UTB SOI SRAM cell stability under the influence of intrinsic parameter fluctuation

Intrinsic parameter fluctuations steadily increases with CMOS technology scaling. Around the 90nm technology node, such fluctuations will eliminate much of the available noise margin in SRAM based on conventional MOSFETs. Ultra thin body (UTB) SOI MOSFETs are expected to replace conventional MOSFETs for integrated memory applications due to superior electrostatic integrity and better resistant to some of the sources of intrinsic parameter fluctuations. To fully realise the performance benefits of UTB SOI based SRAM cells a statistical circuit simulation methodology which can fully capture intrinsic parameter fluctuation information into the compact model is developed. The impact on 6T SRAM static noise margin characteristics of discrete random dopants in the source/drain regions and body-thickness variations has been investigated for well scaled devices with physical channel length in the range of 10nm to 5nm. A comparison with the behaviour of a 6T SRAM based on a conventional 35nm MOSFET is also presented

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

On hadron deformation: a model independent extraction of EMR from pion photoproduction data

The multipole content of pion photoproduction at the $\Delta^+ (1232)$ resonance has been extracted from a data set dominated by recent Mainz Microtron (MAMI) precision measurements. The analysis has been carried out in the Athens Model Independent Analysis Scheme (AMIAS), thus eliminating any model bias. The benchmark quantity for nucleon deformation, $EMR = E2/M1 = E_{1+}^{3/2}/M_{1+}^{3/2}$, was determined to be $-2.5 \pm 0.4_{stat+syst}$, thus reconfirming in a model independent way that the conjecture of baryon deformation is valid. The derived multipole amplitudes provide stringent constraints on QCD simulations and QCD inspired models striving to describe hadronic structure. They are in good agreement with phenomenological models which explicitly incorporate pionic degrees of freedom and with lattice QCD calculations.Comment: 14 pages, 9 figures, 2 table

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

Proof-Pattern Recognition and Lemma Discovery in ACL2

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it

Multipole Extraction: A novel, model independent method

A novel method for extracting multipole amplitudes in the nucleon resonance region from electroproduction data is presented. The method is based on statistical concepts and it relies heavily on Monte Carlo and simulation techniques; it produces precise identification and determination of the contributing multipole amplitudes in the resonance region and for the first time a rigorous determination of the associated experimental uncertainty. The results are demonstrated to be independent of any model bias. The method is applied in the reanalysis of the $Q^{2}=0.127 GeV^2/c^2$ Bates and Mainz $N\to \Delta$ data.Comment: Proceedings, "Shape of Hadrons" Workshop, 27-29 April 2006, Athens, GREEC

Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio