Query: Compact Implementations of ARX-Based Block Ciphers on IoT Processors

Relevant passages:
The SIMON and SPECK lightweight block ciphers <=SEP=> The Simon and Speck families of block ciphers were designed specifically to offer security on constrained devices, where simplicity of design is crucial. However, the intended use cases are diverse and demand flexibility in implementation. Simplicity, security, and flexibility are ever-present yet conflicting goals in cryptographic design. This paper outlines how these goals were balanced in the design of Simon and Speck.

FELICS - Fair Evaluation of Lightweight Cryptographic Systems <=SEP=> In this paper we introduce FELICS, a free and open-source benchmarking framework designed for fair and consistent evaluation of software implementations of lightweight cryptographic primitives for embedded devices. The framework is very flexible thanks to its modular structure, which allows for an easy integration of new metrics, target devices and evaluation scenarios. It consists of two modules that can currently asses the performance of lightweight block and stream ciphers on three widely used microcontrollers: 8-bit AVR, 16-bit MSP and 32-bit ARM. The metrics extracted are execution time, RAM consumption and binary code size. FELICS has a simple user interface and is intended to be used by cipher designers to compare new primitives with the state of the art. The extracted metrics are very detailed and assist embedded software engineers in selecting the best cipher to match the requirements of a particular application. The tool aims to increase the transparency and trust in benchmarking results of lightweight primitives and facilitates a fair comparison between different primitives using the same evaluation conditions.

Block Cipher Speed and Energy Efficiency Records on the MSP430: System Design Trade-Offs for 16-bit Embedded Applications <=SEP=> Embedded microcontroller applications often experience multiple limiting constraints: memory, speed, and for a wide range of portable devices, power. Applications requiring encrypted data must simultaneously optimize the block cipher algorithm and implementation choice against these limitations. To this end we investigate block cipher implementations that are optimized for speed and energy efficiency, the primary metrics of devices such as the MSP430 where constrained memory resources nevertheless allow a range of implementation choices. The results set speed and energy efficiency records for the MSP430 device at 132 cycles/byte and 2.18 μJ/block for AES-128 and 103 cycles/byte and 1.44 μJ/block for equivalent block and key sizes using the lightweight block cipher SPECK. We provide a comprehensive analysis of size, speed, and energy consumption for 24 different variations of AES and 20 different variations of SPECK, to aid system designers of microcontroller platforms optimize the memory and energy usage of secure applications.

The LED Block Cipher <=SEP=> We present a new block cipher LED. While dedicated to compact hardware implementation, and offering the smallest silicon footprint among comparable block ciphers, the cipher has been designed to simultaneously tackle three additional goals. First, we explore the role of an ultra-light (in fact non-existent) key schedule. Second, we consider the resistance of ciphers, and LED in particular, to related-key attacks: we are able to derive simple yet interesting AES-like security proofs for LED regarding relatedor single-key attacks. And third, while we provide a block cipher that is very compact in hardware, we aim to maintain a reasonable performance profile for software implementation.

Linear (Hull) and Algebraic Cryptanalysis of the Block Cipher PRESENT <=SEP=> The contributions of this paper include the first linear hull and a revisit of the algebraic cryptanalysis of reduced-round variants of the block cipher PRESENT, under known-plaintext and ciphertextonly settings. We introduce a pure algebraic cryptanalysis of 5-round PRESENT and in one of our attacks we recover half of the bits of the key in less than three minutes using an ordinary desktop PC. The PRESENT block cipher is a design by Bogdanov et al., announced in CHES 2007 and aimed at RFID tags and sensor networks. For our linear attacks, we can attack 25-round PRESENT with the whole code book, 2 25round PRESENT encryptions, 2 blocks of memory and 0.61 success rate. Further we can extend the linear attack to 26-round with small success rate. As a further contribution of this paper we computed linear hulls in practice for the original PRESENT cipher, which corroborated and even improved on the predicted bias (and the corresponding attack complexities) of conventional linear relations based on a single linear

Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare <=SEP=> The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e.g., through questionnaires or game playing) or to remember to follow a behaviour change plan (e.g., diaries and email reminders). However, recent developments in computational persuasion are leading to an argument-centric approach to persuasion that can potentially be harnessed in behaviour change applications. In this paper, we review developments in computational persuasion, and then focus on domain modelling as a key component. We present a multi-dimensional approach to domain modelling. At the core of this proposal is an ontology which provides a representation of key factors, in particular kinds of belief, which we have identified in the behaviour change literature as being important in diverse behaviour change initiatives. Our proposal for domain modelling is intended to facilitate the acquisition and representation of the arguments that can be used in persuasion dialogues, together with meta-level information about them which can be used by the persuader to make strategic choices of argument to present.

Towards a Multimedia Knowledge-Based Agent with Social Competence and Human Interaction Capabilities <=SEP=> We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.

Temporal multi-hierarchy smoothing for estimating rates of rare events <=SEP=> We consider the problem of estimating rates of rare events obtained through interactions among several categorical variables that are heavy-tailed and hierarchical. In our previous work, we proposed a scalable log-linear model called LMMH (Log-Linear Models for Multiple Hierarchies) that combats data sparsity at granular levels through small sample size corrections that borrow strength from rate estimates at coarser resolutions. This paper extends our previous work in two directions. First, we model excess heterogeneity by fitting local LMMH models to relatively homogeneous subsets of the data. To ensure scalable computation, these subsets are induced through a decision tree, we call this Treed-LMMH. Second, the Treed-LMMH method is coupled with temporal smoothing procedure based on a fast Kalman filter style algorithm. We show that simultaneously performing hierarchical and temporal smoothing leads to significant improvement in predictive accuracy. Our methods are illustrated on a large scale computational advertising dataset consisting of billions of observations and hundreds of millions of attribute combinations(cells).

Security in the Internet of Things: A Survey on Application Layer Protocols <=SEP=> The rapid development of technology nowadays led people to a new and revolutionary concept, named the Internet of Things. This model imposes that all "objects", such as personal objects (smartphones, notebooks, smart watches, tablets etc), electronic equipment embed with sensors and other environmental elements are always connected to a common network. Therefore, one can access any resource at any time, by using a device recognized in the network. While the IoT may be economically and socially beneficial, the implementation of such a system poses many difficulties, risks and security issues that must be taken into consideration. Nowadays, the architecture of the Internet must be updated and rethought in order to interconnect trillions of devices and to ensure interoperability between them. Nevertheless, the most important problem is the security requirements of the IoT, which is probably one of the main reasons of the relatively slow development of this field. This paper presents the most important application layer protocols that are currently used in the IoT context: CoAP, MQTT, XMPP. We discuss them both separately and by comparison, with focus on the security provided by these protocols. Finally, we provide some future research opportunities and conclusions.

Antioxidant defense responses: physiological plasticity in higher plants under abiotic constraints <=SEP=> Environmental stresses (salinity, drought, heat/cold, light and other hostile conditions) may trigger in plants oxidative stress, generating the formation of reactive oxygen species (ROS). These species are partially reduced or activated derivatives of oxygen, comprising both free radical $$ ( {\text{O}}_{2}^{\cdot - } ,{\text{OH}} \cdot , {\text{OH}}_{ 2} \cdot ) $$ and non-radical (H2O2) forms, leading to cellular damage, metabolic disorders and senescence processes. In order to overcome oxidative stress, plants have developed two main antioxidants defense mechanisms that can be classified as non-enzymatic and enzymatic systems. The first class (non-enzymatic) consists of small molecules such as vitamin (A, C and E), glutathione, carotenoids and phenolics that can react directly with the ROS by scavenging them. Second class is represented by enzymes among them superoxide dismutase, peroxidase and catalase which have the capacity to eliminate superoxide and hydrogen peroxide. In this review, we have tried to explore the related works, which have revealed the changes in the basic antioxidant metabolism of plants under various abiotic constraints.

Image analysis of blood microscopic images for acute leukemia detection <=SEP=> Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3–7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy clustering based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.

Beats: Tapping Gestures for Smart Watches <=SEP=> Interacting with smartwatches poses new challenges. Although capable of displaying complex content, their extremely small screens poorly match many of the touchscreen interaction techniques dominant on larger mobile devices. Addressing this problem, this paper presents beating gestures, a novel form of input based on pairs of simultaneous or rapidly sequential and overlapping screen taps made by the index and middle finger of one hand. Distinguished simply by their temporal sequence and relative left/right position these gestures are designed explicitly for the very small screens (approx. 40mm square) of smartwatches and to operate without interfering with regular single touch input. This paper presents the design of beating gestures and a rigorous empirical study that characterizes how users perform them -- in a mean of 355ms and with an error rate of 5.5%. We also derive thresholds for reliably distinguishing between simultaneous (under 30ms) and sequential (under 400ms) pairs of screen touches or releases. We then present five interface designs and evaluate them in a qualitative study in which users report valuing the speed and ready availability of beating gestures.

A Game Theoretic Framework for Analyzing Re-Identification Risk <=SEP=> Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.

The hidden treasure in nursing leadership: informal leaders. <=SEP=> AIM
The goal of the present article was to generate awareness of characteristics of informal leaders in healthcare with the emphasis on nurses in acute care settings. There is limited research or literature regarding informal leaders in nursing and how they positively impact nursing management, the organization and, ultimately, patient care. Identification of nurses with leadership characteristics is important so that leadership development and mentoring can occur within the nursing profession.


BACKGROUND
More than ever, nursing needs energetic, committed and dedicated leaders to meet the challenges of the healthcare climate and the nursing shortage. This requires nurse leaders to consider all avenues to ensure the ongoing profitability and viability of their healthcare facility.


KEY ISSUES
This paper discusses clinical nurses as informal leaders; characteristics of the informal nurse leader, the role they play, how they impact their unit and how they shape the organization.


IMPLICATION FOR NURSING MANAGEMENT
  Informal nurse leaders are an underutilized asset in health care. If identified early, these nurses can be developed and empowered to impact unit performance, efficiency and environmental culture in a positive manner.

An Algorithm for image stitching and blending <=SEP=> In many clinical studies, including those of cancer, it is highly desirable to acquire images of whole tumour sections whilst retaining a microscopic resolution. A usual approach to this is to create a composite image by appropriately overlapping individual images acquired at high magnification under a microscope. A mosaic of these images can be accurately formed by applying image registration, overlap removal and blending techniques. We describe an optimised, automated, fast and reliable method for both image joining and blending. These algorithms can be applied to most types of light microscopy imaging. Examples from histology, from in vivo vascular imaging and from fluorescence applications are shown, both in 2D and 3D. The algorithms are robust to the varying image overlap of a manually moved stage, though examples of composite images acquired both with manually-driven and computer-controlled stages are presented. The overlap-removal algorithm is based on the cross-correlation method; this is used to determine and select the best correlation point between any new image and the previous composite image. A complementary image blending algorithm, based on a gradient method, is used to eliminate sharp intensity changes at the image joins, thus gradually blending one image onto the adjacent ‘composite’. The details of the algorithm to overcome both intensity discrepancies and geometric misalignments between the stitched images will be presented and illustrated with several examples.

INTELLIGENT SYSTEMS IN TOURISM A Social Science Perspective <=SEP=> Intelligent systems sense their environment and learn from the actions they implement to reach specific goals. They are increasingly used to support tourist information search and decision making as well as work processes. In order to model the tourism domain, these systems require a profound understanding of its nature. Looking at existing literature in tourism, this paper discusses critical gaps in the knowledge of the field to be filled so that intelligent system design can be informed and impacts understood. Specifically, it discusses the need to better conceptualize technology in tourism research and argues for a focus on uses and interactions. It challenges simplistic views of tourist information search and decisionmaking processes and calls for more research on potential impacts.

Broadband Capacitively Coupled Stacked Patch Antenna for GNSS Applications <=SEP=> A broadband stacked circular patch antenna is presented in this letter. The broadband characteristic is achieved by employing capacitively coupled feed structure. The antenna is fed by a wideband four-output-ports feed network with equal magnitude and consistent 90° phase shift. The final antenna provides very good circularly polarized radiation for Global Navigation Satellite System applications including GPS, GLONASS, Galileo, and Compass.

Secondary confessions: the influence (or lack thereof) of incentive size and scientific expert testimony on jurors' perceptions of informant testimony. <=SEP=> The goal of this research was to determine whether the size of the incentive (none, small, medium, or large, in terms of sentence reduction) a jailhouse informant receives for testifying, as well as scientific expert testimony regarding the fundamental attribution error, would influence mock juror decision-making in a criminal trial involving a secondary confession. Participants read a murder trial transcript involving informant testimony in which incentive size and expert testimony were manipulated and then provided verdict judgments, made attributions for the informant's decision to testify, and rated the informant and expert on a number of dimensions. Neither expert testimony nor size of incentive had a direct influence on verdicts. However, contrary to previous research on the influence of incentives on jurors' perceptions of secondary confessions, the presence of an incentive did influence verdict decisions, informant ratings, and attributional responses. Results imply that jury-eligible community members may be becoming aware of the issues with informant testimony as a function of incentive but that they are insensitive to the size of the incentive, and expert testimony may not sensitize them to the limitations of such testimony.

Variational Recurrent Neural Networks for Speech Separation <=SEP=> We present a new stochastic learning machine for speech separation based on the variational recurrent neural network (VRNN). This VRNN is constructed from the perspectives of generative stochastic network and variational auto-encoder. The idea is to faithfully characterize the randomness of hidden state of a recurrent neural network through variational learning. The neural parameters under this latent variable model are estimated by maximizing the variational lower bound of log marginal likelihood. An inference network driven by the variational distribution is trained from a set of mixed signals and the associated source targets. A novel supervised VRNN is developed for speech separation. The proposed VRNN provides a stochastic point of view which accommodates the uncertainty in hidden states and facilitates the analysis of model construction. The masking function is further employed in network outputs for speech separation. The benefit of using VRNN is demonstrated by the experiments on monaural speech separation.

Physiological-based emotion recognition with IRS model <=SEP=> A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.

Conceptual Integration of Enterprise Architecture Management and Security Risk Management <=SEP=> Enterprise Architecture Management (EAM) is considered to provide the mechanism for, amongst others, governing enterprise transformations required by changes in the environment. In this paper, we focus on changes that result from the analysis of information security risks and of their impacts on the services delivered by an enterprise. We present how the concepts of an information system security risks management domain can be mapped into the ArchiMate enterprise architecture modeling language. We illustrate the application of the proposed approach through the handling of a lab case.

Particle Belief Propagation <=SEP=> The popularity of particle filtering for inference in Markov chain models defined over random variables with very large or continuous domains makes it natural to consider sample–based versions of belief propagation (BP) for more general (tree–structured or loopy) graphs. Already, several such algorithms have been proposed in the literature. However, many questions remain open about the behavior of particle–based BP algorithms, and little theory has been developed to analyze their performance. In this paper, we describe a generic particle belief propagation (PBP) algorithm which is closely related to previously proposed methods. We prove that this algorithm is consistent, approaching the true BP messages as the number of samples grows large. We then use concentration bounds to analyze the finitesample behavior and give a convergence rate for the algorithm on tree–structured graphs. Our convergence rate is O(1/ √ n) where n is the number of samples, independent of the domain size of the variables.

A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation <=SEP=> In this paper, we adopt 3D CNNs to segment the pancreas in CT images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D applications due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarseto-fine framework for volumetric pancreas segmentation to tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). Moreover, the worst case of DSC on the NIH dataset was improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.

Changes in excitability of the cortical projections to the human tibialis anterior after paired associative stimulation. <=SEP=> Paired associative stimulation (PAS) based on Hebb's law of association can induce plastic changes in the intact human. The optimal interstimulus interval (ISI) between the peripheral nerve and transcranial magnetic stimulus is not known for muscles of the lower leg. The aims of this study were to investigate the effect of PAS for a variety of ISIs and to explore the efficacy of PAS when applied during dynamic activation of the target muscle. PAS was applied at 0.2 Hz for 30 min with the tibialis anterior (TA) at rest. The ISI was varied randomly in seven sessions (n = 5). Subsequently, PAS was applied (n = 14, ISI = 55 ms) with the TA relaxed or dorsi-flexing. Finally, an optimized ISI based on the subject somatosensory evoked potential (SEP) latency plus a central processing delay (6 ms) was used (n = 13). Motor-evoked potentials (MEPs) were elicited in the TA before and after the intervention, and the size of the TA MEP was extracted. ISIs of 45, 50, and 55 ms increased and 40 ms decreased TA MEP significantly (P = 0.01). PAS during dorsi-flexion increased TA MEP size by 92% (P = 0.001). PAS delivered at rest resulted in a nonsignificant increase; however, when the ISI was optimized from SEP latency recordings, all subjects showed significant increases (P = 0.002). No changes in MEP size occurred in the antagonist. Results confirm that the excitability of the corticospinal projections to the TA but not the antagonist can be increased after PAS. This is strongly dependent on the individualized ISI and on the activation state of the muscle.

Characterization of Polymeric Microneedle Arrays for Transdermal Drug Delivery <=SEP=> Microfabrication of dissolvable, swellable, and biodegradable polymeric microneedle arrays (MNs) were extensively investigated based in a nano sensitive fabrication style known as micromilling that is then combined with conventional micromolding technique. The aim of this study was to describe the polymer selection, and optimize formulation compounding parameters for various polymeric MNs. Inverse replication of micromilled master MNs reproduced with polydimethylsiloxane (PDMS), where solid out of plane polymeric MNs were subsequently assembled, and physicochemically characterized. Dissolvable, swellable, and biodegradable MNs were constructed to depth of less than 1 mm with an aspect ratio of 3.6, and 1/2 mm of both inter needle tip and base spacing. Micromolding step also enabled to replicate the MNs very precisely and accurate. Polymeric microneedles (MN) precision was ranging from ± 0.18 to ± 1.82% for microneedle height, ± 0.45 to ± 1.42% for base diameter, and ± 0.22 to ± 0.95% for interbase spacing. Although dissolvable sodium alginate MN showed less physical robustness than biodegradable polylactic-co-glycolic acid MN, their thermogravimetric analysis is of promise for constructing these polymeric types of matrix devices.

Road-marking Analysis for Autonomous Vehicle Guidance <=SEP=> Driving an automobile autonomously on rural roads requires knowledge about the geometry of the road. Furthermore, knowledge about the meaning of each lane of the road is needed in order to decide which lane should be taken and if the vehicle can do a lane change. This paper addresses the problem of extracting additional information about lanes. The information is extracted from the types of road-markings. The type of lane border markings is estimated in order to find out if a lane change is allowed. Arrows, which are painted on the road, are extracted and classified in order to determine the meaning of a lane such as a turn off lane.

Mobile robot navigation based on lidar <=SEP=> A wheeled mobile robot based on Arduino was described in this article. Hardware selection, Simultaneous Localization and Mapping (SLAM), Path Planning and the principle of the algorithm applied in this process are introduced in detail. The algorithm used in ROS is further verified by experiments. In order to verify the feasibility of two different 2D SLAM algorithms, we use the wheeled robot built in the lab to do the experiment in the same indoor environment and compare their results.

Analysis and modelling of a bidirectional push-pull converter with PWM plus phase-shift control <=SEP=> This paper presents analysis and modeling for a bidirectional push-pull dc-dc converter. In particular, attention is paid to leakage current reduction with PWM plus phase-shift control. The leakage current in different operating modes has been compared to identify the efficient modes for various operation conditions. The state space averaging method is adopted to derive the small signal and large signal models of the most efficient mode. A 30~70V/300V prototype was built. Both simulation results and experimental results prove the validity of the model.

SystemT: a system for declarative information extraction <=SEP=> As applications within and outside the enterprise encounter increasing volumes of unstructured data, there has been renewed interest in the area of information extraction (IE) -- the discipline concerned with extracting structured information from unstructured text. Classical IE techniques developed by the NLP community were based on cascading grammars and regular expressions. However, due to the inherent limitations of grammarbased extraction, these techniques are unable to: (i) scale to large data sets, and (ii) support the expressivity requirements of complex information tasks. At the IBM Almaden Research Center, we are developing SystemT, an IE system that addresses these limitations by adopting an algebraic approach. By leveraging well-understood database concepts such as declarative queries and costbased optimization, SystemT enables scalable execution of complex information extraction tasks. In this paper, we motivate the SystemT approach to information extraction. We describe our extraction algebra and demonstrate the effectiveness of our optimization techniques in providing orders of magnitude reduction in the running time of complex extraction tasks.

A Fractional-N Sub-Sampling PLL using a Pipelined Phase-Interpolator With an FoM of -250 dB <=SEP=> A fractional-N sub-sampling PLL architecture based on pipelined phase-interpolator and Digital-to-Time-Converter (DTC) is presented in this paper. The combination of pipelined phase-interpolator and DTC enables efficient design of the multi-phase generation mechanism required for the fractional operation. This technique can be used for designing a fractional-N PLL with low in-band phase noise and low spurious tones with low power consumption. The short-current-free pipelined phase-interpolator used in this work is capable of achieving high-linearity with low-power while minimizing the intrinsic jitter. A number of other circuit techniques and layout techniques are also employed in this design for ensuring high-performance operation with minimal chip area and power consumption. The proposed fractional-N PLL is implemented in standard 65 nm CMOS technology. The PLL has an operating range of 600 MHz from 4.34 GHz to 4.94 GHz. In fractional-N mode, the proposed PLL achieves -249.5 dB FoM and less than -59 dBc fractional spurs.