As a key water quality parameter in the wastewater treatment process (WWTP), the accurate measurement of total phosphorus (TP) would effectively prevent the effluent water from eutrophication. Although soft measurement models can successfully predict effluent TP, the model prediction is unreliable because outliers will inevitably exist in actual WWTP due to a variety of disturbances. To solve this problem, a novel robust small-world feedforward neural network (RSWFNN) is proposed to improve the robustness of effluent TP prediction. First, the robust Spearman rank correlation analysis is used to determine auxiliary variables intrinsically correlated with the effluent TP. Second, inspired by the fault tolerance of the human brain from its small world property, the small-worldness is introduced to obtain a robust network architecture. Third, the robust learning algorithm using the loss function of regularized M-estimation is proposed to suppress the responses of outliers to improve the robustness of the model. Finally, the corresponding two hyperparameters are determined by an adaptive adjustment strategy, thus ensuring the effectiveness of suppressing outliers. Our experimental results have shown that RSWFNN has stronger robustness and better prediction performance to predict effluent TP than other modeling methods, and the superiority of robustness becomes more obvious with the increase of outlier proportion.

The decreasing cost and size of video sensors has led to camera networks becoming pervasive in our lives. However, the ability to analyze these images effectively is very much a function of the quality of the acquired images. In this paper, we consider the problem of automatically controlling the fields of view of individual pan-tilt-zoom (PTZ) cameras in a camera network leading to improved situation awareness (e.g., where and what are the critical targets and events) in a region of interest. The network of cameras attempts to observe the entire region of interest at some minimum resolution while opportunistically acquiring high resolution images of critical events in real time. Since many activities involve groups of people interacting, an important decision that the network needs to make is whether to focus on individuals or groups of them. This is achieved by understanding the performance of video analysis tasks and designing camera control strategies to improve a metric that quantifies the quality of the source imagery. Optimization strategies, along with a distributed implementation, are proposed, and their theoretical properties analyzed. The proposed methods bring together computer vision and network control ideas. The performance of the proposed methodologies discussed herein has been evaluated on a real-life wireless network of PTZ capable cameras.

The performance of dynamic scene algorithms often suffers because of the inability to effectively acquire features on the targets, particularly when they are distributed over a wide field of view. In this paper, we propose an integrated analysis and control framework for a pan, tilt, zoom (PTZ) camera network in order to maximize various scene understanding performance criteria (e.g., tracking accuracy, best shot, and image resolution) through dynamic camera-to-target assignment and efficient feature acquisition. Moreover, we consider the situation where processing is distributed across the network since it is often unrealistic to have all the image data at a central location. In such situations, the cameras, although autonomous, must collaborate among themselves because each camera's PTZ parameter entails constraints on the others. Motivated by recent work in cooperative control of sensor networks, we propose a distributed optimization strategy, which can be modeled as a game involving the cameras and targets. The cameras gain by reducing the error covariance of the tracked targets or through higher resolution feature acquisition, which, however, comes at the risk of losing the dynamic target. Through the optimization of this reward-versus-risk tradeoff, we are able to control the PTZ parameters of the cameras and assign them to targets dynamically. The tracks, upon which the control algorithm is dependent, are obtained through a consensus estimation algorithm whereby cameras can arrive at a consensus on the state of each target through a negotiation strategy. We analyze the performance of this collaborative sensing strategy in active camera networks in a simulation environment, as well as a real-life camera network.

In today's high performance computing, many MPI programs (e.g., ScaLAPACK applications, High Performance Linpack Benchmark HPL, and many PDE solvers based on domain decomposition methods) organize their computational processes as multidimensional process grids. Communications are often necessary in each dimension. Multidimensional broadcast, where a broadcast has to be performed in each dimension, is one of the many operations in applications that use multidimensional process grids. In this paper, we study the impact of the MPI process-to-core mapping on the performance of multidimensional broadcast operations. We show that the default process-to-core mappings in today's state-of-the-art MPI implementations are often sub-optimal for multidimensional broadcast. We propose an application-level multicore-aware process-to-core re-mapping scheme that is capable of achieving optimal performance for multidimensional broadcast operations. The proposed multicore-aware process-to-core re-mapping scheme improves the performance of multidimensional broadcast operations by up to 64% over the default mapping scheme on the world's current eighth fastest supercomputer, Kraken, at the Oak Ridge National Laboratory.

The probability that a failure will occur before the end of the computation increases as the number of processors used in a high performance computing application increases. For long running applications using a large number of processors, it is essential that fault tolerance be used to prevent a total loss of all finished computations after a failure. While checkpointing has been very useful to tolerate failures for a long time, it often introduces a considerable overhead especially when applications modify a large amount of memory between checkpoints and the number of processors is large. In this paper, we propose an algorithm-based recovery scheme for the High Performance Linpack benchmark (which modifies a large amount of memory in each iteration) to tolerate fail-stop failures without checkpointing. It was proved by Huang and Abraham that a checksum added to a matrix will be maintained after the matrix is factored. We demonstrate that, for the right-looking LU factorization algorithm, the checksum is maintained at each step of the computation. Based on this checksum relationship maintained at each step in the middle of the computation, we demonstrate that fail-stop process failures in High Performance Linpack can be tolerated without checkpointing. Because no periodical checkpoint is necessary during computation and no roll-back is necessary during recovery, the proposed recovery scheme is highly scalable and has a good potential to scale to extreme scale computing and beyond. Experimental results on the supercomputer Jaguar demonstrate that the fault tolerance overhead introduced by the proposed recovery scheme is negligible.

Commercial graphics processing units (GPUs) prove their attractive, inexpensive in high performance scientific applications. However, a recent research through Folding@home demonstrates that two-thirds of tested GPUs on Folding@home exhibit a detectable, pattern-sensitive rate of memory soft errors for GPGPU. Fault tolerance has been viewed as critical to the effective use of these GPUs. In this paper, we present an on-line GPU error detection, location, and correction method to incorporate fault tolerance into matrix multiplication. The main contribution of the paper is to extend the traditional algorithm-based fault tolerance (ABFT) from offline to online and apply it to matrix multiplication on GPUs. The proposed on-line fault tolerance mechanism detects soft errors in the middle of the computation so that better reliability can be achieved by correcting corrupted computations in time. Experimental results demonstrate that the proposed method is highly efficient.

Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems-tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.