Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots.

We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.     

Fair multi-agent task allocation for large datasets analysis

MapReduce is a design pattern for processing large datasets distributed on a cluster. Its performances are linked to the data structure and the runtime environment. Indeed, data skew can yield an unfair task allocation, but even when the initial allocation produced by the partition function is well balanced, an unfair allocation can occur during the reduce phase due to the heterogeneous performance of nodes. For these reasons, we propose an adaptive multi-agent system. In our approach, the reducer agents interact during the job and the task reallocation is based on negotiation in order to decrease the workload of the most loaded reducer and so the runtime. In this paper, we propose and evaluate two negotiation strategies. Finally, we experiment our multi-agent system with real-world datasets over heterogeneous runtime environment.     

Mg和V2O5共溅射制备+4价钒的氧化物薄膜

在常温、高真空条件下,采用高纯金属镁靶和V₂O₅靶进行共溅射,利用镁原子的还原性,将+5价的钒还原为+4价,在硅衬底上制备钒的氧化物薄膜。当Mg和V的原子比为1:2时,XPS测试表明薄膜中有V4+和V2+存在。X射线衍射结果显示,制备的薄膜主要成分是MgV₂O₅,且结晶状况良好。温度-电阻率测试结果显示,薄膜在20℃附近有相变行为,电阻温度系数高达－8.6%/K,回线弛豫温度约为0.3℃,负温度系数热敏电阻材料常数高达6700。这一发现为制备非制冷焦平面探测用的热敏薄膜材料提供了新的思路。     

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

     

Evaluation of direct manipulation using finger tracking for complex tasks in an immersive cube

A solution for interaction using finger tracking in a cubic immersive virtual reality system (or immersive cube) is presented. Rather than using a traditional wand device, users can manipulate objects with fingers of both hands in a close-to-natural manner for moderately complex, general purpose tasks. Our solution couples finger tracking with a real-time physics engine, combined with a heuristic approach for hand manipulation, which is robust to tracker noise and simulation instabilities. A first study has been performed to evaluate our interface, with tasks involving complex manipulations, such as balancing objects while walking in the cube. The user’s finger-tracked manipulation was compared to manipulation with a 6 degree-of-freedom wand (or flystick), as well as with carrying out the same task in the real world. Users were also asked to perform a free task, allowing us to observe their perceived level of presence in the scene. Our results show that our approach provides a feasible interface for immersive cube environments and is perceived by users as being closer to the real experience compared to the wand. However, the wand outperforms direct manipulation in terms of speed and precision. We conclude with a discussion of the results and implications for further research.     

A Spatial Regularization Approach for Vector Quantization

Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.     

Voltage- and current-activated metal–insulator transition in VO2-based electrical switches: a lifetime operation analysis

Vanadium dioxide is an intensively studied material that undergoes a temperature-induced metal–insulator phase transition accompanied by a large change in electrical resistivity. Electrical switches based on this material show promising properties in terms of speed and broadband operation. The exploration of the failure behavior and reliability of such devices is very important in view of their integration in practical electronic circuits. We performed systematic lifetime investigations of two-terminal switches based on the electrical activation of the metal–insulator transition in VO₂ thin films. The devices were integrated in coplanar microwave waveguides (CPWs) in series configuration. We detected the evolution of a 10 GHz microwave signal transmitted through the CPW, modulated by the activation of the VO₂ switches in both voltage- and current-controlled modes. We demonstrated enhanced lifetime operation of current-controlled VO₂-based switching (more than 260 million cycles without failure) compared with the voltage-activated mode (breakdown at around 16 million activation cycles). The evolution of the electrical self-oscillations of a VO₂-based switch induced in the current-operated mode is a subtle indicator of the material properties modification and can be used to monitor its behavior under various external stresses in sensor applications.