Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.

Hyperspectral images (HSIs) provide rich spectral information that has contributed to the successful performance improvement of numerous computer vision and remote sensing tasks. However, it can only be achieved at the expense of images’ spatial resolution. HSI super-resolution (HSI-SR), thus, addresses this problem by fusing low-resolution (LR) HSI with the multispectral image (MSI) carrying much higher spatial resolution (HR). Existing HSI-SR approaches require the LR HSI and HR MSI to be well registered, and the reconstruction accuracy of the HR HSI relies heavily on the registration accuracy of different modalities. In this article, we propose an unregistered and unsupervised mutual Dirichlet-Net ($u^{2}$-MDN) to exploit the uncharted problem domain of HSI-SRwithout the requirement of multimodality registration. The success of this endeavor would largely facilitate the deployment of HSI-SR since registration requirement is difficult to satisfy in real-world sensing devices. The novelty of this work is threefold. First, to stabilize the fusion procedure of two unregistered modalities, the network is designed to extract spatial information and spectral information of two modalities with different dimensions through a shared encoder–decoder structure. Second, the mutual information (MI) is further adopted to capture the nonlinear statistical dependencies between the representations from two modalities (carrying spatial information) and their raw inputs. By maximizing the MI, spatial correlations between different modalities can be well characterized to further reduce the spectral distortion. We assume that the representations follow a similar Dirichlet distribution for their inherent sum-to-one and nonnegative properties. Third, a collaborative$l_{2,1}$-norm is employed as the reconstruction error instead of the more common$l_{2}$-norm to better preserve the spectral information. Extensive experimental results demonstrate the superior performance of$u^{2}$-MDN as compared to the state of the art.

Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor to maintain the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. In this letter, we proposed a deep fully convolutional neural network (FCN) model to extract automatically the refugee shelters/tents in the worldview-2 (WV-2) satellite images. In addition, we transferred knowledge in the pretrained VGG-16 model to improve the detection accuracy and network training convergence. We compared the proposed approach with the traditional spectral angle mapper (SAM) method, deep convolutional neural network (CNN) models, and the mask Region-based CNN (R-CNN) model. The experimental results show that the FCN model improved the overall accuracy by 4.49%, 3.54%, and 0.88% compared with the CNNs, SAM, and mask R-CNN models, and improved the precision by 34.61%, 41.99%, and 11.87%, respectively.

Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high spatial resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the upsampled MSI. Recent deep learning endeavors are mostly supervised assuming that the HR MSI is available, which is unrealistic especially for satellite images. Nonetheless, these methods could not fully exploit the rich spectral characteristics in the MSI. Due to the wide existence of mixed pixels in satellite images where each pixel tends to cover more than one constituent material, pansharpening at the subpixel level becomes essential. In this article, we propose an unsupervised pansharpening (UP) method in a deep-learning framework to address the abovementioned challenges based on the self-attention mechanism (SAM), referred to as UP-SAM. The contribution of this article is threefold. First, the SAM is proposed where the spatial varying detail extraction and injection functions are estimated according to the attention representations indicating spectral characteristics of the MSI with subpixel accuracy. Second, such attention representations are derived from mixed pixels with the proposed stacked attention network powered with a stick-breaking structure to meet the physical constraints of mixed pixel formulations. Third, the detail extraction and injection functions are spatial varying based on the attention representations, which largely improves the reconstruction accuracy. Extensive experimental results demonstrate that the proposed approach is able to reconstruct sharper MSI of different types, with more details and less spectral distortion compared with the state-of-the-art.

Abstract Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty detection methods have been explored in prior work for three-channel color images and non-image datasets, but few have considered multispectral or hyperspectral image datasets for the purpose of scientific discovery. We compared the performance of four novelty detection methods—Reed Xiaoli (RX) detectors, principal component analysis (PCA), autoencoders, and generative adversarial networks (GANs)—and the ability of each method to provide explanatory visualizations to help scientists understand and trust predictions made by the system. We show that pixel-wise RX and autoencoders trained with structural similarity (SSIM) loss can detect morphological novelties that are not detected by PCA, GANs, and mean squared error autoencoders, but that the latter methods are better suited for detecting spectral novelties—i.e., the best method for a given setting depends on the type of novelties that are sought. Additionally, we find that autoencoders provide the most useful explanatory visualizations for enabling users to understand and trust model detections, and that existing GAN approaches to novelty detection may be limited in this respect.

In this work, we present a system based on convolutional autoencoders for detecting novel features in multispectral images. We introduce SAMMIE: Selections based on Autoencoder Modeling of Multispectral Image Expectations. Previous work using autoencoders employed the scalar reconstruction error to classify new images as novel or typical. We show that a spatial-spectral error map can enable both accurate classification of novelty in multispectral images as well as human-comprehensible explanations of the detection. We apply our methodology to the detection of novel geologic features in multispectral images of the Martian surface collected by the Mastcam imaging system on the Mars Science Laboratory Curiosity rover.

Public digital media can often mix factual information with fake scientific news, which is typically difficult to pinpoint, especially for non-professionals. These scientific news articles create illusions and misconceptions, thus ultimately influence the public opinion, with serious consequences at a broader social scale. Yet, existing solutions aiming at automatically verifying the credibility of news articles are still unsatisfactory. We propose to verify scientific news by retrieving and analyzing its most relevant source papers from an academic digital library (DL), e.g., arXiv. Instead of querying keywords or regular named entities extracted from news articles, we query domain knowledge entities (DKEs) extracted from the text. By querying each DKE, we retrieve a list of candidate scholarly papers. We then design a function to rank them and select the most relevant scholarly paper. After exploring various representations, experiments indicate that the term frequency-inverse document frequency (TF-IDF) representation with cosine similarity outperforms baseline models based on word embedding. This result demonstrates the efficacy of using DKEs to retrieve scientific papers which are relevant to a specific news article. It also indicates that word embedding may not be the best document representation for domain specific document retrieval tasks. Our method is fully automated and can be effectively applied to facilitating fake and misinformed news detection across many scientific domains.

Soil can be used as a damage indicator of landslides and flooding, which expose soil from vegetation canopy. It can also be used as an indirect indicator of illegal tunnel digging activity. This letter presents a sparsity-based approach to soil detection using multispectral satellite images, where both original and synthetic bands have been used. Spatial and spectral information has then been jointly used in soil detection. Extensive experiments clearly demonstrated the feasibility of our approach.

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.

We propose a high performance compression system with error resilience for sensor networks.

Opus, Advanced Audio Coding (AAC), and mp3 are well known audio codecs in the market. The goal of this research is to compare the quality of above three audio codecs at different bitrates and to determine which codec can maintain the best quality overall. An objective evaluation approach is presented and preliminary results are summarized.

This paper summarizes the development of a high performance video codec with error concealment.

A novel and hybrid data compression framework is proposed to compress wind tunnel data. Both lossless and lossy compression can be performed.

An automatic landing site selection algorithm generates potential landing sites for unmanned air vehicles (UAVs) with engine failures. One important step in the landing site selection algorithm is surface type estimation. In this paper, we focus on distinguishing the following three surface types: grass/soil, tree, and inland water. Two approaches are presented. One is a conventional approach that combines Gabor features and a nonlinear classifier known as Support Vector Machine (SVM). Another one is a deep learning-based approach called SegNet. Extensive simulations showed that although both approaches achieved high performance, the Gabor/SVM approach yielded slightly better robustness with respect to illumination changes.

This paper focuses on path planning for fixed wing unmanned air vehicles (UAVs) with engine failures. The path is between a contingency waypoint (CP) and the initial approach fix (IAF) of a landing place. The mishap UAV is required to avoid passing through no-fly zones. The problem is very challenging due to engine failure and the presence of winds. We propose a new and systematic path planning approach to generating contingency paths from any contingency waypoints on the primary flight path to any IAFs. The inputs to our planning system comprise wind forecast, aircraft capability, primary flight path, and landing sites information. Wind is assumed to be steady within small segments in the contingency path. Moreover, remaining airtime and altitude at IAF can be estimated. Extensive simulations demonstrated the effectiveness of the proposed approach.

This paper presents an information-theoretically (unconditionally) secure approach to enhancing security of control and diagnostic applications. Unconditional (information-theoretic) security means that an attacker, even with infinite computational power, still cannot decrypt the data. Currently, only quantum key distribution (QKD) and the Kirchhoff-law-Johnson-noise (KLJN) schemes can offer unconditional security for the secure key generation/exchange, which is the difficult part to reach information-theoretic security. The key idea of this paper is to deploy the chip-integrable and equally (or more) secure KLJN approach to enhancing the security of control and diagnostic systems. We will present the high level architecture and three potential applications of our approach.

We propose to adapt an earlier unconditionally secure communication system for ground vehicular network to Unmanned Air Systems (UASs). First, one recommended change is to adopt IEEE 802.16d for long range wireless communications (up to 75 km) for drone to drone communications. Second, we propose to strengthen the security of UAS communications by incorporating unconditionally secure key exchanges between certification authorities (CA), which are air traffic control (ATC) centers, and ground communication centers (also can be called road side devices (RSDs)). The proposed key exchange scheme is known as the Kirchhoff Law Johnson Noise (KLJN) scheme, which is unconditionally secure. The KLJN provides equivalent or better security but with much lower cost as compared to the quantum key distribution (QKD). The exchanged keys will be used to create digital signatures for messages between UASs. Third, the digital signatures combined with certificates issued for each UAS by CAs will be used for authentication of messages, which will further improve the security between UASs.

Anomaly detection has been known to be a challenging problem due to the uncertainty of anomaly and the interference of noise. In this paper, we focus on anomaly detection in hyperspectral images (HSIs) and propose a novel detection algorithm based on spectral unmixing and dictionary-based low-rank decomposition. The innovation is threefold. First, due to the highly mixed nature of pixels in HSI data, instead of using the raw pixel directly for anomaly detection, the proposed algorithm applies spectral unmixing to obtain the abundance vectors and uses these vectors for anomaly detection. We show that the abundance vectors possess more distinctive features to identify anomaly from background. Second, to better represent the highly correlated background and the sparse anomaly, we construct a dictionary based on the mean shift clustering of the abundance vectors to improve both the discriminative and representative powers of the algorithm. Finally, a low-rank matrix decomposition method based on the constructed dictionary is proposed to encourage the coefficients of the dictionary, instead of the background itself, to be low rank, and the residual matrix to be sparse. Anomalies can then be extracted by summing up the columns of the residual matrix. The proposed algorithm is evaluated on both synthetic and real data sets. Experimental results show that the proposed approach constantly achieves high detection rate, while maintaining low false alarm rate regardless of the type of images tested.

Many remote sensing applications require a high-resolution hyperspectral image. However, resolutions of most hyperspectral imagers are limited to tens of meters. Existing resolution enhancement techniques either acquire additional multispectral band images or use a pan band image. The former poses hardware challenges, whereas the latter has limited performance. In this paper, we present a new resolution enhancement method that only requires a color image. Our approach integrates two newly developed techniques in the area: (1) A hybrid color mapping algorithm, and (2) A Plug-and-Play algorithm for single image super-resolution. Comprehensive experiments using real hyperspectral images are conducted to validate and evaluate the proposed method.

This paper presents a novel approach to fusing Thermal Emission Imaging System (THEMIS) and Thermal Emission Spectrometer (TES) satellite images, aiming to improve Mars surface characterization performance from orbit. Our approach includes proven registration and advanced pansharpening algorithms developed by us and others. Preliminary experiments show that the fusion approach is highly promising despite the extremely high resolution difference of THEMIS and TES (30 to 1). We also observed some potential issues that require further research.

This paper summarizes a new investigation of applying advanced pansharpening algorithms to enhance the images of the left imager in the Mastcam onboard the Curiosity rover, which landed on Mars in 2012. The various instruments on the rover have already made great contributions in the understanding of Mars. The goal of our research is to generate both high spatial and high spectral image cube by using the left and right Mastcam imagers. Eleven algorithms have been investigated using five objective performance metrics. Subjective evaluations have also been conducted. The image enhancement results are encouraging.

Pansharpening refers to the fusion of a high spatial resolution panchromatic image with high spectral resolution multispectral or hyperspectral images (MSI or HSI) to yield high resolution data in both spectral and spatial domains. It has been widely adopted as a primary preprocessing step for numerous applications. In this paper, we perform a literature survey of various pansharpening algorithms including the most advanced deep learning approaches for both multispectral and hyperspectral images. We further evaluate the effect of the resolution difference on anomaly detection. Synthetic multispectral and hyperspectral images are generated to evaluate the performance of anomaly detection on high resolution images. Eight state-of-the-art MSI and HSI pansharpening methods are compared in this paper. Experimental results show that, performing anomaly detection on high resolution images improves the detection rate, and at the mean time suppresses the false alarm rate.

WorldView 3 (WV-3) is the first commercially deployed super-spectral, very high-resolution (HR) satellite. However, the resolution of the short-wave infrared (SWIR) bands is much lower than that of the other bands. In this letter, we describe four different approaches, which are combinations of pansharpening and hypersharpening methods, to generate HR SWIR images. Since there are no ground truth HR SWIR images, we also propose a new picture quality predictor to assess hypersharpening performance, without the need for reference images. We describe extensive experiments using actual WV-3 images that demonstrate that some approaches can yield better performance than others, as measured by the proposed blind image quality assessment model of hypersharpened SWIR images.

Anomaly detection has been known to be a challenging, ill-posed problem due to the uncertainty of anomaly and the interference of noise. In this paper, we propose a novel low rank anomaly detection algorithm in hyperspectral images (HSI), where three components are involved. First, due to the highly mixed nature of pixels in HSI, instead of using the raw pixel directly for anomaly detection, the proposed algorithm applies spectral unmixing algorithms to obtain the abundance vectors and uses these vectors for anomaly detection. Second, for better classification, a dictionary is built based on the mean-shift clustering of the abundance vectors to better represent the highly-correlated background and the sparse anomaly. Finally, a low-rank matrix decomposition is proposed to encourage the sparse coefficients of the dictionary to be low-rank, and the residual matrix to be sparse. Anomalies can then be extracted by summing up the columns of the residual matrix. The proposed algorithm is evaluated on both synthetic and real datasets. Experimental results show that the proposed approach constantly achieves high detection rate while maintaining low false alarm rate regardless of the type of images tested.

The Reed-Xiaoli (RX) algorithm has been widely used as an anomaly detector for hyperspectral images. Recently, kernel RX (KRX) has been proven to yield high performance in anomaly detection and change detection. In this paper, we present a generalization of the KRX algorithm. The novel algorithm is called cluster KRX (CKRX), which becomes KRX under certain conditions. The key idea is to group background pixels into clusters and then apply a fast eigendecomposition algorithm to generate the anomaly detection index. Both global and local versions of CKRX have been implemented. Application to anomaly detection using actual hyperspectral images is included. In addition to anomaly detection, the CKRX algorithm has been integrated with other prediction algorithms for change detection. Spatially registered visible and near-infrared hyperspectral images collected from a tower-based geometry have been used in the anomaly and change detection studies. Receiver operating characteristics curves and actual computation times were used to compare different algorithms. It was demonstrated that CKRX has comparable detection performance as KRX, but with much lower computational requirements.

Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many material substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm for Hyperspectral Imagery. The HSI data cube is first modeled as a dense low-rank tensor plus a sparse tensor. Based on the obtained low-rank tensor, LTDD further decomposes the low-rank tensor using Tucker decomposition to extract the core tensor which is treated as the “support” of the anomaly spectral signatures. LTDD then adopts an unmixing approach to the reconstructed core tensor for anomaly detection. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.

Noisy environments seriously degrade the performance of speech recognition systems. Here we implement a high performance speech enhancement algorithm. Data from speech separation challenge were used to evaluate the method. It was observed that the enhanced speech significantly improved the recognition performance. In 2 out of 4 SNR cases, over 100% relative percentage improvements were achieved. Standalone software prototype has been developed and evaluated.

Conventional speaker identification and speech recognition algorithms cannot deal with noisy and multiple speaker environments. For example, IBM via Voice has low recognition rates if dictation is done in a noisy environment. In order to achieve high performance in speaker identification and speech recognition, we propose an integrated approach that takes every facet of the process into account. Here we summarize some preliminary results from the application of this integrated approach to robust speaker identification and speech recognition. A real-time stand-alone software prototype has been developed to evaluate the effectiveness of the approach.

Conventional speaker identification and speech recognition algorithms do not perform well if there are multiple speakers in the background. For high performance speaker identification and speech recognition applications in multiple speaker environments, a speech separation stage is essential. Here we summarize the implementation of three speech separation techniques. Advantages and disadvantages of each method are highlighted, as no single method can work under all situations. Stand-alone software prototypes for these methods have been developed and evaluated.

The strategy of data fusion has been applied in threat prediction and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called JDL data fusion model, which currently called DFIG model. Higher levels of the DFIG model call for prediction of future development and awareness of the development of a situation. It is known that Bayesian network is an insightful approach to determine optimal strategies against asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a highly innovative data-fusion framework for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. In particular, asymmetric and adaptive threats are detected and grouped by intelligent agent and hierarchical entity aggregation in level 2 and their intents are predicted by a decentralized Markov (stochastic) game model with deception in level 3. We have verified that our proposed algorithms are scalable, stable, and perform satisfactorily according to the situation awareness performance metric

Partial adaptation is often used to reduce the computation and improve tracking ability of an adaptive array. In some practical situations, the received signal to be processed contains some interferences whose characteristics are known. The previously proposed partially adaptive concentric ring array is not able to utilize the prior information of known interferences without sacrificing the number of degrees of freedom, which will cause higher steady state error and smaller number of interferences that can be cancelled. We propose in this paper an improved partially adaptive concentric ring array that can utilize the prior knowledge to improve performance and maintain the same number of degrees of freedom. The proposed method designs the non-adaptive weights to remove the known interferences, and is shown to provide much faster convergence speed and lower steady state error than the original method.

In this paper, spectral unmixing methods, which are extensively used in hyperspectral imaging area, are proposed for classification and abundance fraction (concentration) estimation of chemical and biological agents that exist in the mixture form. Several government-furnished datasets, which were collected through the infrared spectrum method, were thoroughly analyzed. Two similarity measures-the spectral angle mapper and spectral information divergence-were investigated in order to provide a quantitative comparison basis with respect to the performance of the applied spectral unmixing methods in the existence of similar and distinct agents. The use of the similarity measures provided valuable information about the signature characteristics of the agents, which led to a better understanding about the capabilities of the investigated methods. The orthogonal subspace projection (OSP) method was investigated as the first unmixing, classification, and abundance estimation technique. It was observed that the OSP method provided good results when the number of agents in the database was small and was composed of distinct agents. However, when the number of agents was incremented by adding agents that share similar characteristics, the abundance estimation accuracy gradually degraded in addition to generating negative abundance fraction estimates. The second investigated unmixing method was called nonnegatively constrained least squares (NCLS). The results and analyses indicated that the NCLS method outperformed the OSP approach by providing considerably more accurate fraction estimates while at the same time not generating any negative fraction estimates; thus, the use of the NCLS method was found to be promising in detection and abundance fraction estimation of chemical and biological agents that exist in the form of mixtures. In addition, efficient implementation of NCLS has resulted in much lower computations than the conventional OSP implementation.

To minimize the number of birdstrikes, a common method is to use microphone arrays to monitor and identify dangerous birds near the airport or some critical locations in the airspace. However, it was recognized that the range of existing ground-based acoustic monitoring devices is only limited to a few hundred meters. Moreover, the bird classification performance in low signal-to-noise environments such as airports is not very satisfactory. This paper summarizes the development of a high performance bird classification system using a hidden Markov model (HMM) and Gaussian mixture model (GMM). Experimental results verified the classification performance.

The design of an adaptive concentric ring array for broadband beamforming is a challenge due to the large number of adapting coefficients. In this paper, the concept of setting the weights among different rings to enhance the beamformer output is used to design a partial adaptive ring array for applications in nonstationary signal environment. We decompose the weights of the array into two components: the weights for each receiving element within a ring and the weights for each ring. The partial adaptive array is obtained by setting the weight for each receiving element based on a priori knowledge of the direction of arrival (DOA) of the desired signal, and only the weights for each ring are adapted in real-time. The partial array design results in faster convergence rate, better performance and smaller amount of computation compared to a fully adaptive array. Experimental results demonstrate the advantage of the partial adaptive array design.

A texture analysis approach of using an improved texture feature coding method (TFCM) and the support vector machines (SVM) for target detection is presented. Preliminary tests on mammograms showed over 88% of normal mammograms and 85% of abnormal mammograms were correctly identified. Automatic target detection with a cascade-sliding-window (CSW) technique is also discussed.

Ship-motion prediction is very useful for several naval operations such as aircraft landing, cargo transfer, off-loading of small boats, and ship "mating" between a big transport ship and some small ships. The prediction information is extremely useful in sea states above 3. Five to ten seconds of ship motion prediction can give the operator ample time to avoid serious collisions. The paper summarizes the development of a high performance ship-motion prediction algorithm using minor component analysis (MCA). Simulation results show that this method can predict ship motion a long time ahead with consistent accuracy. That is, the prediction error is almost the same for the 5 second and 20 second predictions. Other conventional algorithms, such as neural networks (NN), autoregressive methods (AR), and Wiener prediction, were also studied for comparative purposes.

This paper summarizes the development of a high performance VOX prototype for use in a high noise environment (>=100 dB). Conventional VOX only operates well up to 90 dB. Experimental results verified the performance of the prototype.

In this paper we address the problem of speech acquisition using a concentric circular ring array with omnidirectional microphones. The goal of our design is to achieve a specified sidelobe level in the beam pattern. A previous work by Stearns et al. (1965) proposed a method to achieve low sidelobe level for a continuous concentric ring antenna. The method assumes a narrowband signal and uses continuous ring and therefore is not suitable for speech application. This paper generalizes Stearns' method to broadband signal acquisition in 3D using a discrete ring array. A compound ring structure is employed to reduce the number of rings involved. An example is given to demonstrate our design method. The proposed design method can be used to produce a nonadaptive beamformer with a certain desirable beam pattern, or to generate the weight constraint corresponding to the white-noise beam pattern in an adaptive beamformer.

A novel fault diagnostics and prognostics algorithm based on hidden Markov model (HMM) is proposed. The algorithm combines fault diagnostics and prognostics in a unified framework. The algorithm has been fully tested by using experimental data from a rotating shift testbed in our laboratory.

In this paper, we present a new robust control technique for induction motors using neural networks (NNs). The method is systematic and robust to parameter variations. Motivated by the well-known backstepping design technique, we first treat certain signals in the system as fictitious control inputs to a simpler subsystem. A two-layer NN is used in this stage to design the fictitious controller. Then we apply a second two-layer NN to robustly realize the fictitious NN signals designed in the previous step. A new tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. A main advantage of our method is that we do not require regression matrices, so that no preliminary dynamical analysis is needed. Another salient feature of our NN approach is that the off-line learning phase is not needed. Full state feedback is needed for implementation. Load torque and rotor resistance can be unknown but bounded.

A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme.

A robust neural-network (NN) controller is proposed for the motion control of rigid-link electrically driven (RLED) robots. Two-layer NN's are used to approximate two very complicated nonlinear functions. The main advantage of our approach is that the NN weights are tuned on-line, with no off-line learning phase required. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. When compared with standard adaptive robot controllers, we do not require lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of RLED robots without any modifications.