Design and Evaluation of a Wireless Sensor Network Based Aircraft Strength Testing System

The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system

Robust data storage in a network of computer systems

PhD ThesisRobustness of data in this thesis is taken to mean reliable storage of data and also high availability of data .objects in spite of the occurrence of faults. Algorithms and data structures which can be used to provide such robustness in the presence of various disk, processor and communication network failures are described. Reliable storage of data at individual nodes in a network of computer systems is based on the use of a stable storage mechanism combined with strategies which are used to help ensure crash resis- tance of file operations in spite of the use of buffering mechan- isms by operating systems. High availability of data in the net- work is maintained by replicating data on different computers and mutual consistency between replicas is ensured in spite of network partitioning. A stable storage system which provides atomicity for more complex data structures instead of the usual fixed size page has been designed and implemented and its performance evaluated. A crash resistant file system has also been implemented and evaluated. Many of the techniques presented here are used in the design of what we call CRES (Crash-resistant, Replicated and Stable) storage. CRES storage provides fault tolerance facilities for various disk and processor faults. It also provides fault tolerance facilities for network partitioning through the provision of an algorithm for the update and merge of a partitioned data storage system

Shuttle Ku-band and S-band communications implementation study

Various aspects of the shuttle orbiter S-band network communication system, the S-band payload communication system, and the Ku-band communication system are considered. A method is proposed for obtaining more accurate S-band antenna patterns of the actual shuttle orbiter vehicle during flight because the preliminary antenna patterns using mock-ups are not realistic that they do not include the effects of additional appendages such as wings and tail structures. The Ku-band communication system is discussed especially the TDRS antenna pointing accuracy with respect to the orbiter and the modifications required and resulting performance characteristics of the convolutionally encoded high data rate return link to maintain bit synchronizer lock on the ground. The TDRS user constraints on data bit clock jitter and data asymmetry on unbalanced QPSK with noisy phase references are included. The S-band payload communication system study is outlined including the advantages and experimental results of a peak regulator design built and evaluated by Axiomatrix for the bent-pipe link versus the existing RMS-type regulator. The nominal sweep rate for the deep-space transponder of 250 Hz/s, and effects of phase noise on the performance of a communication system are analyzed

A memory ann computing structure for nonlinear systems emulation identification

Currently, almost all efforts for using artificial neural networks for control oriented process identification are based on feed-forward networks. Provided the system order or the upper limit of the order is known, a neural network design is feasible for which all the collection of previous values of the inputs and outputs of the system to be identified can be used as input data to train in the network computing structures to learn the input-output map. This work reports on a novel technique that makes use of memory artificial neural network architecture that can learn and transform so as to emulate any non-linear input-output map for multi-input-multi-output systems when no prior knowledge on specific system features exists

Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

Scalable Probabilistic Model Selection for Network Representation Learning in Biological Network Inference

A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. Although the biological networks not only provide an elegant theoretical framework but also offer a mathematical foundation to analyze, understand, and learn from complex biological systems, the reconstruction of biological networks is an important and unsolved problem. Current biological networks are noisy, sparse and incomplete, limiting the ability to create a holistic view of the biological reconstructions and thus fail to provide a system-level understanding of the biological phenomena. Experimental identification of missing interactions is both time-consuming and expensive. Recent advancements in high-throughput data generation and significant improvement in computational power have led to novel computational methods to predict missing interactions. However, these methods still suffer from several unresolved challenges. It is challenging to extract information about interactions and incorporate that information into the computational model. Furthermore, the biological data are not only heterogeneous but also high-dimensional and sparse presenting the difficulty of modeling from indirect measurements. The heterogeneous nature and sparsity of biological data pose significant challenges to the design of deep neural network structures which use essentially either empirical or heuristic model selection methods. These unscalable methods heavily rely on expertise and experimentation, which is a time-consuming and error-prone process and are prone to overfitting. Furthermore, the complex deep networks tend to be poorly calibrated with high confidence on incorrect predictions. In this dissertation, we describe novel algorithms that address these challenges. In Part I, we design novel neural network structures to learn representation for biological entities and further expand the model to integrate heterogeneous biological data for biological interaction prediction. In part II, we develop a novel Bayesian model selection method to infer the most plausible network structures warranted by data. We demonstrate that our methods achieve the state-of-the-art performance on the tasks across various domains including interaction prediction. Experimental studies on various interaction networks show that our method makes accurate and calibrated predictions. Our novel probabilistic model selection approach enables the network structures to dynamically evolve to accommodate incrementally available data. In conclusion, we discuss the limitations and future directions for proposed works

Design And Evaluation of Flexibility-Based Structural Damage Localization Using Wireless Sensor Networks

The health of civil structures is very important and sometimes life-critical. While there are different ways to monitor their health, wireless sensor network: WSN) has the advantage of easy deployment and low cost, which make it feasible for most structures. We designed and implemented a system to localize damages on structures with a WSN by detecting the change in structure flexibility. This method has been validated to work well on bridges like a cantilever beam and a truss. It is also possible to be extended to other type of structures. Different from other systems, in network data processing was applied to lower the bandwidth requirement of large amount of raw sensing data. Only the intermediate computation results, that capture the flexibility related information, were transmitted back to the base station. We also divide the detection and localization into multiple levels. Lower level acts as the sentinel to detect the existence of damage; and higher levels, which consume more energy, are then triggered when necessary to get a higher resolution of localization. This design helps to further extend the lifetime of the system