Dynamics of quantum-dot mode-locked lasers with optical injection

We present the dynamics of quantum-dot passively mode-locked semiconductor lasers under optical injection. We discuss the benefits of various configurations of the master source including single, dual, and multiple coherent frequency sources. In particular, we demonstrate that optical injection can improve the properties of the slave laser in terms of time-bandwidth product, optical linewidth, and timing jitter.

Stabilization of self-coherent OFDM with injection locked laser

With the rebirth of coherent detection, various algorithms have come forth to alleviate phase noise, one of the main impairments for coherent receivers. These algorithms provide stable compensation, however they limit the DSP. With this key issue in mind, Fabry Perot filter based self coherent optical OFDM was analyzed which does not require phase noise compensation reducing the complexity in DSP at low OSNR. However, the performance of such a receiver is limited due to ASE noise at the carrier wavelength, especially since an optical amplifier is typically employed with the filter to ensure sufficient carrier power. Subsequently, the use of an injection-locked laser (ILL) to retrieve the frequency and phase information from the extracted carrier without the use of an amplifier was recently proposed. In ILL based system, an optical carrier is sent along with the OFDM signal in the transmitter. At the receiver, the carrier is extracted from the OFDM signal using a Fabry-Perot tunable filter and an ILL is used to significantly amplify the carrier and reduce intensity and phase noise. In contrast to CO-OFDM, such a system supports low-cost broad linewidth lasers and benefits with lower complexity in the DSP as no carrier frequency estimation and correction along with phase noise compensation is required.

Processing of highly porous titanium parts by metal injection moulding in combination with innovative plasma treatment

A combinação da Moldagem por Injeção de pós Metálicos (Metal Injection Moulding MIM) e o Método do Retentor Espacial (Space Holder Method - SHM) é uma técnica promissora para fabricação de peças porosas de titânio com porosidade bem definida como implantes biomédicos, uma vez que permite um alto grau de automatização e redução dos custos de produção em larga escala quando comparado a técnica tradicional (SHM e usinagem a verde). Contudo a aplicação desta técnica é limitada pelo fato que há o fechamento parcial da porosidade na superfície das amostras, levando ao deterioramento da fixação do implante ao osso. E além disso, até o presente momento não foi possível atingir condições de processamento estáveis quando a quantidade de retentor espacial excede 50 vol. %. Entretanto, a literatura descreve que a melhor faixa de porosidade para implantes de titânio para coluna vertebral está entre 60 - 65 vol. %. Portanto, no presente estudo, duas abordagens foram conduzidas visando a produção de amostras altamente porosas através da combinação de MIM e SHM com o valor constante de retentor espacial de 70 vol. % e uma porosidade aberta na superfície. Na primeira abordagem, a quantidade ótima de retentor espacial foi investigada, para tal foram melhorados a homogeneização do feedstock e os parâmetros de processo com o propósito de permitir a injeção do feedstock. Na segunda abordagem, tratamento por plasma foi aplicado nas amostras antes da etapa final de sinterização. Ambas rotas resultaram na melhoria da estabilidade dimensional das amostras durante a extração térmica do ligante e sinterização, permitindo a sinterização de amostras de titânio altamente porosas sem deformação da estrutura.

Individual monitoring of immune responses in rainbow trout after cohabitation and intraperitoneal injection challenge with Yersinia ruckeri

Acknowledgements This work was funded by the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs, grant G1100675). The authors are grateful to the aquarium staff at the University of Aberdeen (Karen Massie) and Dr David Smail at Marine Scotland for valuable discussion during the establishment of the experimental design.

Coagent Modified Polypropylene Prepared by Reactive Extrusion: A New Look Into the Structure-Property Relations of Injection Molded Parts

Peroxide-mediated reactive extrusion of linear isotactic polypropylene (L-PP) was conducted in the presence of trimethylolpropane trimethacrylate (TMPTMA) and triallyl trimesate (TAM) coagents, using a twin screw extruder. The resulting coagent-modified polypropylenes (CM-PP) had higher viscosities and elasticities, as well as increased crystallization temperature compared to PP reacted only with peroxide (DCP-PP). Additionally, deviations from terminal flow, and strain hardening were observed in PP modified with TAM, signifying the presence of long chain branching (LCB). The CM-PP formulations retained the modulus and tensile strength of the parent L-PP, in spite of their lower molar mass and viscosities, whereas their elongation at break and the impact strength were better. This was attributed to the finer spherulitic structure of these materials, and to the disappearance of the skin-core layer in the injection molded specimens.

Method to Improve the Linearity of Active Commutating Mixers Using Dynamic Current Injection

A double balanced (DBM) CMOS mixer providing high linearity is presented in this paper. A cross-coupled pair used in the IF stage of the mixer to dynamically inject current into the to mixer provide a high linearity. The proposed DBM was fabricated using a standard 130-nm CMOS process and was tested on-wafer. The double balanced mixer delivers 10 dB conversion gain, 9.5 dBm IIP3, and input P1dB of -2.4 dBm. RF bandwidth of the proposed mixer is 6 GHz, covering 0.5 GHz to 6.5 GHz with IF bandwidth of 300 MHz. RF to IF and LO to IF isolation are also better than 59 dB in the whole frequency band. The circuit uses an area of 0.015 mm2 excluding bonding pads and draw 4.5mW from a 1.2V supply.

Mixing behaviour of side injection of air jets and gaseous fuel jets into the axial flow of tunnel kilns

Otto-von-Guericke-Universität Magdeburg, Fakultät für Verfahrens- und Systemtechnik, Dissertation, 2016

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

An investigation into PL/SQL Injection.

SQL injection is a common attack method used to leverage infor-mation out of a database or to compromise a company’s network. This paper investigates four injection attacks that can be conducted against the PL/SQL engine of Oracle databases, comparing two recent releases (10g, 11g) of Oracle. The results of the experiments showed that both releases of Oracle were vulner-able to injection but that the injection technique often differed in the packages that it could be conducted in.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.