Abstract-We consider a discrete-time time-varying linear dynamical system, perturbed by process noise, with linear noise corrupted measurements, over a finite horizon. We address the problem of designing a general affine causal controller, in which the control input is an affine function of all previous measurements, in order to minimize a convex objective, in either a stochastic or worst-case setting. This controller design problem is not convex in its natural form, but can be transformed to an equivalent convex optimization problem by a nonlinear change of variables, which allows us to efficiently solve the problem. Our method is related to the classical -design procedure for time-invariant, infinite-horizon linear controller design, and the more recent purified output control method. We illustrate the method with applications to supply chain optimization and dynamic portfolio optimization, and show the method can be combined with model predictive control techniques when perfect state information is available.Index Terms-Affine controller, dynamical system, dynamic linear programming (DLP), linear exponential quadratic Gaussian (LEQG), linear quadratic Gaussian (LQG), model predictive control (MPC), proportional-integral-derivative (PID).

Abstract-We formulate the problem of hyperspectral image unmixing as a nonconvex optimization problem, similar to nonnegative matrix factorization. We present a heuristic for approximately solving this problem using an alternating projected subgradient approach. Finally, we present the results of applying this method on the 1990 AVIRIS image of Cuprite, Nevada and show that our results are in agreement with similar studies on the same data.

Abstract-This paper presents an integrated framework, together with control policies, for optimizing dynamic control of self-tuning parameters of a digital system over its lifetime in the presence of circuit aging. A variety of self-tuning parameters such as supply voltage, operating clock frequency, and dynamic cooling are considered, and jointly optimized using efficient algorithms described in this paper. Our optimized self-tuning approach satisfies performance constraints at all times, and maximizes a lifetime computational power efficiency (LCPE) metric, which is defined as the total number of clock cycles achieved over lifetime divided by the total energy consumed over lifetime. We present three control policies: 1) progressive-worst-case-aging (PWCA), which assumes worst-case aging at all times; 2) progressive-on-stateaging (POSA), which estimates aging by tracking active/sleep modes, and then assumes worst-case aging in active mode and long recovery effects in sleep mode; and 3) progressive-real-timeaging-assisted (PRTA), which acquires real-time information and initiates optimized control actions. Various flavors of these control policies for systems with dynamic voltage and frequency scaling (DVFS) are also analyzed. Simulation results on benchmark circuits, using aging models validated by 45 nm measurements, demonstrate the effectiveness and practicality of our approach in significantly improving LCPE and/or lifetime compared to traditional one-time worst-case guardbanding. We also derive system design guidelines to maximize self-tuning benefits.Index Terms-Adaptive supply voltage and clock frequency, circuit aging, energy-efficiency, lifetime reliability.

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Abstract-We consider a continuous-time linear system with sampled constant linear state-feedback control and a convex quadratic performance measure. The sample times, however, are subject to variation within some known interval. We use linear matrix inequality (LMI) methods to derive a Lyapunov function that establishes an upper bound on performance degradation due to the timing jitter. The same Lyapunov function can be used in a heuristic for finding a bad timing jitter sequence, which gives a lower bound on the possible performance degradation. Numerical experiments show that these two bounds are often close, which means that our bound is tight. We show how LMI methods can be used to synthesize a constant statefeedback controller that minimizes the performance bound, for a given level of timing jitter.