Network coding has been widely used as a technology to ensure efficient and reliable communication. The ability to recode packets at the intermediate nodes is a major benefit of network coding implementations. This allows the intermediate nodes to choose a different code rate and fine-tune the outgoing transmission to the channel conditions, decoupling the requirement for the source node to compensate for cumulative losses over a multi-hop network. Block network coding solutions already have practical recoders but an on-the-fly recoder for sliding window network coding has not been studied in detail. In this paper, we present the implementation details of a practical recoder for sliding window network coding for the first time along with a comprehensive performance analysis of a multi-hop network using the recoder. The sliding window recoder ensures that the network performs closest to its capacity and that each node can use its outgoing links efficiently.

The network traffic prediction problem involves predicting characteristics of future network traffic from observations of past traffic. Network traffic prediction has a variety of applications including network monitoring, resource management, and threat detection. In this paper, we propose several Recurrent Neural Network (RNN) architectures (the standard RNN, Long Short Term Memory (LSTM) networks, and Gated Recurrent Units (GRU)) to solve the network traffic prediction problem. We analyze the performance of these models on three important problems in network traffic prediction: volume prediction, packet protocol prediction, and packet distribution prediction. We achieve state of the art results on the volume prediction problem on public datasets such as the GEANT and Abilene networks. We also believe this is the first work in the domain of protocol prediction and packet distribution prediction using RNN architectures. In this paper, we show that RNN architectures demonstrate promising results in all three of these domains in network traffic prediction, outperforming standard statistical forecasting models significantly.

Delay faults are frequently encountered in nanometer technologies. Therefore, it is critical to detect these faults during factory test. Testing for a delay fault requires the application of a pair of test vectors in an at-speed manner. To maximize the delay fault detection capability, it is desired that the vectors in this pair are independent. Independent vector pairs cannot always be applied to a circuit implemented with standard scan design approaches. However, this can be achieved by using enhanced scan flip-flops, which store two bits of data. This paper has two contributions. First, we develop a pulsed flip-flop (PFF) design. Second, we present an enhanced scan flipflop design, based on our PFF circuit. We have compared the performance of our pulse based flip-flop with recently published pulse based flip-flop designs, as well as a traditional master-slave D flip-flop. Our PFF shows significant improvements in power and timing compared to the other designs. Our pulse based enhanced scan flip-flop (PESFF) has 13% lower power dissipation and 26% better timing than a conventional D flipflop based enhanced scan flip-flop (DESFF). The layout area of our PESFF is 5.2% smaller than the DESFF. Monte Carlo simulations demonstrate that our design is more robust to process variations than the DESFF.

This paper investigates the the feasibility of using visual and infrared imaging sensors to aid in the location of the aircraft position during operations such as landing in bad weather. The choice of the airport model used is crucial to algorithms which are used for position estimation based on pattern recognition. In this paper we describe the effects the choice of a model has on the behaviour of such matching algorithms. Three basic models are chosen: a line segment based model, an area based model and a texture based model. It is seen that a sparse line segment based model is not adequate to identify the runway since it matches a number of false artifacts in the image. An enhanced line segment based model containing a large number of features compares favourably with the area based model. The texture based model is seen to need a number of camera and weather dependent parameters and the performance of such a scheme is not seen to be substantially better. Thus either a proper area based model or a pseudo-area based model (based on a very large number of line features) can be seen to provide the best performance for such landmark identification and position determination algorithms.>

This work studies the performance of dimensional least mean square (TDLMS) adaptive filters as prewhitening filters for the detection of small objects in image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter of much larger spatial extent. The correlated clutter is predicted and subtracted from the input signal, leaving components of the spatially small signal in the residual output. The receiver operating characteristics of a detection system augmented by a TDLMS prewhitening filter are plotted using Monte-Carlo techniques. It is shown that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter. For very low signal-to-background ratios, TDLMS-based detection systems show a considerable reduction in the number of false alarms. The output energy in both the residual and prediction channels of such filters is shown to be dependent on the correlation length of the various components in the input signal. False alarm reduction and detection gains obtained by using this detection scheme on thermal infrared sensor data with known object positions is presented.

A recursive detection scheme for point targets in infrared images is described. Estimation of the background noise is done using a weighted autocorrelation matrix update method and the detection statistic is calculated using a recursive technique. A weighting factor allows the algorithm to have finite memory and deal with nonstationary noise characteristics. The detection statistic is created by using a matched filter for colored noise, using the estimated noise autocorrelation matrix. The relationship between the weighting factor, the nonstationarity of the noise and the probability of detection is described. Some results on one- and two-dimensional infrared images are presented.>

The use of adaptive filters for the enhancement of images is studied. In particular, the enhancement of images where the region of interest has a small spatial extent compared to the noise is considered. A two stage approach for enhancing the desired signal is presented. At each stage, a two dimensional adaptive filter employing the least mean square (LMS) algorithm is used. By properly choosing the adaptation step size, in the first stage the colored noise is whitened, and in the second stage the desired signal is recovered from the white noise. Procedures for selecting the step size are discussed. Computer simulations to support the results are also presented.>