Flapping wing micro air vehicles (MAVs) take inspiration from natural fliers, such as insects and hummingbirds. Existing designs manage to mimic the wing motion of natural fliers to a certain extent; nevertheless, differences will always exist due to completely different building blocks of biological and man-made systems. The same holds true for the design of the wings themselves, as biological and engineering materials differ significantly. This paper presents results of experimental optimization of wing shape of a flexible wing for a hummingbird-sized flapping wing MAV. During the experiments we varied the wing 'slackness' (defined by a camber angle), the wing shape (determined by the aspect and taper ratios) and the surface area. Apart from the generated lift, we also evaluated the overall power efficiency of the flapping wing MAV achieved with the various wing design. The results indicate that especially the camber angle and aspect ratio have a critical impact on the force production and efficiency. The best performance was obtained with a wing of trapezoidal shape with a straight leading edge and an aspect ratio of 9.3, both parameters being very similar to a typical hummingbird wing. Finally, the wing performance was demonstrated by a lift-off of a 17.2 g flapping wing robot.

Hovering flapping flight is inherently unstable and needs to be stabilized actively. We present a control mechanism that modulates independently the wing flapping amplitude and offset by displacing joints of a flapping linkage mechanism. We demonstrate its performance by high speed camera recordings of the wing motion as well as by direct measurements of pitch moment and lift force. While flapping at 17 Hz the prototype produces 90 mN of lift and generates pitch moments from-0.7 N.mm to 1.1 N.mm. The mechanism shows low level of cross-coupling in combined pitch and roll commands.

Abstract-In this paper, we propose an efficient implementation of the branch and bound method for knapsack problems on a CPU-GPU system via CUDA. Branch and bound computations can be carried out either on the CPU or on a GPU according to the size of the branch and bound list. A better management of GPUs memories, less GPU-CPU communications and better synchronization between GPU threads are proposed in this new implementation in order to increase efficiency. Indeed, a series of computational results is displayed and analyzed showing a substantial speedup on a Tesla C2050 GPU.

The Simplex algorithm is a well known method to solve linear programming (LP) problems. In this paper, we propose a parallel implementation of the Simplex on a CPU-GPU systems via CUDA. Double precision implementation is used in order to improve the quality of solutions. Computational tests have been carried out on randomly generated instances for non-sparse LP problems. The tests show a maximum speedup of 12.5 on a GTX 260 board.

International audienceThe Simplex algorithm is a well known method to solve linear programming (LP) problems. In this paper, we propose an implementation via CUDA of the Simplex method on a multi GPU architecture. Computational tests have been carried out on randomly generated instances for non-sparse LP problems. The tests show a maximum speedup of 24.5 with two Tesla C2050 boards. I. INTRODUCTION Initially developed for real time and high-definition 3D graphic applications, Graphics Processing Units (GPUs) have gained recently attention for High Performance Computing applications. Indeed, the peak computational capabilities of modern GPUs exceeds the one of top-of-the-line central processing units (CPUs). GPUs are highly parallel, multithreaded, manycore units. In November 2006, NVIDIA introduced, Compute Unified Device Architecture (CUDA), a technology that enables users to solve many complex problems on their GPU cards (see for example [1]-[4]). Some related works have been presented on the parallel implementation of algorithms on GPU for linear programming (LP) problems. O'Leary and Jung have proposed in [5] a combined CPU-GPU implementation of the Interior Point Method for LP; computational results carried out on NETLIB LP problems [6] for at most 516 variables and 758 constraints, show that some speedup can be obtained by using GPU for sufficiently large dense problems. Spampinato and Elster have proposed in [7] a parallel implementation of the revised Simplex method for LP on GPU with NVIDIA CUBLAS [8] and NVIDIA LAPACK [9] libraries. Tests were carried out on randomly generated LP problems of at most 2000 variables and 2000 constraints. The implementation showed a maximum speedup of 2.5 on a NVIDIA GTX 280 GPU as compared with sequential implementation on CPU with Intel Core2 Quad 2.83 GHz. Bieling, Peschlow and Martini have proposed in [10] an other implementation of the revised Simplex method on GPU. This implementation permits one to speed up solution with a maximum factor of 18 in single precision on a NVIDIA GeForce 9600 GT GPU card as compared with GLPK solver run on Intel Core 2 Duo 3GHz CPU. In [11], we have presented a parallel implementation via CUD

International audienceHybrid implementation via CUDA of a branch and bound method for knapsack problems is proposed. Branch and bound computations can be carried out either on the CPU or on the GPU according to the size of the branch and bound list, i.e. the number of nodes. Tests are carried out on a Tesla C2050 GPU. A first series of computational results showing a substantial speedup is displayed and analyzed

Hovering flapping wing flight is intrinsically unstable in most cases and requires active flight stabilization mechanisms. This paper explores the passive stability enhancement with the addition of top and bottom sails, and the capability to predict the stability from a very simple model decoupling the roll and pitch axes. The various parameters involved in the dynamical model are evaluated from experiments. One of the findings is that the damping coefficient of a bottom sail (located in the flow induced by the flapping wings) is significantly larger than that of a top sail. Flight experiments have been conducted on a flapping wing robot of the size of a hummingbird with sails of various sizes and the observations regarding the flight stability correlate quite well with the predictions of the dynamical model. Twelve out of 13 flight experiments are in agreement with stability predictions.

Abstract:In this paper, we present a heuristic which derives a feasible solution for the Multiple Knapsack Problem (MKP). The proposed heuristic called RCH, is a recursive method that performs computation on the core of knapsacks. The RCH heuristic is compared with the MTHM heuristic of Martello and Toth. Computational results on randomly generated instances show that the proposed approach gives better gap and smaller restitution times.