Phase identification of micro and macro bubbles at the interface of directly bonded GaAs on sapphire

Direct wafer bonding (DWB) of 3 GaAs and R-cut sapphire was performed in a microcleanroom using ultra pure water as cleaning agent. The initial bonding is mediated by Van der Waals forces and hydrogen bridges. The bond energy is released by subsequent heating up to temperatures of 500°C. During heating the formation of macroscopic bubbles at the interface was observed. Details of the interface structure were investigated by cross-sectional as well as plan-view transmission electron microscope (TEM) micrographs. The chemical composition of the elements at the interface was measured by energy dispersive X-ray analysis (EDX) and electron energy loss spectroscopy (EELS). A high density of micro bubbles in bonded areas, a network of micro channels in the transition region and macro bubbles in debonded areas could be distinguished. The macro bubbles are filled with a porous oxide. X-ray diffraction (XRD) and selected area electron diffraction (SAED) revealed the growth of textured -Ga2O3 and elemental arsenic. © 1998 Chapman & Hall     

The effect of load weight on balance control during lateral box transfers

Few studies have endeavoured to measure balance control during manual material handling. This study examined the effects of load weight during a stationary manual material handling task. In total, 36 healthy participants completed 180° lateral transfer tasks of a loaded (5% of body weight) and an unloaded box. The projection of the centre of mass onto the base of support, as measured via a passive-marker 3-D motion analysis system, was used to quantify balance control. Muscle activities of lower extremity muscles were also measured. When moving the loaded box, individuals ventured ≥ 1 cm closer to the edges of the base of support and increased centre of mass movement up to 14%. In addition, muscle electromyographic activity on both sides of the shank increased. In summary, during loaded configurations, vulnerability to loss of balance was increased and individuals appeared to adapt by increasing co-contraction of the shank muscles suggesting increased ankle stiffness. STATEMENT OF RELEVANCE: Industries requiring manual material handling have a particularly high rate of injuries due to falls. This study suggests that larger load weights during lateral material handling tasks adversely affect balance control and may create a vulnerability to imbalance throughout the entire manoeuvre.     

Adaptive locks: Combining transactions and locks for efficient concurrency

Transactional memory is being advanced as an alternative to traditional lock-based synchronization for concurrent programming. Transactional memory simplifies the programming model and maximizes concurrency. At the same time, transactions can suffer from interference that causes them to often abort, from heavy overheads for memory accesses, and from expressiveness limitations (e.g., for I/O operations). In this paper we propose an adaptive locking technique that dynamically observes whether a critical section would be best executed transactionally or while holding a mutex lock. The critical new elements of our approach include the adaptivity logic and cost–benefit analysis, a low-overhead implementation of statistics collection and adaptive locking in a full C compiler, and an exposition of the effects on the programming model. In experiments with both micro and macrobenchmarks we found adaptive locks to consistently match or outperform the better of the two component mechanisms (mutexes or transactions). Compared to either mechanism alone, adaptive locks often provide 3-to-10x speedups. Additionally, adaptive locks simplify the programming model by reducing the need for fine-grained locking: with adaptive locks, the programmer can specify coarse-grained locking annotations and often achieve fine-grained locking performance due to the transactional memory mechanisms.     

Composable continuous-space programs for robotic swarms

Programmability is an increasingly important barrier to the deployment of multi-robot systems, as no prior approach allows routine composition and reuse of general aggregate behaviors. The Proto spatial computing language, however, already provides this sort of aggregate behavior programming for non-mobile systems using an abstraction of the network as a continuous-space-filling device. We extend this abstraction to mobile systems and show that Proto can be applied to multi-robot systems with an actuator that turns a vector field into device motion. Proto programs operate on fields of values over an abstract device called the amorphous medium and can be joined together using functional composition. These programs are then automatically transformed for execution by individual devices, producing an approximation of the specified continuous-space behavior. We are thus able to build up a library of simple swarm behaviors, and to compose them together into highly succinct programs that predictably produce the desired complex swarm behaviors, as demonstrated in simulation and on a group of 40 iRobot SwarmBots.     

Optimal Sparse Matrix Dense Vector Multiplication in the I/O-Model

We study the problem of sparse-matrix dense-vector multiplication (SpMV) in external memory. The task of SpMV is to compute y:=Ax, where A is a sparse N×N matrix and x is a vector. We express sparsity by a parameter k, and for each choice of k consider the class of matrices where the number of nonzero entries is kN, i.e., where the average number of nonzero entries per column is k.     

Solution to profit based unit commitment problem using particle swarm optimization

In this paper, an algorithm to solve the profit based unit commitment problem (PBUCP) under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique to maximize the GENCOs profit. Deregulation in power sector increases the efficiency of electricity production and distribution, offer lower prices, higher quality, a secure and a more reliable product. The proposed algorithm has been developed from the view point of a generation company wishing to maximize its profit in the deregulated power and reserve markets. UC schedule depends on the market price in the deregulated market. In deregulated environment utilities are not required to meet the demand. GENCO can consider a schedule that produce less than the predicted load demand and reserve but creates maximum profit. More number of units are committed when the market price is higher. When more number of generating units are brought online more power is generated and participated in the deregulated market to get maximum profit. This paper presents a new approach of GENCOs profit based unit commitment using PSO technique in a day ahead competitive electricity markets. The profit based unit commitment problem is solved using various PSO techniques such as Chaotic PSO (CPSO), New PSO (NPSO) and Dispersed PSO (DPSO) and the results are compared. Generation, spinning reserve, non-spinning reserve, and system constraints are considered in proposed formulation. The proposed approach has been tested on IEEE-30 bus system with 6 generating units as an individual GENCO. The results obtained are quite encouraging and useful in deregulated market. The algorithm and simulation are carried out using Matlab software.     

Unscented Kalman filter based nonlinear model predictive control of a LDPE autoclave reactor

This paper presents a multivariable nonlinear model predictive control (NMPC) scheme for the regulation of a low-density polyethylene (LDPE) autoclave reactor. A detailed mechanistic process model developed previously was used to describe the dynamics of the LDPE reactor and the properties of the polymer product. Closed-loop simulations are used to demonstrate the disturbance rejection and tracking performance of the NMPC algorithm for control of reactor temperature and weight-averaged molecular weight (WAMW). In addition, the effect of parametric uncertainty in the kinetic rate constants of the LDPE reactor model on closed-loop performance is discussed. The unscented Kalman filtering (UKF) algorithm is employed to estimate plant states and disturbances. All control simulations were performed under conditions of noisy process measurements and structural plant–model mismatch. Where appropriate, the performance of the NMPC algorithm is contrasted with that of linear model predictive control (LMPC). It is shown that for this application the closed-loop performance of the UKF based NMPC scheme is very good and is superior to that of the linear predictive controller.     

Express Virtual Channels with Capacitively Driven Global Links

Networks on chip must deliver high bandwidth at low latencies while keeping within a tight power envelope. Using express virtual channels for flow control improves energy-delay throughput by letting packets bypass intermediate routers, but EVCs have key limitations. Nochi (NoC with hybrid interconnect) overcomes these limitations by transporting data payloads and control information on separate planes, optimized for bandwidth and latency respectively.     

Velocity control of a cylindrical rolling robot by shape changing

A rolling robot is developed that possesses an elliptically shaped outer surface with the ability to change shape as it rolls, resulting in a gravity-powered torque imbalance that accelerates or brakes the robot’s motion. Angular position and velocity are measured onboard and used as feedback control to trigger and define shape change actuation. Goal of the control is to direct the robot to follow a given step angular velocity profile. An equation of motion for the rolling robot is derived and solved numerically, and simulations are compared to velocity data from roll trials of the actual robot. Results show that when the robot is given a set of advantageous initial conditions, it is able to accelerate from rest, maintain constant average velocity, and brake its motion in order to follow a desired velocity profile with significant accuracy.     

Interactive multilevel focus+context visualization framework

In this article, we present the construction of an interactive multilevel focus+context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution technique supported by a balanced wavelet transform (BWT). It extends the mode of focus+context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. We describe the underlying data structure efficiently support such operations. Changes in the spatial locations of query windows defining the ROIs trigger on-demand reconstruction queries. We explain in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization of complex large-scale imagery. We also discuss the performance characteristics of our proposed framework from various aspects, such as time and space complexities and achieved frame rates.