Search results for: data throughput

Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: John Q. Todd

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Given that modern equipment can provide comprehensive health, status, and error condition data via built-in sensors, maintenance organizations have a new and valuable source of insight to take advantage of. This presentation will expose what these data payloads might look like and how they can be filtered, visualized, calculated into metrics, used for machine learning, and generate alerts for further action.

Keywords: condition based maintenance, equipment data, metrics, alerts

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Authors: Christy Rosaline, Rathankar Roa, Waheeta Hopper

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Persistence of tuberculosis, emergence of multidrug-resistance and extensively drug-resistant forms of the disease, has increased the interest in developing new antitubercular drugs. Developing inhibitors for 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase from Mycobacterium tuberculosis (MtbDAH7Ps), an enzyme involved in shikimate pathway, gives a selective target for antitubercular agents. MtbDAH7Ps was screened against ZINC database, and shortlisted compounds were subjected to induce fit docking. Prime/Molecular Mechanics Generalized Born Surface Area calculation was used to validate the binding energy of ligand-protein complex. Molecular Dynamics analysis for of the lead compounds–MtbDAH7Ps complexes showed that the backbone of MtbDAH7Ps in their complexes were stable. These results suggest that the shortlisted lead compounds ZINC04097114, ZINC15163225, ZINC16857013, ZINC06275603, and ZINC05331260 could be developed into novel drug leads to inhibit DAH7Ps in Mycobacterium tuberculosis.

Keywords: MtbDAH7Ps, Mycobacterium tuberculosis, HTVS, molecular dynamics

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Authors: Dragana Calic

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The openness, availability and the sheer volume of big data have provided, what some regard as, an invaluable and rich dataset. Researchers, businesses, advertising agencies, medical institutions, to name only a few, collect, share, and analyze this data to enable their processes and decision making. However, there are important ethical considerations associated with the use of big data. The rapidly evolving nature of online technologies has overtaken the many legislative, privacy, and ethical frameworks and principles that exist. For example, should we obtain consent to use people’s online data, and under what circumstances can privacy considerations be overridden? Current guidance on how to appropriately and ethically handle big data is inconsistent. Consequently, this paper focuses on two quite distinct but related ethical considerations that are at the core of the use of big data for research purposes. They include empowering the producers of data and empowering researchers who want to study big data. The first consideration focuses on informed consent which is at the core of empowering producers of data. In this paper, we discuss some of the complexities associated with informed consent and consider studies of producers’ perceptions to inform research ethics guidelines and practice. The second consideration focuses on the researcher. Similarly, we explore studies that focus on researchers’ perceptions and experiences.

Keywords: big data, ethics, producers’ perceptions, researchers’ perceptions

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Ankur Mundhra, Shubhadeep Chakraborty, Y. R. Singh, Vishal Das

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Seismic data scaling affects the dynamic range of a data and with present day lower costs of storage and higher reliability of Hard Disk data, scaling is not suggested. However, in dealing with data of different vintages, which perhaps were processed in 16 bits or even 8 bits and are need to be processed with 32 bit available data, scaling is performed. Also, scaling amplifies low amplitude events in deeper region which disappear due to high amplitude shallow events that saturate amplitude scale. We have focused on significance of scaling data to aid interpretation. This study elucidates a proper seismic loading procedure in workstations without using default preset parameters as available in most software suites. Differences and distribution of amplitude values at different depth for seismic data are probed in this exercise. Proper loading parameters are identified and associated steps are explained that needs to be taken care of while loading data. Finally, the exercise interprets the un-certainties which might arise when correlating scaled and unscaled versions of seismic data with synthetics. As, seismic well tie correlates the seismic reflection events with well markers, for our study it is used to identify regions which are enhanced and/or affected by scaling parameter(s).

Keywords: clipping, compression, resolution, seismic scaling

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Authors: Rejab Hajlaoui, Tarek Moulahi, Hervé Guyennet

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In a highway scenario, the vehicle speed can exceed 120 kmph. Therefore, any vehicle can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To ensure an effective stability in vehicular ad hoc networks with minimum broadcasting storm, we have developed a weighted algorithm based on the k-medoids clustering algorithm (WKCA). Indeed, the number of clusters and the initial cluster heads will not be selected randomly as usual, but considering the available transmission range and the environment size. Then, to ensure optimal assignment of nodes to clusters in both k-medoids phases, the combined weight of any node will be computed according to additional metrics including direction, relative speed and proximity. Empirical results prove that in addition to the convergence speed that characterizes the k-medoids algorithm, our proposed model performs well both AODV-Clustering and OLSR-Clustering protocols under different densities and velocities in term of end-to-end delay, packet delivery ratio, and throughput.

Keywords: communication, clustering algorithm, k-medoids, sensor, vehicular ad hoc network

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data mining

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

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Authors: Dharamvir Mangal

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Supply chain management has increasingly become an inevitable challenge to most companies to continuously survive and prosper in the global chain-based competitive environment. The current challenges of the Indian automotive world, their implications on supply chain are summarized and analyzed in this paper. In this competitive era of ‘LPG’ i.e. Liberalization, Privatization and Globalization, modern marketing systems, introduction of products with short life cycles, and the discriminating expectations of customers have enforced business enterprises to invest in and focus attention on their Supply Chains (SCs) in order to meet out the level of customer’s satisfaction and to survive in the competitive market. In fact, many of trends in the auto industry are reinforcing the need to redefine supply chain strategies layouts, and operations etc. Many manufacturing operations are designed to maximize throughput and lower costs with modest considerations for the crash on inventory levels and distribution capabilities. To improve profitability and efficiency, automotive players are seeking ways to achieve operational excellence, reduce operating cost and enhance customer service through efficient supply chain management.

Keywords: automotive industry, supply chain, challenges, market potential

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Authors: Sakeh N. Mohd, Bahari M. N. Abdul, Abdullah S. N. Akmar

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Oil palm production generates high export earnings to many countries especially in Southeast Asian region. Infection by necrotrophic fungus, Ganoderma boninense on oil palm results in basal stem rot which compromises oil palm production leading to significant economic loss. There are no reliable disease treatments nor promising resistant oil palm variety has been cultivated to eradicate the disease up to date. Thus, understanding molecular mechanisms underlying early interactions of oil palm with Ganoderma boninense may be vital to promote preventive or control measure of the disease. In the present study, four months old oil palm seedlings were infected via artificial inoculation of Ganoderma boninense on rubber wood blocks. Roots of six biological replicates of treated and untreated oil palm seedlings were harvested at 0, 3, 7 and 11 days post inoculation. Next-generation sequencing was performed to generate high-throughput RNA-Seq data and identify differentially expressed genes (DEGs) during early oil palm-Ganoderma boninense interaction. Based on de novo transcriptome assembly, a total of 427,122,605 paired-end clean reads were assembled into 30,654 unigenes. DEGs analysis revealed upregulation of 173 transcription factors on Ganoderma boninense-treated oil palm seedlings. Sixty-one transcription factors were categorized as DEGs according to stringent cut-off values of genes with log2 ratio [Number of treated oil palm seedlings/ Number of untreated oil palm seedlings] ≥ |1.0| (corresponding to 2-fold or more upregulation) and P-value ≤ 0.01. Transcription factors in response to biotic stress will be screened out from abiotic stress using reverse transcriptase polymerase chain reaction. Transcription factors unique to biotic stress will be verified using real-time polymerase chain reaction. The findings will help researchers to pinpoint defense response mechanism specific against Ganoderma boninense.

Keywords: Ganoderma boninense, necrotrophic, next-generation sequencing, transcription factors

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Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Nidhi Gopal

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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

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Authors: Rozo D. Juan Felipe, Ramírez L. Leonardo Juan, Puerta A. Gabriel Alberto

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The telemedicine services require correct computing resource management to guarantee productivity and efficiency for medical and non-medical staff. The aim of this study was to examine web management strategies to ensure the availability of resources and services in telemedicine so as to provide medical information management with an accessible strategy. In addition, to evaluate the quality-of-service parameters, the followings were measured: delays, throughput, jitter, latency, available bandwidth, percent of access and denial of services based of web management performance map with profiles permissions and database management. Through 24 different test scenarios, the results show 100% in availability of medical information, in relation to access of medical staff to web services, and quality of service (QoS) of 99% because of network delay and performance of computer network. The findings of this study suggest that the proposed strategy of web management is an ideal solution to guarantee the availability, reliability, and accessibility of medical information. Finally, this strategy offers seven user profile used at telemedicine center of Bogota-Colombia keeping QoS parameters suitable to telemedicine services.

Keywords: availability, medical information, QoS, strategy, telemedicine

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Authors: M. Abd elfattah, M. Ossman, Nahla A. Taha

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The present work explored the use of Egyptian rice straw, an agricultural waste that leads to global warming problem through brown cloud, as a potential feedstock for the preparation of activated carbon by physical and chemical activation. The results of this study showed that it is feasible to prepare activated carbons with relatively high surface areas and pore volumes from the Egyptian rice straw by direct chemical and physical activation. The produced activated carbon from the two methods (AC1 and AC2) could be used as potential adsorbent for the removal of Fe(III) from aqueous solution contains heavy metals and polluted water. The adsorption of Fe(III) was depended on the pH of the solution. The optimal Fe(III) removal efficiency occurs at pH 5. Based on the results, the optimum contact time is 60 minutes and adsorbent dosage is 3 g/L. The adsorption breakthrough curves obtained at different bed depths indicated increase of breakthrough time with increase in bed depths. A rise in inlet Fe(III) concentration reduces the throughput volume before the packed bed gets saturated. AC1 showed higher affinity for Fe(III) as compared to Raw rice husk.

Keywords: rice straw, activated carbon, Fe(III), fixed bed column, pyrolysis

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

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Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Jayanthi Sivaraman

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Claudins are the important components of the tight junctions that play a key role in paracellular permeability. Among various members of Claudin family, Claudin 4 (CLDN4) is found to be overexpressed in ovarian, pancreatic carcinomas and other epithelial malignancies. Therefore, in this study, an attempt has been made to identify potent inhibitors for CLDN4 from the ZINC database using virtual screening, molecular docking and molecular dynamics simulations. A well refined molecular model of CLDN4 was built using Prime of Schrodinger v10.2(Template- PDB ID: 4P79). Approximately, 6 million compounds from ZINC database are subjected to high-throughput virtual screening (HTVS) against the active site of CLDN4. Molecular docking using GLIDE predicted ARG31, ASN142, ASP146 and ARG158 as critically important residues. Furthermore, three compounds from ZINC database (ZINC96331839, ZINC36533519 and ZINC75819394) showed highly promising ADME properties and binding affinity with stable conformation. The therapeutic efficiency of these lead compounds is evaluated and confirmed by in-vitro and in-vivo studies which leads to the development of novel anti-cancer drugs.

Keywords: ADME property, inhibitors, molecular docking, virtual screening

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Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

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Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

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Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

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Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

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Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast

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Authors: Do-Won Kim, Andy Eu Jin Lim, Raja Muthusamy Kumarasamy, Vishal Vinayak, Jacky Wang Yu-Shun, Jason Liow Tsung Yang, Patrick Lo Guo Qiang

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In this study, we demonstrate 4-channel coarse wavelength division multiplexing (CWDM) optical transceiver based on silicon photonics integrated circuits (PIC) of waveguide Ge-photodiode (Ge-PD) and Mach Zehnder (MZ)-modulator. 4-channel arrayed PICs of Ge-PD and MZ-modulator are verified to operate at 25 Gbps/ch achieving 4x25 Gbps of total data rate. 4 bare dies of single-channel commercial electronics ICs (EICs) of trans-impedance amplifier (TIA) for Ge-PD and driver IC for MZ-modulator are packaged with PIC on printed circuit board (PCB) in a chip-on-board (COB) manner. Each single-channel EIC is electrically connected to the one channel of 4-channel PICs by wire bonds to trace. The PICs have 4-channel multiplexer for MZ-modulator and 4-channel demultiplexer for Ge-PD. The 4-channel multiplexer/demultiplexer have echelle gratings for4 CWDM optic signals of which center wavelengths are 1511, 1531, 1553, and 1573 nm. Its insertion loss is around 4dB with over 15dB of extinction ratio.The dimension of 4-channel Ge-PD is 3.6x1.4x0.3mm, and its responsivity is 1A/W with dark current of less than 20 nA.Its measured 3dB bandwidth is around 20GHz. The dimension of the 4-channel MZ-modulator is 3.6x4.8x0.3mm, and its 3dB bandwidth is around 11Ghz at -2V of reverse biasing voltage. It has 2.4V•cmbyVπVL of 6V for π shift to 4 mm length modulator.5x5um of Inversed tapered mode size converter with less than 2dB of coupling loss is used for the coupling of the lensed fiber which has 5um of mode field diameter.The PCB for COB packaging and signal transmission is designed to have 6 layers in the hybrid layer structure. 0.25 mm-thick Rogers Duroid RT5880 is used as the first core dielectric layer for high-speed performance over 25 Gbps. It has 0.017 mm-thick of copper layers and its dielectric constant is 2.2and dissipation factor is 0.0009 at 10 GHz. The dimension of both single ended and differential microstrip transmission lines are calculated using full-wave electromagnetic (EM) field simulator HFSS which RF industry is using most. It showed 3dB bandwidth at around 15GHz in S-parameter measurement using network analyzer. The wire bond length for transmission line and ground connection from EIC is done to have less than 300 µm to minimize the parasitic effect to the system.Single layered capacitors (SLC) of 100pF and 1000pF are connected as close as possible to the EICs for stabilizing the DC biasing voltage by decoupling. Its signal transmission performance is under measurement at 25Gbps achieving 100Gbps by 4chx25Gbps. This work can be applied for the active optical cable (AOC) and quad small form-factor pluggable (QSFP) for high-speed optical interconnections. Its demands are quite large in data centers targeting 100 Gbps, 400 Gbps, and 1 Tbps. As the demands of high-speed AOC and QSFP for the application to intra/inter data centers increase, this silicon photonics based high-speed 4 channel CWDM scheme can have advantages not only in data throughput but also cost effectiveness since it reduces fiber cost dramatically through WDM.

Keywords: active optical cable(AOC), 4-channel coarse wavelength division multiplexing (CWDM), communication system, data center, ge-photodiode, Mach Zehnder (MZ) modulator, optical interconnections, optical transceiver, photonics integrated circuits (PIC), quad small form-factor pluggable (QSFP), silicon photonics

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Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

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Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

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Authors: Lukasz Szydlowski, Jiri Ehlich, Igor Goryanin

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We demonstrate a single chamber, 96-well-plated based Microbial Fuel Cell (MFC) with printed, electronic components. This invention is aimed at robust selection of electrogenic microbial community under specific conditions, e.g., electrode potential, pH, nutrient concentration, salt concentration that can be altered within the 96 well plate array. This invention enables robust selection of electrogenic microbial community under the homogeneous reactor, with multiple conditions that can be altered to allow comparative analysis. It can be used as a standalone technique or in conjunction with other selective processes, e.g., flow cytometry, microfluidic-based dielectrophoretic trapping. Mobile conductive elements, like carbon paper, carbon sponge, activated charcoal granules, metal mesh, can be inserted inside to increase the anode surface area in order to collect electrogenic microorganisms and to transfer them into new reactors or for other analytical works. An array of 96-well plate allows this device to be operated by automated pipetting stations.

Keywords: bioengineering, electrochemistry, electromicrobiology, microbial fuel cell

Procedia PDF Downloads 123