and inner conflicts. She also begins to find solace in her relationship with her therapist, satisfying her self-object needs and enabling her to widen her self-awareness and wish for deeper connections with others. In understanding her needs and fears, the role of the jinn in her psychological landscape aims to be understood, with a larger discussion of how to work with patients experiencing supernatural phenomena and how the phenomena serve as an object, whether real or imaginary. The overall aim is to shed light on the intricate interplay between cultural and religious beliefs and psychological manifestations.

Keywords: cultural considerations, jinn, projective identification, self-object needs, transference

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Authors: Pardeep Singh, Kamlesh Dutta

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Many languages have fixed sentence structure and others are free word order. The accuracy of anaphora resolution of syntax based algorithm depends on structure of the sentence. So, it is important to analyze the structure of any language before implementing these algorithms. In this study, we analyzed the sentence structure exploiting the case marker in Hindi as well as some special tag for subject and object. We also investigated the word order for Hindi. Word order typology refers to the study of the order of the syntactic constituents of a language. We analyzed 165 news items of Ranchi Express from EMILEE corpus of plain text. It consisted of 1745 sentences. Eight file of dialogue based from the same corpus has been analyzed which will have 1521 sentences. The percentages of subject object verb structure (SOV) and object subject verb (OSV) are 66.90 and 33.10, respectively.

Keywords: anaphora resolution, free word order languages, SOV, OSV

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Authors: Tian Xia, Yuan Yan Tang

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In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection.

Keywords: small target detection, local contrast, human vision system, Laplacian of Gaussian

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Authors: Joanne Hardman

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South Africa’s crisis in mathematics attainment is well documented. To meet the need to develop students’ mathematical performance in schools the government has launched various initiatives using computers to impact on mathematical attainment. While it is clear that computers can change pedagogical practices, there is a dearth of qualitative studies indicating exactly how pedagogy is transformed with Information Communication Technologies (ICTs) in a teaching activity. Consequently, this paper addresses the following question: how, along which dimensions in an activity, does pedagogy alter with the use of computer drill and practice software in four disadvantaged grade 6 mathematics classrooms in the Western Cape province of South Africa? The paper draws on Cultural Historical Activity Theory (CHAT) to develop a view of pedagogy as socially situated. Four ideal pedagogical types are identified: Reinforcement pedagogy, which has the reinforcement of specialised knowledge as its object; Collaborative pedagogy, which has the development of metacognitive engagement with specialised knowledge as its object; Directive pedagogy, which has the development of technical task skills as its object, and finally, Defensive pedagogy, which has student regulation as its object. Face-to-face lessons were characterised as predominantly Reinforcement and Collaborative pedagogy and most computer lessons were characterised as mainly either Defensive or Directive.

Keywords: computers, cultural historical activity theory, mathematics, pedagogy

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Authors: Laid Fekih

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Background: It is difficult to detect lying, deception, and misrepresentation just by looking at verbal or non-verbal expression during the criminal investigation process, as there is a common belief that it is possible to tell whether a person is lying or telling the truth just by looking at the way they act or behave. The process of detecting lies and deception during the criminal investigation process needs more studies and research to overcome the difficulties facing the investigators. Method: The present study aimed to identify the effectiveness of cognitive methods and techniques in detecting deception during the criminal investigation. It adopted the quasi-experimental method and covered a sample of (20) defendants distributed randomly into two homogeneous groups, an experimental group of (10) defendants be subject to criminal investigation by applying cognitive techniques to detect deception and a second experimental group of (10) defendants be subject to the direct investigation method. The tool that used is a guided interview based on models of investigative questions according to the cognitive deception detection approach, which consists of three techniques of Vrij: imposing the cognitive burden, encouragement to provide more information, and ask unexpected questions, and the Direct Investigation Method. Results: Results revealed a significant difference between the two groups in term of lie detection accuracy in favour of defendants be subject to criminal investigation by applying cognitive techniques, the cognitive deception detection approach produced superior total accuracy rates both with human observers and through an analysis of objective criteria. The cognitive deception detection approach produced superior accuracy results in truth detection: 71%, deception detection: 70% compared to a direct investigation method truth detection: 52%; deception detection: 49%. Conclusion: The study recommended if practitioners use a cognitive deception detection technique, they will correctly classify more individuals than when they use a direct investigation method.

Keywords: the cognitive lie detection approach, deception, criminal investigation, mental health

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Authors: Dmitry Usenco

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The greatest paradox about the origin of language is the fact that, while language is always taught by adults to children, it can never be learnt properly unless its acquisition occurs during childhood. The question that naturally arises in that respect is as follows: How could language be taught for the first time by a non-speaker, i.e., by someone who did not have the opportunity to master it as a child? Yet the above paradox will appear less unresolvable if we hypothesise that language was originally introduced not as a means of communication but as a relatively modest training/playing technique that was used to develop the learners’ mimetic skills. Its communicative and expressive properties could have been discovered and exploited later – upon the learners’ reaching their adulthood. The importance of mimesis in children’s development is universally recognised. The most common forms of it are onomatopoeia and mime, which consist in reproducing sounds and imitating shapes/movements of externally observed objects. However, in some cases, neither of these exercises can be adequate to the task. An object, especially an inanimate one, may emit no characteristic sounds, making onomatopoeia problematic. In other cases, it may have no easily reproduceable shape, while its movements may depend on the specific way of our interacting with it. On such occasions, onomatopoeia and mime can perhaps be supplemented, or even replaced, by movements of the tongue which can metonymically represent certain aspects of our interaction with the object. This is especially evident with consonants: e.g., a fricative sound can designate the subject’s relatively slow approach to the object or vice versa, while a plosive one can express the relatively abrupt process of grabbing/sticking or parrying/bouncing. From that point of view, a protoword can be regarded as a sophisticated gesture of the tongue but also as a mnemonic sequence that contains encoded instructions about the way to handle the object. When this originally subjective link between the object and its mimetic/mnemonic representation eventually installs itself in the collective mind (however small at first the community might be), the initially nameless object acquires a name, and the first word is created. (Discussing the difference between proper and common names is out of the scope of this paper). In its very beginning, this word has two major applications. It can be used for interhuman communication because it allows us to invoke the presence of a currently absent object. It can also be used for designing, expressing, and memorising our interaction with the object itself. The first usage gives rise to language, the second to religion. By the act of naming, we attach to the object a mental (‘spiritual’) dimension which has an independent existence in our collective mind. By referring to the name (idea/demon/soul) of the object, we perform our first act of spirituality, our first religious observance. This is the beginning of animism – arguably, the most ancient form of religion. To conclude: the rise of religion is simultaneous with the the emergence of language in human evolution.

Keywords: language, religion, origin, acquisition, childhood, adulthood, play, represntation, onomatopoeia, mime, gesture, consonant, simultaneity, spirituality, animism

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Authors: Xiaoyuan Suo

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3D object reconstruction is a broad research area within the computer vision field involving many stages and still open problems. One of the existing challenges in this field lies with micromotion, such as the facial expressions on the appearance of the human or animal face. Similar literatures in this field focuses on 3D reconstruction in stable conditions such as an existing image or photos taken in a rather static environment, while the purpose of this work is to discuss a flexible scan system using multiple cameras that can correctly reconstruct 3D stable and moving objects -- human face with expression in particular. Further, a mathematical model is proposed at the end of this literature to automate the 3D object reconstruction process. The reconstruction process takes several stages. Firstly, a set of simple 2D lines would be projected onto the object and hence a set of uneven curvy lines can be obtained, which represents the 3D numerical data of the surface. The lines and their shapes will help to identify object’s 3D construction in pixels. With the two-recorded angles and their distance from the camera, a simple mathematical calculation would give the resulting coordinate of each projected line in an absolute 3D space. This proposed research will benefit many practical areas, including but not limited to biometric identification, authentications, cybersecurity, preservation of cultural heritage, drama acting especially those with rapid and complex facial gestures, and many others. Specifically, this will (I) provide a brief survey of comparable techniques existing in this field. (II) discuss a set of specialized methodologies or algorithms for effective reconstruction of 3D objects. (III)implement, and testing the developed methodologies. (IV) verify findings with data collected from experiments. (V) conclude with lessons learned and final thoughts.

Keywords: 3D photogrammetry, 3D object reconstruction, facial expression recognition, facial recognition

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Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Jaiyeop Lee

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Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals.

Keywords: rapid detection, SARS-CoV-2, sensitive detection, wastewater surveillance

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Authors: Mahdi Rahaie

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MicroRNAs are a novel class of small RNAs which regulate gene expression by translational repression or degradation of messenger RNAs. To produce sensitive, simple and cost-effective assays for microRNAs, detection is in urgent demand due to important role of these biomolecules in progression of human disease such as Alzheimer’s, Multiple sclerosis, and some other neurodegenerative diseases. Herein, we report several novel, sensitive and specific microRNA nanobiosensors which were designed based on colorimetric and fluorescence detection of nanoparticles and hybridization chain reaction amplification as an enzyme-free amplification. These new strategies eliminate the need for enzymatic reactions, chemical changes, separation processes and sophisticated equipment whereas less limit of detection with most specify are acceptable. The important features of these methods are high sensitivity and specificity to differentiate between perfectly matched, mismatched and non-complementary target microRNAs and also decent response in the real sample analysis with blood plasma. These nanobiosensors can clinically be used not only for the early detection of neuro diseases but also for every sickness related to miRNAs by direct detection of the plasma microRNAs in real clinical samples, without a need for sample preparation, RNA extraction and/or amplification.

Keywords: hybridization chain reaction, microRNA, nanobiosensor, neurodegenerative diseases

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Authors: Bright Emeni

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The project is a system that allows users to locate their misplaced or lost devices by using Bluetooth technology. It utilizes the DFRobot Bettle BLE Arduino microcontroller as its main component for communication and control. By interfacing it with an LED and a buzzer, the system provides visual and auditory signals to assist in locating the target device. The search process can be initiated through an Android application, by which the system creates a Bluetooth connection between the microcontroller and the target device, permitting the exchange of signals for tracking purposes. When the device is within range, the LED indicator illuminates, and the buzzer produces audible alerts, guiding the user to the device's location. The application also provides an estimated distance of the object using Bluetooth signal strength. The project’s goal is to offer a practical and efficient solution for finding misplaced devices, leveraging the capabilities of Bluetooth technology and microcontroller-based control systems.

Keywords: Bluetooth finder, object finder, Bluetooth tracking, tracker

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

Abstract:

Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

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