Search results for: elliptic curve digital signature algorithm

Authors: Sahar Shams

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Coeliac disease (CD) is a widely reported disease particularly in countries with predominant Caucasian populations. It presents with many signs and symptoms including iron deficiency (ID) and iron deficiency anaemia/anaemia (IDA/A). The exact association between ID, IDA/A and CD and how accurate these signs are in diagnosing CD is not fully known. This systematic review was conducted to investigate the accuracy of both ID & IDA/A as a diagnostic indicator for CD and whether it warrants point of care testing. A systematic review was performed looking at studies published in MEDLINE, Embase, Cochrane Library, and Web of Science. QUADAS-2 tool was used to assess risk of bias in each study. ROC curve and forest plots were generated as part of the meta-analysis after data extraction. 16 studies were identified in total, 13 of which were IDA/A studies and 3 ID studies. The prevalence of CD regardless of diagnostic indicator was assumed as 1%. The QUADAS-2 tool indicated most of studies as having high risk of bias. The PPV for CD was higher in those with ID than for those with IDA/A. Meta-analysis showed the overall odds of having CD is 5 times higher in individuals with ID & IDA/A. The ROC curve showed that there is definitely an association between both diagnostic indicators and CD, the association is not a particularly strong one due to great heterogeneity between studies. Whilst an association between IDA/A & ID and coeliac disease was evident, the results were not deemed significant enough to prompt coeliac disease testing in those with IDA/A & ID.

Keywords: anemia, iron deficiency anemia, coeliac disease, point of care testing

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Authors: Rama Bhargava

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In the current paper, numerical simulation has been performed for the two-dimensional time dependent Pennes’ heat transfer model which is solved for irregular diseased tumor cells. An elliptic cryoprobe of varying sizes is taken at the center of the computational domain in such a manner that the location of the probe is fixed throughout the computation. The phase transition occurs due to the effect of probe with infusion of different nanoparticles Au, Al₂O₃, Fe₃O₄. The cooling performance of these nanoparticles injected at very low temperature, has been studied by implementing a hybrid FEM/EFGM method in which the whole domain is decomposed into two subdomains. The results are shown in terms of temperature profile inside the computational domain. Rate of cooling is obtained for various nanoparticles and it is observed that infusion of Au nanoparticles is very much efficient in increasing the heating rate than other nanoparticles. Such numerical scheme has direct applications where the domain is irregular.

Keywords: cryosurgery, hybrid EFGM/FEM, nanoparticles, simulation

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Authors: Wangxu Xia

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BACKGROUND BI-RADS 4 lesions raise the possibility of malignancy that warrant further clinical and radiologic work-up. This study aimed to evaluate the predictive performance of diffusion-weighted imaging(DWI) and microcalcifications on mammography for predicting malignancy of BI-RADS 4 lesions. In addition, the predictive performance of DWI combined with microcalcifications was alsocompared with the Kaiser score. METHODS During January 2021 and June 2023, 144 patients with 178 BI-RADS 4 lesions underwent conventional MRI, DWI, and mammography were included. The lesions were dichotomized intobenign or malignant according to the pathological results from core needle biopsy or surgical mastectomy. DWI was performed with a b value of 0 and 800s/mm2 and analyzed using theapparent diffusion coefficient, and a Kaiser score > 4 was considered to suggest malignancy. Thediagnostic performances for various diagnostic tests were evaluated with the receiver-operatingcharacteristic (ROC) curve. RESULTS The area under the curve (AUC) for DWI was significantly higher than that of the of mammography (0.86 vs 0.71, P<0.001), but was comparable with that of the Kaiser score (0.86 vs 0.84, P=0.58). However, the AUC for DWI combined with mammography was significantly highthan that of the Kaiser score (0.93 vs 0.84, P=0.007). The sensitivity for discriminating malignant from benign BI-RADS 4 lesions was highest at 89% for Kaiser score, but the highest specificity of 83% can be achieved with DWI combined with mammography. CONCLUSION DWI combined with microcalcifications on mammography could discriminate malignant BI-RADS4 lesions from benign ones with a high AUC and specificity. However, Kaiser score had a better sensitivity for discrimination.

Keywords: MRI, DWI, mammography, breast disease

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Authors: Hafiz Gulfam Ahmad, Chuangdong Li, Zeeshan Ahmad

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In this paper, we proposed a parallel IDS and honeypot based approach to detect and analyze the unknown and known attack taxonomy for improving the IDS performance and protecting the network from intruders. The main theme of our approach is to record and analyze the intruder activities by using both the low and high interaction honeypots. Our architecture aims to achieve the required goals by combing signature based IDS, honeypots and generate the new signatures. The paper describes the basic component, design and implementation of this approach and also demonstrates the effectiveness of this approach reducing the probability of network attacks.

Keywords: network security, intrusion detection, honeypot, snort, nmap

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Authors: E. Vlachopapadopoulou, E. Dikaiakou, E. Anagnostou, I. Panagiotopoulos, E. Kaloumenou, M. Kafetzi, A. Fotinou, S. Michalacos

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Aim: To investigate the relation and impact of Body Mass Index (BMI), Waist Circumference (WC) and Body Fat Weight (BFW) on insulin resistance (MATSUDA INDEX < 2.5) in children and adolescents. Methods: Data from 95 overweight and obese children (47 boys and 48 girls) with mean age 10.7 ± 2.2 years were analyzed. ROC analysis was used to investigate the predictive ability of BMI, WC and BFW for insulin resistance and find the optimal cut-offs. The overall performance of the ROC analysis was quantified by computing area under the curve (AUC). Results: ROC curve analysis indicated that the optimal-cut off of WC for the prediction of insulin resistance was 97 cm with sensitivity equal to 75% and specificity equal to 73.1%. AUC was 0.78 (95% CI: 0.63-0.92, p=0.001). The sensitivity and specificity of obesity for the discrimination of participants with insulin resistance from those without insulin resistance were equal to 58.3% and 75%, respectively (AUC=0.67). BFW had a borderline predictive ability for insulin resistance (AUC=0.58, 95% CI: 0.43-0.74, p=0.101). The predictive ability of WC was equivalent with the correspondence predictive ability of BMI (p=0.891). Obese subjects had 4.2 times greater odds for having insulin resistance (95% CI: 1.71-10.30, p < 0.001), while subjects with WC more than 97 had 8.1 times greater odds for having insulin resistance (95% CI: 2.14-30.86, p=0.002). Conclusion: BMI and WC are important clinical factors that have significant clinical relation with insulin resistance in children and adolescents. The cut off of 97 cm for WC can identify children with greater likelihood for insulin resistance.

Keywords: body fat weight, body mass index, insulin resistance, obese children, waist circumference

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Authors: Angad S. Kushwaha, Rajeev Kumar, Monika Srivastava, S. K. Srivastava

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A lot of research work is going on in the field of graphene based SPR biosensor. In the conventional SPR based biosensor, graphene is used as a biomolecular recognition element. Graphene adsorbs biomolecules due to carbon based ring structure through sp2 hybridization. The proposed SPR based biosensor configuration will open a new avenue for efficient biosensing by taking the advantage of Graphene and its fascinating nanofabrication properties. In the present study, we have studied an SPR biosensor based on graphene mediated by Zinc Oxide (ZnO) and Gold. In the proposed structure, prism (BK7) base is coated with Zinc Oxide followed by Gold and Graphene. Using the waveguide approach by transfer matrix method, the proposed structure has been investigated theoretically. We have analyzed the reflectance versus incidence angle curve using He-Ne laser of wavelength 632.8 nm. Angle, at which the reflectance is minimized, termed as SPR angle. The shift in SPR angle is responsible for biosensing. From the analysis of reflectivity curve, we have found that there is a shift in SPR angle as the biomolecules get attached on the graphene surface. This graphene layer also enhances the sensitivity of the SPR sensor as compare to the conventional sensor. The sensitivity also increases by increasing the no of graphene layer. So in our proposed biosensor we have found minimum possible reflectivity with optimum level of sensitivity.

Keywords: biosensor, sensitivity, surface plasmon resonance, transfer matrix method

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Authors: Alexandre Daliberto Frugoli, Pedro Jose Gabriel Ferreira, Pedro Americo Frugoli, Lucio Leonardo, Thais Cavalheri Santos

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Laboratory classes in Engineering courses are essential for students to be able to integrate theory with practical reality, by handling equipment and observing experiments. In the researches of physical phenomena, students can learn about the complexities of science. Over the past years, universities in developing countries have been reducing the course load of engineering courses, in accordance with cutting cost agendas. Quality education is the object of study for researchers and requires educators and educational administrators able to demonstrate that the institutions are able to provide great learning opportunities at reasonable costs. Didactic test benches are indispensable equipment in educational activities related to turbo hydraulic pumps and pumping facilities study, which have a high cost and require long class time due to measurements and equipment adjustment time. In order to overcome the aforementioned obstacles, aligned with the professional objectives of an engineer, GruPEFE - UNIP (Research Group in Physics Education for Engineering - Universidade Paulista) has developed a multi-purpose stand for the discipline of fluid mechanics which allows the study of velocity and flow meters, loads losses and pump association. In this work, results obtained by the association in series and in parallel of hydraulic pumps will be presented and discussed, mainly analyzing the repeatability of experimental procedures and their agreement with the theory. For the association in series two identical pumps were used, consisting of the connection of the discharge of a pump to the suction of the next one, allowing the fluid to receive the power of all machines in the association. The characteristic curve of the set is obtained from the curves of each of the pumps, by adding the heads corresponding to the same flow rates. The same pumps were associated in parallel. In this association, the discharge piping is common to the two machines together. The characteristic curve of the set was obtained by adding to each value of H (head height), the flow rates of each pump. For the tests, the input and output pressure of each pump were measured. For each set there were three sets of measurements, varying the flow rate in range from 6.0 to 8.5 m 3 / h. For the two associations, the results showed an excellent repeatability with variations of less than 10% between sets of measurements and also a good agreement with the theory. This variation agrees with the instrumental uncertainty. Thus, the results validate the use of the fluids bench designed for didactic purposes. As a future work, a digital acquisition system is being developed, using differential sensors of extremely low pressures (2 to 2000 Pa approximately) for the microcontroller Arduino.

Keywords: engineering education, fluid mechanics, hydrostatic pumps association, multi-purpose stand

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Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour

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This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.

Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks

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Authors: Arjun Prakash, Samanvitha H.

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BACKGROUND: Breast cancer is the commonest malignancy among women globally, with an estimated incidence of 2.3 million new cases as of 2020, representing 11.7% of all malignancies. As per Globocan data 2020, it accounted for 13.5% of all cancers and 10.6% of all cancer deaths in India. Early diagnosis and treatment can improve the overall morbidity and mortality, which necessitates the importance of differentiating benign from malignant breast masses. OBJECTIVE: The objective of the present study was to evaluate and compare the role of Digital Mammography (DM), B mode Ultrasound (USG), Shear Wave Elastography (SWE) and Strain Elastography (SE) in differentiating benign and malignant breast masses (ACR BI-RADS 3 - 5). Histo-Pathological Examination (HPE) was considered the Gold standard. MATERIALS & METHODS: We conducted a cross-sectional study on 53 patients with 64 breast masses over a period of 10 months. All patients underwent DM, USG, SWE and SE. These modalities were individually assessed to know their accuracy in differentiating benign and malignant masses. All Digital Mammograms were done using the Fujifilm AMULET Innovality Digital Mammography system and all Ultrasound examinations were performed on SAMSUNG RS 80 EVO Ultrasound system equipped with 2 to 9 MHz and 3 – 16 MHz linear transducers. All masses were subjected to HPE. Independent t-test and Chi-square or Fisher’s exact test were used to assess continuous and categorical variables, respectively. ROC analysis was done to assess the accuracy of diagnostic tests. RESULTS: Of 64 lesions, 51 (79.68%) were malignant and 13 (20.31%) (p < 0.0001) were benign. SE was the most specific (100%) (p < 0.0001) and USG (98%) (p < 0.0001) was the most sensitive of all the modalities. E max, E mean, E max ratio, E mean ratio and Strain Ratio of the malignant masses significantly differed from those of the benign masses. Maximum SWE value showed the highest sensitivity (88.2%) (p < 0.0001) among the elastography parameters. A combination of USG, SE and SWE had good sensitivity (86%) (p < 0.0001). CONCLUSION: A combination of USG, SE and SWE improves overall diagnostic yield in differentiating benign and malignant breast masses. Early diagnosis and treatment of breast carcinoma will reduce patient mortality and morbidity.

Keywords: digital mammography, breast cancer, ultrasound, elastography

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Authors: Odile Amoncou, Djedje-Kossu Zahui

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The increased demand for digital communications during the COVID-19 pandemic has seen an unprecedented surge in new telecom infrastructure around the world. The expansion has been more remarkable in countries with developing telecom infrastructures. Particularly, the three telecom operators in Cote d’Ivoire, Orange CI, MTN CI, and MOOV Africa, have considerably scaled up their exploitation technologies and capacities in terms of towers, fiber optic installation, and customer service hubs. The trend will likely continue upward while expanding the carbon footprint of the Ivorian telecom operators. Therefore, the corporate social and environmental responsibilities of these telecommunication companies can no longer be overlooked. This paper assesses the sustainability of the three Ivorian telecommunication network operators by applying a combination of commonly used sustainability management indexes. These tools are streamlined and adapted to the relatively young and developing digital network of Cote D’Ivoire. We trust that this article will push the respective CEOs to make sustainability a top strategic priority and understand the substantial potential returns in terms of saving, new products, and new clients while improving their corporate image. In addition, good sustainability management can increase their stakeholders.

Keywords: sustainability of telecom operators, sustainability management index, carbon footprint, digital communications

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Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Vivek Verma, Sanjeev Kumar

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This study presents a novel quantum image encryption algorithm, using a 3D chaotic map and controlled qubit-level scrambling operations. The newly proposed 3D-BNM chaotic map effectively reduces the degradation of chaotic dynamics resulting from the finite word length effect. It facilitates the generation of highly unpredictable random sequences and enhances chaotic performance. The system’s efficacy is additionally enhanced by the inclusion of a SHA-256 hash function. Initially, classical plain images are converted into their quantum equivalents using the Novel Enhanced Quantum Representation (NEQR) model. The Generalized Quantum Arnold Transformation (GQAT) is then applied to disrupt the coordinate information of the quantum image. Subsequently, to diffuse the pixel values of the scrambled image, XOR operations are performed using pseudorandom sequences generated by the 3D-BNM chaotic map. Furthermore, to enhance the randomness and reduce the correlation among the pixels in the resulting cipher image, a controlled qubit-level scrambling operation is employed. The encryption process utilizes fundamental quantum gates such as C-NOT and CCNOT. Both theoretical and numerical simulations validate the effectiveness of the proposed algorithm against various statistical and differential attacks. Moreover, the proposed encryption algorithm operates with low computational complexity.

Keywords: 3D Chaotic map, SHA-256, quantum image encryption, Qubit level scrambling, NEQR

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Authors: Youcef Faci, Djillali Allou, Ahmed Mebtouche, Badredine Maalem

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The inclination of the bolt in a fastened joint of composite material during a tensile test can be influenced by several parameters, including material properties, bolt diameter and length, the type of composite material being used, the size and dimensions of the bolt, bolt preload, surface preparation, the design and configuration of the joint, and finally testing conditions. These parameters should be carefully considered and controlled to ensure accurate and reliable results during tensile testing of composite materials with fastened joints. Our work focuses on the effect of the stacking sequence and the geometry of specimens. An experimental test is carried out to obtain the inclination of a bolt during a tensile test of a composite material using acoustic emission and digital image correlation. Several types of damage were obtained during load. Digital image correlation techniques permit to obtain the inclination of bolt angle value during tensile test. We concluded that the inclination of the bolt during a tensile test of a composite material can be related to the damage that occurs in the material. It can cause stress concentrations and localized deformation in the material, leading to damage such as delamination, fiber breakage, matrix cracking, and other forms of failure.

Keywords: damage, digital image correlation, bolt inclination angle, joint

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Authors: Ibtissem Talbi

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During the last decade, a variety of chaos-based cryptosystems have been investigated. Most of them are based on the structure of Fridrich, which is based on the traditional confusion-diffusion architecture proposed by Shannon. Compared with traditional cryptosystems (DES, 3DES, AES, etc.), the chaos-based cryptosystems are more flexible, more modular and easier to be implemented, which make them suitable for large scale-data encyption, such as images and videos. The heart of any chaos-based cryptosystem is the chaotic generator and so, a part of the efficiency (robustness, speed) of the system depends greatly on it. In this talk, we give an overview of the state of the art of chaos-based block ciphers and we describe some of our schemes already proposed. Also we will focus on the essential characteristics of the digital chaotic generator, The needed performance of a chaos-based block cipher in terms of security level and speed of calculus depends on the considered application. There is a compromise between the security and the speed of the calculation. The security of these block block ciphers will be analyzed.

Keywords: chaos-based cryptosystems, chaotic generator, security analysis, structure of Fridrich

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Authors: Amit Kumar Happy

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This paper is motivated by the importance of multi-sensor image fusion with a specific focus on infrared (IR) and visual image (VI) fusion for various applications, including military reconnaissance. Image fusion can be defined as the process of combining two or more source images into a single composite image with extended information content that improves visual perception or feature extraction. These images can be from different modalities like visible camera & IR thermal imager. While visible images are captured by reflected radiations in the visible spectrum, the thermal images are formed from thermal radiation (infrared) that may be reflected or self-emitted. A digital color camera captures the visible source image, and a thermal infrared camera acquires the thermal source image. In this paper, some image fusion algorithms based upon multi-scale transform (MST) and region-based selection rule with consistency verification have been proposed and presented. This research includes the implementation of the proposed image fusion algorithm in MATLAB along with a comparative analysis to decide the optimum number of levels for MST and the coefficient fusion rule. The results are presented, and several commonly used evaluation metrics are used to assess the suggested method's validity. Experiments show that the proposed approach is capable of producing good fusion results. While deploying our image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also make it hard to become deployed in systems and applications that require a real-time operation, high flexibility, and low computation ability. So, the methods presented in this paper offer good results with minimum time complexity.

Keywords: image fusion, IR thermal imager, multi-sensor, multi-scale transform

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Authors: Ehsan Motamedian

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Finding all optimal flux distributions of a metabolic model is an important challenge in systems biology. In this paper, a new algorithm is introduced to identify all alternate optimal solutions of a large scale metabolic network. The algorithm reduces the model to decrease computations for finding optimal solutions. The algorithm was implemented on the Escherichia coli metabolic model to find all optimal solutions for lactate and acetate production. There were more optimal flux distributions when acetate production was optimized. The model was reduced from 1076 to 80 variable fluxes for lactate while it was reduced to 91 variable fluxes for acetate. These 11 more variable fluxes resulted in about three times more optimal flux distributions. Variable fluxes were from 12 various metabolic pathways and most of them belonged to nucleotide salvage and extra cellular transport pathways.

Keywords: flux variability, metabolic network, mixed-integer linear programming, multiple optimal solutions

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Authors: Paula Andrea Segura Delgado, Martha Helena Ramírez-Bahena

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Research on how the brain learns has a transcendental application in the educational context. It is crucial for teacher training to understand the nature of brain changes and their direct influence on learning processes. This communication is based on a literature review focused on neuroscience, neuroeducation, and the impact of digital technology on the human brain. Information was gathered from both English and Spanish language sources, using online journals, books and reports. The general objective was to analyze the role of neuroscience knowledge in enriching our understanding of the learning process. In fact, the authors have focused on the impact of digital technology on the human brain as well as its influence in the field of education..Neuroscience knowledge can contribute significantly to improving the training of educators and therefore educational practices. Education as an instrument of change and school as an agent of socialization, it is necessary to understand what it aims to transform: the human brain. Understanding the functioning of the human brain has important repercussions on education: this elucidates cognitive skills, psychological processes and elements that influence the learning process (memory, executive functions, emotions and the circadian cycle); helps identify psychological and neurological deficits that can impede learning processes (dyslexia, autism, hyperactivity); It allows creating environments that promote brain development and contribute to the advancement of brain capabilities in alignment with the stages of neurobiological development. The digital age presents diverse opportunities to every social environment. The frequent use of digital technology (DT) has had a significant and abrupt impact on both the cognitive abilities and physico-chemical properties of the brain, significantly influencing educational processes. Hence, educational community, with the insights from advances in neuroscience, aspire to identify the positive and negative effects of digital technology on the human brain. This knowledge helps ensure the alignment of teacher training and practices with these findings. The knowledge of neuroscience enables teachers to develop teaching methods that are aligned with the way the brain works. For example, neuroscience research has shown that digital technology is having a significant impact on the human brain (addition, anxiety, high levels of dopamine, circadian cycle disorder, decrease in attention, memory, concentration, problems with their social relationships). Therefore, it is important to understand the nature of these changes, their impact on the learning process, and how educators should effectively adapt their approaches based on these brain's changes.

Keywords: digital technology, learn process, neuroscience knowledge, neuroeducation, training proffesors

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Authors: Qinglin Liu, Zhiyong Lin, Zongheng Wei, Jianfeng Wen, Congming Yi, Hai Liu

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The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users in the network can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. The QCMS-CH algorithm can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between the users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix "R00". Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel, hoping to attempt rendezvous with other users. We prove the correctness of QCMS-CH and derive an upper bound on its Maximum Time-to-Rendezvous (MTTR). Simulation results show that the QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-to-Rendezvous (ETTR).

Keywords: channel hopping, blind rendezvous, cognitive radio networks, quaternary-quinary coding

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Authors: Yuan-Hsiang Chang, Zhong-Xian Peng, Li-Der Jeng

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Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: connected component labeling, image processing, morphological processing, optical musical recognition

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Authors: Maria Cecilia G. Cantos, Lorena W. Rabago, Bartolome T. Tanguilig III

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This paper describes a conducive and structured information exchange environment for the students of the College of Computer Studies in Manuel S. Enverga University Foundation in. The system was developed to help the students to check their academic result, manage profile, make self-enlistment and assist the students to manage their academic status that can be viewed also in mobile phones. Developing class schedules in a traditional way is a long process that involves making many numbers of choices. With Hill Climbing Algorithm, however, the process of class scheduling, particularly with regards to courses to be taken by the student aligned with the curriculum, can perform these processes and end up with an optimum solution. The proponent used Rapid Application Development (RAD) for the system development method. The proponent also used the PHP as the programming language and MySQL as the database.

Keywords: hill climbing algorithm, integrated system, mobile web-based, student information system

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Rafat Rob, Khaled Alotaibi, Dana Nour, Abdullah Albadrani, Abdulmohsen Mulhim

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Power systems digitization solutions provides a comprehensive smart, cohesive, interconnected network, extensive connectivity between digital assets, physical power plants, and resources to form digital economies. However, digitization has exposed the classical air gapped power plants to the rapid spread of cyber threats and attacks in the process delaying and forcing many organizations to rethink their cyber security policies and standards before they can augment their operation the new advanced digital devices. Cyber Security requirements for power systems (and industry control systems therein) demand a new approach, unique methodology, and design process that is completely different to Cyber Security measures designed for the IT systems. In practice, Cyber Security strategy, as applied to power systems, tends to be closely aligned to those measures applied for IT system purposes. The differentiator for Cyber Security in terms of power systems are the physical assets and applications used, alongside the ever-growing rate of expansion within the industry controls sector (in comparison to the relatively saturated growth observed for corporate IT systems). These factors increase the magnitude of the cyber security risk within such systems. The introduction of smart devices and sensors along the grid initiate vulnerable entry points to the systems. Every installed Smart Meter is a target; the way these devices communicate with each other may instigate a Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack. Attacking one sensor or meter has the potential to propagate itself throughout the power grid reaching the IT network, where it may manifest itself as a malware infiltration.

Keywords: supply chain, cybersecurity, maturity model, risk, smart grid

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Authors: Yul Y. Nazaruddin, Anas Y. Widiaribowo, Satriyo Nugroho

Abstract:

Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process.

Keywords: ANFIS, boiler, combustion process, genetic algorithm, optimization.

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Authors: Ismail Cengiz, Faruk Elaldi

Abstract:

Within the scope of the study presented in this paper, load carrying capacity and buckling behavior of a stiffened aluminum panel designed by adopting current ‘buckle-resistant’ design application and ‘Post –Buckling’ design approach were investigated experimentally and numerically. The test specimen that is stabilized by Z-type stiffeners and manufactured from aluminum 2024 T3 Clad material was test under compression load. Buckling behavior was observed by means of 3 – dimensional digital image correlation (DIC) and strain gauge pairs. The experimental study was followed by developing an efficient and reliable finite element model whose ability to predict behavior of the stiffened panel used for compression test is verified by compering experimental and numerical results in terms of load – shortening curve, strain-load curves and buckling mode shapes. While finite element model was being constructed, non-linear behaviors associated with material and geometry was considered. Finally, applicability of aluminum stiffened panel in airframe design against to composite structures was evaluated thorough the concept of ‘Structural Efficiency’. This study reveals that considerable amount of weight saving could be gained if the concept of ‘post-buckling design’ is preferred to the already conventionally used ‘buckle resistant design’ concept in aircraft industry without scarifying any of structural integrity under load spectrum.

Keywords: post-buckling, stiffened panel, non-linear finite element method, aluminum, structural efficiency

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Authors: Samia Kouki

Abstract:

Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limit us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce significantly the computational burden of sequential versions of Branch-and-Bound is to design parallel versions of these algorithms which employ several processors. This paper describes a parallel Branch-and-Bound algorithm called GALB for solving the classical permutation flowshop scheduling problem as well as its implementation on a Grid computing infrastructure. The experimental study of our distributed parallel algorithm gives promising results and shows clearly the benefit of the parallel paradigm to solve large-scale instances in moderate CPU time.

Keywords: grid computing, permutation flow shop problem, branch and bound, load balancing

Procedia PDF Downloads 283

Authors: Philip Katz

Abstract:

The research examines preferred reading of contemporary ads aimed at Generation Z through digital media. A qualitative analysis of focus groups consisting of members of Generation Z from 13 countries in Europe, the Middle East, South America and Asia has shown that, among this cohort, the influence of national culture does not create a strong impediment to understanding media communications targeting Generation Z. The familiarity of members of Generation Z with other countries’ popular culture through the spread of digital media has allowed a homogenizing effect and allowed a greater understanding of those cultures among this generation that lessens the impact of geographic separation.

Keywords: audience, Generation Z, marketing communication, preferred reading

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Authors: Zhu-Qing Jia, Tao Lin, Tong Zhou

Abstract:

The recognition method of Optical Character Recognition has been expensively utilized, while it is rare to be employed specifically in recognition of electronic components. This paper suggests a high-effective algorithm on appearance identification of integrated circuit components based on the existing methods of character recognition, and analyze the pros and cons.

Keywords: optical character recognition, fuzzy page identification, mutual correlation matrix, confidence self-adaptation

Procedia PDF Downloads 540

Authors: Ward Bakker, Shadi Alhakimi

Abstract:

The amount of people moving towards more privacy focused instant messaging applications has grown significantly. Signal is one of these instant messaging applications, which makes Signal interesting for digital investigators. In this research, we evaluate the artifacts that are generated by the Signal messenger for Android. This evaluation was done by using the features that Signal provides to create artifacts, whereafter, we made an image of the internal storage and the process memory. This image was analysed manually. The manual analysis revealed the content that Signal stores in different locations during its operation. From our research, we were able to identify the artifacts and interpret how they were used. We also examined the source code of Signal. Using our obtain knowledge from the source code, we developed a tool that decrypts some of the artifacts using the key stored in the Android Keystore. In general, we found that most artifacts are encrypted and encoded, even after decrypting some of the artifacts. During data visualization, some artifacts were found, such as that Signal does not use relationships between the data. In this research, two interesting groups of artifacts were identified, those related to the database and those stored in the process memory dump. In the database, we found plaintext private- and group chats, and in the memory dump, we were able to retrieve the plaintext access code to the application. Nevertheless, we conclude that Signal contains a wealth of artifacts that could be very valuable to a digital forensic investigation.

Keywords: forensic, signal, Android, digital

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Authors: C. Temaneh-Nyah, F. A. Phiri, D. Karegeya

Abstract:

This paper presents an algorithm for obtaining the map of subscriber’s density distribution for a mobile wireless communication network based on the actual subscriber's traffic data obtained from the base station. This is useful in statistical characterization of the mobile wireless network.

Keywords: electromagnetic compatibility, statistical analysis, simulation of communication network, subscriber density

Procedia PDF Downloads 309