982 resultados para ADAPTIVE RECOVERY CLOCK
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A single electroabsorption modulator was used to demultiplex a 10 Gbit/s channel from a 40 Gbit/s OTDM data stream, whilst simultaneously recovering the 10 GHz electrical clock. This was achieved using a new bi-directional operation of the EA modulator, combined with a simple phase-locked loop feedback circuit. Excellent system performance was achieved, indicating that operation up to and beyond 100 Gbit/s is possible using current technology.
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High-speed optical clock recovery, demultiplexing and data regeneration will be integral parts of any future photonic network based on high bit-rate OTDM. Much research has been conducted on devices that perform these functions, however to date each process has been demonstrated independently. A very promising method of all-optical switching is that of a semiconductor optical amplifier-based nonlinear optical loop mirror (SOA-NOLM). This has various advantages compared with the standard fiber NOLM, most notably low switching power, compact size and stability. We use the SOA-NOLM as an all-optical mixer in a classical phase-locked loop arrangement to achieve optical clock recovery, while at the same time achieving data regeneration in a single compact device
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We demonstrate simultaneous demultiplexing, data regeneration and clock recovery at 10Gbits/s, using a single semiconductor optical amplifier–based nonlinear-optical loop mirror in a phase-locked loop configuration.
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The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.
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Stream ciphers are encryption algorithms used for ensuring the privacy of digital telecommunications. They have been widely used for encrypting military communications, satellite communications, pay TV encryption and for voice encryption of both fixed lined and wireless networks. The current multi year European project eSTREAM, which aims to select stream ciphers suitable for widespread adoptation, reflects the importance of this area of research. Stream ciphers consist of a keystream generator and an output function. Keystream generators produce a sequence that appears to be random, which is combined with the plaintext message using the output function. Most commonly, the output function is binary addition modulo two. Cryptanalysis of these ciphers focuses largely on analysis of the keystream generators and of relationships between the generator and the keystream it produces. Linear feedback shift registers are widely used components in building keystream generators, as the sequences they produce are well understood. Many types of attack have been proposed for breaking various LFSR based stream ciphers. A recent attack type is known as an algebraic attack. Algebraic attacks transform the problem of recovering the key into a problem of solving multivariate system of equations, which eventually recover the internal state bits or the key bits. This type of attack has been shown to be effective on a number of regularly clocked LFSR based stream ciphers. In this thesis, algebraic attacks are extended to a number of well known stream ciphers where at least one LFSR in the system is irregularly clocked. Applying algebriac attacks to these ciphers has only been discussed previously in the open literature for LILI-128. In this thesis, algebraic attacks are first applied to keystream generators using stop-and go clocking. Four ciphers belonging to this group are investigated: the Beth-Piper stop-and-go generator, the alternating step generator, the Gollmann cascade generator and the eSTREAM candidate: the Pomaranch cipher. It is shown that algebraic attacks are very effective on the first three of these ciphers. Although no effective algebraic attack was found for Pomaranch, the algebraic analysis lead to some interesting findings including weaknesses that may be exploited in future attacks. Algebraic attacks are then applied to keystream generators using (p; q) clocking. Two well known examples of such ciphers, the step1/step2 generator and the self decimated generator are investigated. Algebraic attacks are shown to be very powerful attack in recovering the internal state of these generators. A more complex clocking mechanism than either stop-and-go or the (p; q) clocking keystream generators is known as mutual clock control. In mutual clock control generators, the LFSRs control the clocking of each other. Four well known stream ciphers belonging to this group are investigated with respect to algebraic attacks: the Bilateral-stop-and-go generator, A5/1 stream cipher, Alpha 1 stream cipher, and the more recent eSTREAM proposal, the MICKEY stream ciphers. Some theoretical results with regards to the complexity of algebraic attacks on these ciphers are presented. The algebraic analysis of these ciphers showed that generally, it is hard to generate the system of equations required for an algebraic attack on these ciphers. As the algebraic attack could not be applied directly on these ciphers, a different approach was used, namely guessing some bits of the internal state, in order to reduce the degree of the equations. Finally, an algebraic attack on Alpha 1 that requires only 128 bits of keystream to recover the 128 internal state bits is presented. An essential process associated with stream cipher proposals is key initialization. Many recently proposed stream ciphers use an algorithm to initialize the large internal state with a smaller key and possibly publicly known initialization vectors. The effect of key initialization on the performance of algebraic attacks is also investigated in this thesis. The relationships between the two have not been investigated before in the open literature. The investigation is conducted on Trivium and Grain-128, two eSTREAM ciphers. It is shown that the key initialization process has an effect on the success of algebraic attacks, unlike other conventional attacks. In particular, the key initialization process allows an attacker to firstly generate a small number of equations of low degree and then perform an algebraic attack using multiple keystreams. The effect of the number of iterations performed during key initialization is investigated. It is shown that both the number of iterations and the maximum number of initialization vectors to be used with one key should be carefully chosen. Some experimental results on Trivium and Grain-128 are then presented. Finally, the security with respect to algebraic attacks of the well known LILI family of stream ciphers, including the unbroken LILI-II, is investigated. These are irregularly clock- controlled nonlinear filtered generators. While the structure is defined for the LILI family, a particular paramater choice defines a specific instance. Two well known such instances are LILI-128 and LILI-II. The security of these and other instances is investigated to identify which instances are vulnerable to algebraic attacks. The feasibility of recovering the key bits using algebraic attacks is then investigated for both LILI- 128 and LILI-II. Algebraic attacks which recover the internal state with less effort than exhaustive key search are possible for LILI-128 but not for LILI-II. Given the internal state at some point in time, the feasibility of recovering the key bits is also investigated, showing that the parameters used in the key initialization process, if poorly chosen, can lead to a key recovery using algebraic attacks.
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We examined acute molecular responses in skeletal muscle to repeated sprint and resistance exercise bouts. Six men [age, 24.7 ± 6.3 yr; body mass, 81.6 ± 7.3 kg; peak oxygen uptake, 47 ± 9.9 ml·kg -1 ·min -1; one repetition maximum (1-RM) leg extension 92.2 ± 12.5 kg; means ± SD] were randomly assigned to trials consisting of either resistance exercise (8 × 5 leg extension, 80% 1-RM) followed by repeated sprints (10 × 6 s, 0.75 N·m torque·kg -1) or vice-versa. Muscle biopsies from vastus lateralis were obtained at rest, 15 min after each exercise bout, and following 3-h recovery to determine early signaling and mRNA responses. There was divergent exercise order-dependent phosphorylation of p70 S6K (S6K). Specifically, initial resistance exercise increased S6K phosphorylation (?75% P < 0.05), but there was no effect when resistance exercise was undertaken after sprints. Exercise decreased IGF-I mRNA following 3-h recovery (?50%, P = 0.06) independent of order, while muscle RING finger mRNA was elevated with a moderate exercise order effect (P < 0.01). When resistance exercise was followed by repeated sprints PGC-1? mRNA was increased (REX1-SPR2; P = 0.02) with a modest distinction between exercise orders. Repeated sprints may promote acute interference on resistance exercise responses by attenuating translation initiation signaling and exacerbating ubiquitin ligase expression. Indeed, repeated sprints appear to generate the overriding acute exercise-induced response when undertaking concurrent repeated sprint and resistance exercise. Accordingly, we suggest that sprint-activities are isolated from resistance training and that adequate recovery time is considered within periodized training plans that incorporate these divergent exercise modes.
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PURPOSE: Regulation of skeletal muscle mass is highly dependent on contractile loading. The purpose of this study was to examine changes in growth factor and inflammatory pathways following high-frequency resistance training. METHODS: Using a novel design in which male Sprague-Dawley rats undertook a "stacked" resistance training protocol designed to generate a summation of transient exercise-induced signaling responses (four bouts of three sets × 10 repetitions of squat exercise, separated by 3 h of recovery), we determined the effects of high training frequency on signaling pathways and transcriptional activity regulating muscle mass. RESULTS: The stacked training regimen resulted in acute suppression of insulin-like growth factor 1 mRNA abundance (P < 0.05) and Akt phosphorylation (P < 0.05), an effect that persisted 48 h after the final training bout. Conversely, stacked training elicited a coordinated increase in the expression of tumor necrosis factor alpha, inhibitor kappa B kinase alpha/beta activity (P < 0.05), and p38 mitogen-activated protein kinase phosphorylation (P < 0.05) at 3 h after each training bout. In addition, the stacked series of resistance exercise bouts induced an increase in p70 S6 kinase phosphorylation 3 h after bouts ×3 and ×4, independent of the phosphorylation state of Akt. CONCLUSIONS: Our results indicate that high resistance training frequency extends the transient activation of inflammatory signaling cascades, concomitant with persistent suppression of key mediators of anabolic responses. We provide novel insights into the effects of the timing of exercise-induced overload and recovery on signal transduction pathways and transcriptional activity regulating skeletal muscle mass in vivo.
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Successful biodiversity conservation requires safeguarding viable populations of species. To work with this challenge Sweden has introduced a concept of Action Plans, which focus on the recovery of one or more species; while keeping in mind the philosophy of addressing ecosystems in a more comprehensive way, following the umbrella concept. In this paper we investigate the implementationprocess of the ActionPlanfor one umbrella species, the White-backed Woodpecker (WBW) Dendrocopos leucotos. We describe the plan's organisation and goals, and investigate its implementation and accomplishment of particular targets, based on interviewing and surveying the key actors. The achievement of the targets in 2005-2008 was on average much lower than planned, explained partially by the lack of knowledge/data, experienced workers, and administrative flexibility. Surprisingly, the perceived importance of particular conservation measures, the investment priority accorded to them, the money available and various practical obstacles all failed to kg? explain the target levels achieved. However qualitative data from both the interviews and the survey highlight possible implementation obstacles: competing interests with other conservation actions and the level of engagement of particular implementing actors. Therefore we suggest that for successful implementation of recovery plans, there is aneed for initial and inclusive scoping prior to embarking on the plan, where not only issues like ecological knowledge and practical resources are considered, but also possible conflicts and synergies with other conservation actions. An adaptive approach with regular review of the conservation process is essential, particularly in the case of such complex action plans as the one for the WBW.
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The concept of specificity of exercise prescription and training is a longstanding and widely accepted foundation of the exercise sciences. Simply, the principle holds that training adaptations are achieved relative to the stimulus applied. That is, the manipulation of training variables (e.g. intensity or loading, mode, volume and frequency) directly influences the acute training stimulus, and so the long-term adaptive response (Young et al., 2001; Bird et al., 2005). Translating this concept to practice then recommends that exercise be prescribed specific to the desired outcomes, and the more closely this is achieved, the greater the performance gain is likely to be. However, the cardiovascular and metabolic adaptations traditionally associated with long, slow distance training types, similarly achieved using high-intensity training methods (for a review see Gibala et al., 2012), highlights understanding of underlying physiology as paramount for effective training program design. Various other factors including illness, sleep and psychology also impact on the training stimulus (Halson, 2014) and must be managed collectively with appropriate post-exercise recovery to continue performance improvements and reduce overtraining and injury risks (Kenttä and Hassmén, 1998).
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Changes at work are often accompanied with the threat of, or actual, resource loss. Through an experiment, we investigated the detrimental effect of the threat of resource loss on adaptive task performance. Self-regulation (i.e., task focus and emotion control) was hypothesized to buffer the negative relationship between the threat of resource loss and adaptive task performance. Adaptation was conceptualized as relearning after a change in task execution rules. Threat of resource loss was manipulated for 100 participants undertaking an air traffic control task. Using discontinuous growth curve modeling, 2 kinds of adaptation—transition adaptation and reacquisition adaptation—were differentiated. The results showed that individuals who experienced the threat of resource loss had a stronger drop in performance (less transition adaptation) and a subsequent slower recovery (less reacquisition adaptation) compared with the control group who experienced no threat. Emotion control (but not task focus) moderated the relationship between the threat of resource loss and transition adaptation. In this respect, individuals who felt threatened but regulated their emotions performed better immediately after the task change (but not later on) compared with those individuals who felt threatened and did not regulate their emotions as well. However, later on, relearning (reacquisition adaptation) under the threat of resource loss was facilitated when individuals concentrated on the task at hand.
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We present a Bayesian sampling algorithm called adaptive importance sampling or population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.
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Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an ``adaptive threshold,'' i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
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Compressive Sensing theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate and computational complexity of the measurement system. In recent years, many recovery algorithms were proposed to reconstruct the signal efficiently. Look Ahead OMP (LAOMP) is a recently proposed method which uses a look ahead strategy and performs significantly better than other greedy methods. In this paper, we propose a modification to the LAOMP algorithm to choose the look ahead parameter L adaptively, thus reducing the complexity of the algorithm, without compromising on the performance. The performance of the algorithm is evaluated through Monte Carlo simulations.
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Layered steam injection, widely used in Liaohe Oilfield at Present, is an effective recovery technique to heavy oil reserves. Which makes the steam front-peak push forward uniformly, the amount of steam injection be assigned rationally, and the effect of injection steam be obtained as expected. To maintain a fixed ratio of layered steam injection and solve the problem of nonadjustable hole diameter with the change of layer pressure in the existing injectors, a new method is proposed in this paper to design layered steam injectors based on the dynamic balance theory. According to gas-liquid two-phase flow theory and beat transfer theory, the energy equation and the heat conduction equation in boreholes are developed. By analyzing the energy equilibrium of water-steam passing through the injector hole, we find an expression to describe the relation between the cross-sectional area of injector hole and the layer pressure. With this expression, we provide a new set of calculation methods and write the corresponding computer program to design and calculate the main parameters of a steam injector. The actual measurement data show that the theoretically calculated results are accurate, the software runs reliably, and they provide the design of self-adjustable layered steam injectors with the theoretical foundation.
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Layered steam injection, widely used in Liaohe Oilfield at present, is an effective recovery technique to heavy oil reserves. Which makes the steam front-peak push forward uniformly, the amount of steam injection be assigned rationally, and the effect of injection steam be obtained as expected. To maintain a fixed ratio of layered steam injection and solve the problem of nonadjustable hole diameter with the change of layer pressure in the existing injectors, a new method is proposed in this paper to design layered steam injectors based on the dynamic balance theory According to gas-liquid two-phase flow theory and heat transfer theory, the energy equation and the heat conduction equation in boreholes are developed. By analyzing the energy equilibrium of water-steam passing through the injector hole, we find an expression to describe the relation between the cross-sectional area of injector hole and the layer pressure. With this expression, we provide a new set of calculation methods and write the corresponding computer program to design and calculate the main parameters of a steam injector. The actual measurement data show that the theoretically calculated results are accurate, the software runs reliably, and they provide the design of self-adjustable layered steam injectors with the theoretical foundation.