Search results for: signal classification

Authors: Hussin K. Ragb, Vijayan K. Asari

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

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Authors: Pukhtoon Yar

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Peshawar City is experiencing a rapid spatial urban growth primarily as a result of high rate of urbanization along with economic development. This paper was designed to understand the impacts of urbanization on agriculture land use change by particularly focusing on land use change trajectories from the past (1991-2014). We used Landsat imageries (30 meters) for1991along with Spot images (2.5 meters) for year 2014. . The ground truthing of the satellite data was performed by collecting information from Peshawar Development Authority, revenue department, real estate agents and interviews with the officials of city administration. The temporal satellite images were processed by applying supervised maximum likelihood classification technique in ArcGIS 9.3. The procedure resulted into five main classes of land use i.e. built-up area, farmland, barren land, cultivable-wasteland and water bodies. The analysis revealed that, in Peshawar City the built-up environment has been doubled from 8.1 percent in 1991 to over 18.2 percent in 2014 by predominantly encroaching land producing food. Furthermore, the CA-Markov Model predicted that the area under impervious surfaces would continue to flourish during the next three decades. This rapid increase in built-up area is accredited to the lack of proper land use planning and management, which has caused chaotic urban sprawl with detrimental social and environmental consequences.

Keywords: Urban Expansion, Land use, GIS, Remote Sensing, Markov Model, Peshawar City

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Justin Lee, Siraj Shaikh, Alice Chu MD

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Children with cerebral palsy (CP) commonly present with upper extremity impairment, affecting one or both extremities, and are classified using the Manual Ability Classification Scale (MACS). The MACS defines bimanual hand abilities for children ages 4-18 years in everyday tasks and is a gradient scale, with I being nearly normal and V requiring total assistance. Children with more severe upper extremity impairment (MACS III-V) are often underrepresented, and relatively few effective therapies have been identified for these patients. Current orthoses are static and are only meant to prevent the progression of contractures in these patients. Other limitations include cost, comfort, accessibility, and longevity of the orthoses. Taking advantage of advances in 3D printing technology, we have created a highly customizable upper extremity orthotic that can be produced at a low cost. Iterations in our design have resulted in an orthotic that is custom fit to the patient based on scans of their arm, made of rigid polymer when needed to provide support, flexible material where appropriate to allow for comfort, and designed with a mechanical pulley system to allow for some functional use of the arm while in the orthotic. Preliminary data has shown that our orthotic can be built at a fraction of the cost of current orthoses and provide clinically significant improvement in assisting hand assessment (AHA) and pediatric quality of life scores (PedsQL).

Keywords: upper extremity orthosis, upper extremity, orthosis, 3-D printing, cerebral palsy, occupational therapy, spasticity, customizable

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Authors: Tahseen Saad, Halim Ceylan, Jonathan Weaver, Osman Nuri Çelik, Onur Gungor Sahin

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A new concept of uniform delay offset dependent mathematical optimization problem is derived as the main objective for this study using a differential evolution algorithm. To control the coordination problem, which depends on offset selection and to estimate uniform delay based on the offset choice in a traffic signal network. The assumption is the periodic sinusoidal function for arrival and departure patterns. The cycle time is optimized at the entry links and the optimized value is used in the non-entry links as a common cycle time. The offset optimization algorithm is used to calculate the uniform delay at each link. The results are illustrated by using a case study and are compared with the canonical uniform delay model derived by Webster and the highway capacity manual’s model. The findings show new model minimizes the total uniform delay to almost half compared to conventional models. The mathematical objective function is robust. The algorithm convergence time is fast.

Keywords: area traffic control, traffic flow, differential evolution, sinusoidal periodic function, uniform delay, offset variable

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Authors: Dasom Kim, Chen Liu, Myungsu Lim, Su-Hyeon Jeon, ByeoungKug Jeon, Kee-Young Kwahk, Namgyu Kim

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Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we previously proposed a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. In this paper, we design a survey-based verification scenario for estimating the accuracy of our automatic categorization methodology.

Keywords: big data analysis, document classification, multi-category, text mining, topic analysis

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

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

Keywords: archaea, bacteria, detection, FISH, fluorescence

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

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

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

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Authors: M. Draman, S. Z. Muhamad Yassin, M. S. Alias, Z. Lambak, M. I. Zulkifli, S. N. Padhi, K. N. Baharim, F. Maskuriy, A. I. A. Rahim

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The performance of Malay automatic speech recognition (ASR) system for the call centre environment is presented. The system utilizes Kaldi toolkit as the platform to the entire library and algorithm used in performing the ASR task. The acoustic model implemented in this system uses a deep neural network (DNN) method to model the acoustic signal and the standard (n-gram) model for language modelling. With 80 hours of training data from the call centre recordings, the ASR system can achieve 72% of accuracy that corresponds to 28% of word error rate (WER). The testing was done using 20 hours of audio data. Despite the implementation of DNN, the system shows a low accuracy owing to the varieties of noises, accent and dialect that typically occurs in Malaysian call centre environment. This significant variation of speakers is reflected by the large standard deviation of the average word error rate (WERav) (i.e., ~ 10%). It is observed that the lowest WER (13.8%) was obtained from recording sample with a standard Malay dialect (central Malaysia) of native speaker as compared to 49% of the sample with the highest WER that contains conversation of the speaker that uses non-standard Malay dialect.

Keywords: conversational speech recognition, deep neural network, Malay language, speech recognition

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Authors: Nana Saralidze, Nino Sharashenidze, Zurab Kevanishvili

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

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

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Authors: Pooja Ahuja, Tejal Bhutani, M. S. Dahiya

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Cheiloscopy, the study of lip prints, is a tool in forensic investigation technique that deals with identification of individuals based on lips patterns. The objective of this study is to determine predominant lip print pattern found among the Gujarati population, to evaluate whether any sex difference exists and to study the permanence of the pattern over six months duration. The study comprised of 100 healthy individuals (50 males and 50 females), in the age group of 18 to 25 years of Gujarati population of the Gandhinagar region of the Gujarat state, India. By using Suzuki and Tsuchihashi classification, Lip prints were then divided into four quadrants and also classified on the basis of peripheral shape of the lips. Materials used to record the lip prints were dark brown colored lipstick, cellophane tape, and white bond paper. Lipstick was applied uniformly, and lip prints were taken on the glued portion of cellophane tape and then stuck on to a white bond paper. These lip prints were analyzed with magnifying lens and virtually with stereo microscope. On the analysis of the subject population, results showed Branched pattern Type II (29.57 percentage) to be most predominant in the Gujarati population. Branched pattern Type II (35.60 percentage) and long vertical Type I (28.28 percentage) were most prevalent in males and females respectively and large full lips were most predominantly present in both the sexes. The study concludes that lip prints in any form can be an effective tool for identification of an individual in a closed or open group forms.

Keywords: cheiloscopy, lip pattern, predomianant, Gujarati population

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Authors: Vertika Goswami, Gargi Sharma

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The rapid evolution of fintech has led to the rise of robo-advisor platforms that utilize artificial intelligence (AI) and machine learning to offer personalized investment solutions efficiently and cost-effectively. This research paper conducts a comprehensive sentiment analysis of investor experiences with these platforms, employing natural language processing (NLP) and sentiment classification techniques. The study investigates investor perceptions, engagement, and satisfaction, identifying key drivers of positive sentiment such as clear communication, low fees, consistent returns, and robust security. Conversely, negative sentiment is linked to issues like inconsistent performance, hidden fees, poor customer support, and a lack of transparency. The analysis reveals that addressing these pain points—through improved transparency, enhanced customer service, and ongoing technological advancements—can significantly boost investor trust and satisfaction. This paper contributes valuable insights into the fields of behavioral finance and fintech innovation, offering actionable recommendations for stakeholders, practitioners, and policymakers. Future research should explore the long-term impact of these factors on investor loyalty, the role of emerging technologies, and the effects of ethical investment choices and regulatory compliance on investor sentiment.

Keywords: artificial intelligence in finance, automated investment, financial technology, investor satisfaction, investor sentiment, robo-advisors, sentimental analysis

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Authors: Vimanyu Chadha, Ranganath M. Singari

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

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

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Authors: Xue Wang, Li-Wei Fan

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The technology innovation has become the driving force of social and economic development and transformation. The guidance and support of public policies is an important condition to promote the realization of technology innovation goals. Policy effectiveness evaluation is instructive in policy learning and adjustment. This paper reviews existing studies and systematically evaluates the effectiveness of policy-driven technological innovation. We used 167 articles from WOS and CNKI databases as samples to clarify the measurement of technological innovation indicators and analyze the classification and application of policy evaluation methods. In general, technology innovation input and technological output are the two main aspects of technological innovation index design, among which technological patents are the focus of research, the number of patents reflects the scale of technological innovation, and the quality of patents reflects the value of innovation from multiple aspects. As for policy evaluation methods, statistical analysis methods are applied to the formulation, selection and evaluation of the after-effect of policies to analyze the effect of policy implementation qualitatively and quantitatively. The bibliometric methods are mainly based on the public policy texts, discriminating the inter-government relationship and the multi-dimensional value of the policy. Decision analysis focuses on the establishment and measurement of the comprehensive evaluation index system of public policy. The economic analysis methods focus on the performance and output of technological innovation to test the policy effect. Finally, this paper puts forward the prospect of the future research direction.

Keywords: technology innovation, index, policy effectiveness, evaluation of policy, bibliometric analysis

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

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

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

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Authors: S. Hutasavi, D. Chen

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The built-up area is a significant proxy to measure regional economic growth and reflects the Gross Provincial Product (GPP). However, an up-to-date and reliable database of built-up areas is not always available, especially in developing countries. The cloud-based geospatial analysis platform such as Google Earth Engine (GEE) provides an opportunity with accessibility and computational power for those countries to generate the built-up data. Therefore, this study aims to extract the built-up areas in Eastern Economic Corridor (EEC), Thailand using day and nighttime satellite imagery based on GEE facilities. The normalized indices were generated from Landsat 8 surface reflectance dataset, including Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Modified Built-up Index (MBUI). These indices were applied to identify built-up areas in EEC. The result shows that MBUI performs better than BUI and NDBI, with the highest accuracy of 0.85 and Kappa of 0.82. Moreover, the overall accuracy of classification was improved from 79% to 90%, and error of total built-up area was decreased from 29% to 0.7%, after night-time light data from the Visible and Infrared Imaging Suite (VIIRS) Day Night Band (DNB). The results suggest that MBUI with night-time light imagery is appropriate for built-up area extraction and be utilize for further study of socioeconomic impacts of regional development policy over the EEC region.

Keywords: built-up area extraction, google earth engine, adaptive thresholding method, rapid mapping

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Authors: Saad M. Darwish, Magda M. Madbouly, Murad B. Khorsheed

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Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper, several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD). SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi attribute hand gesture data to feature vectors. SVD optimally exposes the geometric structure of a matrix. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additive constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 86.21% for cluttered hand images.

Keywords: hand gesture recognition, hand detection, type-2 fuzzy logic, hidden Markov Model

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

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

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

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

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

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

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

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

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

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Authors: Ahmad Idris, O.A. Uche, Ado Y Abdulfatah

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Lateritic soils are found in abundance and are the most common types of soils used in construction of roads and embankments in Nigeria. Strength properties of the soils depend on the amount of compaction applied and the amount of water available in the soil at the time of compaction. In this study, the influence of the compactive effort and that of the amount of water in the soil in the determination of the shear strength properties of lateritic soil was investigated. Lateritic soil sample was collected from an existing borrow pit in Kano, Nigeria and its basic characteristics were determined and the soil was classified according to AASHTO classification method. The soil was then compacted under various compactive efforts and at wide range of moisture contents. The maximum dry density (MDD) and optimum moisture content (OMC) at each compactive effort was determined. Unconfined undrained triaxial test was carried out to determine the shear strength properties of the soil under various conditions of moisture and energy. Preliminary results obtained indicated that the soil is an A-7-5 soil. The final results obtained shows that as the compaction energy is increased, both the cohesion and friction angle increased irrespective of the moisture content used in the compaction. However, when the amount of water in the soil was increased and compaction effort kept constant, only the cohesion of the soil increases while the friction angle shows no any pattern of variation. It was also found that the highest values for cohesion and friction angle were obtained when the soil was compacted at the highest energy and at OMC.

Keywords: laterite, OMC, compaction energy, moisture content

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Authors: Arka Roy, Chandra Prakash Dubey

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

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

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Authors: Nur Afiqah Mohd Kamal, Khamarrul Azahari Razak

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The earthquake aftermath has become a major concern, especially in high seismicity region. In Kundasang, Sabah, the earthquake on 5th June 2015 resulted in several catastrophes; landslides, rockfalls, mudflows and major slopes affected regardless of the series of the aftershocks. Certainly, the consequences of earthquake generate and induce the episodic disaster, not only life-threatening but it also affects infrastructure and economic development. Therefore, a need for investigating the change in land use and land cover (LULC) of post-earthquake geohazard is essential for identifying the extent of disastrous effects towards the development in Kundasang. With the advancement of remote sensing technology, post-earthquake geohazards (landslides, mudflows, rockfalls, debris flows) assessment can be evaluated by the employment of object-based image analysis in investigating the LULC change which consists of settlements, public infrastructure and vegetation cover. Therefore, this paper discusses the preliminary results on post-earthquakes geohazards distribution in Kundasang and evaluates the LULC classification effect upon the occurrences of geohazards event. The result of this preliminary analysis will provide an overview to determine the extent of geohazard impact on LULC. This research also provides beneficial input to the local authority in Kundasang about the risk of future structural development on the geohazard area.

Keywords: geohazard, land use land cover, object-based image analysis, remote sensing

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Authors: Maryam Amiri, Ali Rajabzade, Gholam Reza Goudarzi, Reza Heidari

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Growth of technology and environmental changes are so fast and therefore, companies and industries have much tendency to do activities of R&D for active participation in the market and achievement to a competitive advantages. Aviation industry and its subdivisions have high level technology and play a special role in economic and social development of countries. So, in the aviation industry for getting new technologies and competing with other countries aviation industry, there is a requirement for capability in R&D. Considering of appropriate R&D strategy is supportive that day technologies of the world can be achieved. Biofuel technology is one of the newest technologies that has allocated discussion of the world in aviation industry to itself. The purpose of this research has been formulation of R&D strategy of biofuel technology in aviation industry of Iran. After reviewing of the theoretical foundations of the methods and R&D strategies, finally we classified R&D strategies in four main categories as follows: internal R&D, collaboration R&D, out sourcing R&D and in-house R&D. After a review of R&D strategies, a model for formulation of R&D strategy with the aim of developing biofuel technology in aviation industry in Iran was offered. With regard to the requirements and aracteristics of industry and technology in the model, we presented an integrated approach to R&D. Based on the techniques of decision making and analyzing of structured expert opinion, 4 R&D strategies for different scenarios and with the aim of developing biofuel technology in aviation industry in Iran were recommended. In this research, based on the common features of the implementation process of R&D, a logical classification of these methods are presented as R&D strategies. Then, R&D strategies and their characteristics was developed according to the experts. In the end, we introduced a model to consider the role of aviation industry and biofuel technology in R&D strategies. And lastly, for conditions and various scenarios of the aviation industry, we have formulated a specific R&D strategy.

Keywords: aviation industry, biofuel technology, R&D, R&D strategy

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Authors: Michael A. Kraus, Navid Pourmoghaddam, Martin Botz, Jens Schneider, Geralt Siebert

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Fragmentation analysis of tempered glass gives insight into the quality of the tempering process and defines a certain degree of safety as well. Different standard such as the European EN 12150-1 or the American ASTM C 1048/CPSC 16 CFR 1201 define a minimum number of fragments required for soda-lime safety glass on the basis of fragmentation test results for classification. This work presents an approach for the glass breakage pattern prediction using a Voronoi Tesselation over Random Fields. The random Voronoi tessellation is trained with and validated against data from several breakage patterns. The fragments in observation areas of 50 mm x 50 mm were used for training and validation. All glass specimen used in this study were commercially available soda-lime glasses at three different thicknesses levels of 4 mm, 8 mm and 12 mm. The results of this work form a Bayesian framework for the training and prediction of breakage patterns of tempered soda-lime glass using a Voronoi Random Field Tesselation. Uncertainties occurring in this process can be well quantified, and several statistical measures of the pattern can be preservation with this method. Within this work it was found, that different Random Fields as basis for the Voronoi Tesselation lead to differently well fitted statistical properties of the glass breakage patterns. As the methodology is derived and kept general, the framework could be also applied to other random tesselations and crack pattern modelling purposes.

Keywords: glass breakage predicition, Voronoi Random Field Tessellation, fragmentation analysis, Bayesian parameter identification

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Authors: Rattana Sangchan, Pattaraporn Thampradit

Abstract:

Teaching students to use a variety of reading methods in developing reading is essential for Thai university students. However, there haven’t been a lot of studies concerned about developing reading methods that are used by Thai students in the industrial education field. Therefore, this study was carried out not only to investigate the developing reading methods of Industrial Education students at King Mongkut’s Institute of Technology Ladkrabang, but also to determine if the developing reading strategies differ among the students’ reading abilities and differ gender: male and female. The research instrument used in collecting the data consisted of fourteen statements which include either metacognitive strategies, cognitive strategies or social / affective strategies. Results of this study revealed that students could develop their reading methods in moderate level (mean=3.13). Furthermore, high reading ability students had different levels of using reading methods to develop their reading from those of mid reading ability students. In addition, high reading ability students could use either metacognitive reading methods or cognitive reading methods to develop their reading much better than mid reading ability students. Interestingly, male students could develop their reading methods in great levels while female students could develop their reading methods only in moderate level. Last but not least, male students could use either metacognitive reading methods or cognitive reading methods to develop their reading much better than female students. Thus, the results of this study could indicate that most students need to apply much more reading strategies to develop their reading. At the same time, suggestions on how to motivate and train their students to apply much more appropriate effective reading strategies to better comprehend their reading were also provided.

Keywords: developing reading methods, industrial education, reading abilities, reading method classification

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Authors: Rong-Terng Juang

Abstract:

Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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

Abstract:

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

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

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Authors: Yagahira E. Castro-Sesquen, Chloe Kim, Robert H. Gilman, David J. Sullivan, Peter C. Searson

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Diagnosis of severe malaria is particularly important in highly endemic regions since most patients are positive for parasitemia and treatment differs from non-severe malaria. Diagnosis can be challenging due to the prevalence of diseases with similar symptoms. Accurate diagnosis is increasingly important to avoid overprescribing antimalarial drugs, minimize drug resistance, and minimize costs. A nanoparticle-based assay for detection and quantification of Plasmodium falciparum histidine-rich protein 2 (HRP2) in urine and serum is reported. The assay uses magnetic beads conjugated with anti-HRP2 antibody for protein capture and concentration, and antibody-conjugated quantum dots for optical detection. Western Blot analysis demonstrated that magnetic beads allows the concentration of HRP2 protein in urine by 20-fold. The concentration effect was achieved because large volume of urine can be incubated with beads, and magnetic separation can be easily performed in minutes to isolate beads containing HRP2 protein. Magnetic beads and Quantum Dots 525 conjugated to anti-HRP2 antibodies allows the detection of low concentration of HRP2 protein (0.5 ng mL-1), and quantification in the range of 33 to 2,000 ng mL-1 corresponding to the range associated with non-severe to severe malaria. This assay can be easily adapted to a non-invasive point-of-care test for classification of severe malaria.

Keywords: HRP2 protein, malaria, magnetic beads, Quantum dots

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Authors: Min Kyung An

Abstract:

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

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

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