Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization.

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

Pioneer factor Pax7 initiates two-step cell-cycle-dependent chromatin opening.

Pioneer transcription factors direct cell differentiation by deploying new enhancer repertoires through their unique ability to target and initiate remodelling of closed chromatin. The initial steps of their action remain undefined, although pioneers have been shown to interact with nucleosomal target DNA and with some chromatin-remodeling complexes. We now define the sequence of events that enables the pioneer Pax7 with its unique abilities. Chromatin condensation exerted by linker histone H1 is the first constraint on Pax7 recruitment, and this establishes the initial speed of chromatin remodeling. The first step of pioneer action involves recruitment of the KDM1A (LSD1) H3K9me2 demethylase for removal of this repressive mark, as well as recruitment of the MLL complex for deposition of the activating H3K4me1 mark. Further progression of pioneer action requires passage through cell division, and this involves dissociation of pioneer targets from perinuclear lamin B. Only then are the SWI-SNF remodeling complex and the coactivator p300 recruited, leading to nucleosome displacement and enhancer activation. Thus, the unique features of pioneer actions are those occurring in the lamin-associated compartment of the nucleus. This model is consistent with previous work that showed a dependence on cell division for establishment of new cell fates.

Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles.

This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k-means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source-Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3× speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU.