Detection method of the Kuroshio front using the satellite-derived chlorophyll-a images

Satellite-derived sea surface temperature (SST) has been providing high-resolution information of the oceanic front. However, in summer, increasing surface heating effects make SSTs uniform and the SST front disappears. The purpose of the present study is to examine applicability of satellite-derived surface chlorophyll-a (Chl-a) for detection of the Kuroshio front in the ocean south of Japan during the summer season.The Kuroshio surface front is formed between the Kuroshio water (KW) and the coastal water (CW) in the study area. Investigating accumulated in situ observations of SST and Chl-a, it is shown that the difference of Chl-a between KW and CW is increased in summer though that of SST becomes small. Using 76 pairs of cloud-free satellite-derived SST and Chl-a images with 0.01° spatial resolution, their seasonal variations are investigated through two-dimensional (2-D) histograms of Chl-a and SST. In summer, SST ranges from 25 to 30 °C and Chl-a ranges 0.03 to 0.4 mg/m³. Two peaks in the summer 2-D histogram correspond to wider KW and CW areas with rather uniform Chl-a and SST distributions. The peak values are extracted from the 2-D histograms and used to determine a threshold value dividing the KW and CW surface Chl-a during summer. It is found that the Chl-a concentration of 0.17 mg/m³ divides the extracted peaks into two groups, i.e., CW and KW except for a few peaks.The proposed Kuroshio-front detection method using the Chl-a images and the threshold value is examined using the satellite images and in situ data. The threshold contour superimposed in the summer Chl-a image distinctly separates the two representative waters in the ocean south of Japan. The front positions determined by the satellite-derived Chl-a, in situ SST, and in situ surface salinity transects along observation lines crossing the Kuroshio front agree with each other. Applying a traditional Kuroshio front index of temperature at 200 m depth, the capability of new Chl-a threshold in dividing two waters around the near-front area is quantitatively examined. Producing 74 match-ups of the satellite-derived Chl-a and 200-m temperature in the frontal zone, the dividing capability is evaluated quantitatively using the traditional Kuroshio-axis index. The detection capability of the proposed method is 82%, which can be considered high enough for practical applications.     

A fault-tolerant pipelined architecture for symmetric block ciphers

Secure transmission over wired/wireless networks requires encryption of data and control information. For high-speed data transmission, it would be desirable to implement the encryption algorithms in hardware. Faults in the hardware, however, may cause interruption of service. This paper presents a simple technique for achieving fault tolerance in pipelined implementation of symmetric block ciphers. It detects errors, locates the corresponding faults, and readily reconfigures during normal operation to isolate the identified faulty modules. Bypass links with some extra pipeline stages are used to achieve fault tolerance. The hardware overhead can be controlled by properly choosing the number of extra stages. Moreover, fault tolerance is achieved with negligible time overhead.     

State diagrams and steady-state balance equations for open queuing network models

In this paper, the state diagrams and steady-state balance equations for two kinds of open queuing network models are presented. The first model comprises a network of single queues with single servers, while the second model comprises multiple servers for single queues. State diagrams are drawn for (2, 3) queuing networks with (i) single servers and (ii) multiple servers. Steady-state balance equations are derived from the state diagrams. The paper provides a method to solve open queuing networks by analyzing the stochastic process involved in the transition of states in a continuous time Markov chain which represents the state diagram of a queuing system.     

Bringing forth worlds

Kress encourages us to understand writing as “transformative engagement in the world,” and his emphasis on the materiality of modes draws our attention to the multiple possibilities offered by different materials and sensory channels. Writing involves the transformation of all aspects of the resources for meaning; thus writing is always an ethical act, and the worlds that different cultures and communities bring forth through their semiotic work offer different possibilities of being.     

Faceless students, virtual places: Emergence and communal accountability in online classrooms

A pedagogical problem growing out of virtual classrooms is the temptation to act without communal accountability, the reciprocal commitment among individuals to maintain the health of their interconnections. Drawing on an ethnographic study of a fully online composition class, I argue that teachers can encourage accountability within virtual sites by conceiving of the online classroom as an emergent phenomenon. The relationships and activities among language, physical reality, and interpretant provide the matrix out of which place organizes itself. This ecological orientation provides local and systemic strategies for fostering communal health. I begin my exploration of online place by describing the value of complex systems theory and emergence for conceptualizing place. Next, I describe the roles of language, physical reality, and interpretant, pointing out the contribution of each to the configuration of virtual place and to communal accountability. Then, I focus on the emergence of place, which reorganizes language, reality, and interpretant, opening up a new dimension to communal accountability.     

Optimal controller tuning for nonlinear processes

The present work proposes a systematic methodology for the optimal selection of controller parameters in the sense of minimizing a performance index, which is a quadratic function of the tracking error and the control effort. The performance index is calculated explicitly as an algebraic function of the controller parameters by solving Zubov's partial differential equation (PDE). Standard optimization techniques are then employed for the calculation of the optimal values of the controller parameters. The solution of Zubov's PDE is also used to estimate the closed-loop stability region for the chosen values of the controller parameters. The proposed approach is finally illustrated in a chemical reactor control problem.     

Consistency analysis of some closed-loop subspace identification methods

We study statistical consistency of two recently proposed subspace identification algorithms for closed-loop systems. These algorithms may be seen as implementations of an abstract state-space construction procedure described by the authors in previous work on stochastic realization of closed-loop systems. A detailed error analysis is undertaken which shows that both algorithms are biased due to an unavoidable mishandling of initial conditions which occurs in closed-loop identification. Instability of the open loop system may also be a cause of trouble.     

Discrete-time certainty equivalence output feedback: allowing discontinuous control laws including those from model predictive control

We present certainty equivalence output feedback results for discrete-time nonlinear systems that employ possibly discontinuous control laws in the feedback loop. Coupling assumptions of nominal robustness with uniform observability or detectability assumptions, we assert nominally robust stability for output feedback closed loops. We further show that model predictive control (MPC) can be used to generate a feedback control law that is robustly globally asymptotically stabilizing when used in a certainty equivalence output feedback closed loop. Allowing for discontinuous feedback control laws is important for systems employing MPC, since the method can, and sometimes necessarily does, result in discontinuous control laws.     

Nonlinear system identification via direct weight optimization

A general framework for estimating nonlinear functions and systems is described and analyzed in this paper. Identification of a system is seen as estimation of a predictor function. The considered predictor function estimate at a particular point is defined to be affine in the observed outputs and the estimate is defined by the weights in this expression. For each given point, the maximal mean-square error (or an upper bound) of the function estimate over a class of possible true functions is minimized with respect to the weights, which is a convex optimization problem. This gives different types of algorithms depending on the chosen function class. It is shown how the classical linear least squares is obtained as a special case and how unknown-but-bounded disturbances can be handled.Most of the paper deals with the method applied to locally smooth predictor functions. It is shown how this leads to local estimators with a finite bandwidth, meaning that only observations in a neighborhood of the target point will be used in the estimate. The size of this neighborhood (the bandwidth) is automatically computed and reflects the noise level in the data and the smoothness priors.The approach is applied to a number of dynamical systems to illustrate its potential.