As a core component of mechanical connections, the preload state of bolts directly affects the reliability and safety of structures. The detection of bolt preload is an important engineering issue in the field of Structural Health Monitoring (SHM). Traditional detection methods have limitations such as dependence on external power supply, wired connection, inconvenient disassembly and assembly, which are not suitable for equipment like tower cranes that require frequent assembly and disassembly with many bolts distributed separately. To address these challenges, a passive wireless strain-sensing node based on Radio Frequency Identification (RFID) technology is proposed in this paper. The proposed sensing node has a potential to enable collaborative but independent preload detection for multiple bolts, thereby facilitating efficient integrity connection monitoring of connection components. By integrating a Wheatstone bridge strain measurement module, a hardware architecture comprising a tag antenna, a tag chip, a low-power micro-controller unit (MCU), and a piezoresistive sensing signal-reading chip is designed to form a tag sensing node. An adjacent-bridge-arm temperature-compensation strategy is also incorporated and studied to enhance practical reliability. Experiments show that at a reading distance of 0.60 m, the tag achieves a strain resolution of 7.01 με and good linearity. The distributed sensing experiments on cantilever beams demonstrate its ability to accurately capture strain distribution. For an M30 bolt, the resolutions of the bolt head and the bolt shank are 3.72 kN and 0.21 kN, respectively. The proposed sensor features cost-effective, passive, wireless, and easy to install in a non invasive manner, making it suitable for frequent disassembly scenarios such as tower cranes and providing a reliable solution for real-time monitoring of bolt connection status.

Current non-destructive tomography sensing systems, particularly in the domain of eddy current sensing, lack the capability to dynamically and intelligently optimize sensing parameters in response to time-varying environments. To address this limitation, we propose a hybrid tomography sensing system that integrates physical artificial intelligence (PAI) with an electromagnetic dynamics model for intelligent sensing. This system combines a physical recurrent neural network (PRNN) entities performed by the programmable planar coil arrays with its digital counterpart in a closed-loop configuration, facilitating forward inference and the backpropagation of errors respectively. Additionally, the system leverages physics-guided methods based on electromagnetic field dynamics and employs a combination of standard neural network training techniques to optimize the parameters of PRNN, enabling real-time adaptive optimization of sensing. Theoretical modelling of the PRNN has been rigorously conducted in this study. Furthermore, high-fidelity electromagnetic tomography (EMT) results for non-destructive testing are demonstrated, showcasing the potential of physics-guided analogue AI in EMT sensing. The results shows that the electromagnetic controlling system, optimized through physical AI approach, achieves higher precision results with lower complexity compared to standard digital implementations in eddy current testing. This also provides a novel potential way for optimizing PNNs, thereby enhancing the acceleration of physical AI and projecting diversity of physical information into analogue AI solver.

In the realm of electromagnetic nondestructive testing (NDT), accurately identifying and characterizing flaws within various materials is crucial for ensuring structural integrity. This article proposes a novel intelligent electromagnetic perception framework that combines physical and digital artificial intelligence to address the sensitivity and accuracy limitations inherent in conventional electromagnetic NDT. Unlike traditional passive data acquisition methods, the proposed system integrates a physical electromagnetic neural network and a physics-aware reinforcement learning algorithm to adaptively optimize electromagnetic field sensing parameters in real-time, significantly enhancing sensitivity in regions close to defects. On the digital side, a sensor-informed diffusion model reconstructs high-resolution images from low-resolution optimal sensitivity sensor data, allowing for detailed analysis of defect contours and depths. Experimental results demonstrate a maximum sensitivity improvement of 105.8% and a minimum defect quantification of 0.2 mm, exceeding the performance of established electromagnetic NDT techniques. This innovative framework combines adaptive electromagnetic field focusing with advanced image reconstruction, establishing a new benchmark in real-time, high-precision defect detection. In addition, it is offering valuable applications in pipeline inspection, aerospace, and automotive industries.

Visual surface defect detection plays a crucial role in industrial quality control. A series of deep learning based algorithms have been introduced into visual defect detection, achieving remarkable performance. However, these algorithms generally suffer from inadequate adaptability to the expanding of detection categories, which limits their detection capabilities when dealing with complex and dynamic real-world application scenarios. To address this issue, this paper proposes an innovative incremental defect detection model. This model is based on learnable feature fusion and dynamic category modeling, aiming to enhance the model’s online learning and adaptability to new defect categories. To effectively utilize the existing data and optimize the model training process, this study introduces a macrodata probability control strategy to guide the reasonable configuration of training data as well as the model. Furthermore, to improve the performance during the detection process, an interactive feedback mechanism is constructed. This mechanism provides effective data in real-time during the detection process, driving the model to undergo dynamic training and gradually enhancing its recognition and adaptability to newly emerging defect types. To verify the practicality and effectiveness of the proposed algorithm, comprehensive comparative experiments were conducted on multiple datasets and a real-world detection platform was established for validation. The experimental results demonstrate the superior performance and dynamic adaptability in practical applications.

Abstract During steel production, ensuring the integrity of the product’s surface is crucial for maintaining a competitive edge. Detecting surface flaws is a key component in preserving high standards of production quality, affecting both the end product’s reliability and the efficiency of the manufacturing process. Traditional methods for identifying these defects have relied heavily on manual inspection or basic computer vision techniques, both of which present a significant challenge in terms of accuracy and the health and safety of the inspectors. This paper explores an innovative approach by integrating a sequence generation model incorporating the transformer architecture, enhancing the detection of defects in the production of quality hot-rolled steel sheets. This method, which solves object detection through the lens of sequence generation, offers a more intricate analysis of images, enabling a comprehensive examination of surface anomalies along with detailed annotations regarding their nature and precise location. The approach not only proposes to increase the accuracy of defect detection but also highlights its adaptability for broader industrial uses.

In this study, we propose a new post processing algorithm, using a stable low-rank decomposed pseudo restored heat flux based on the ensemble variational Bayes tensor factorization (EVBTF-RPHF) algorithm for performing periodic square wave thermographic nondestructive testing (thermographic NDT). Previous studies have shown that both RPHF and EVBTF can separately improve the detectability of thermography by enhancing some defect features. However, both methods are limited by their particularly constraints: RPHF are heavily degraded by noises and missing data due to the assumptions under which the physical models are derived while efficiency of EVBT reduces when the lateral heat diffusion weights out. By embedding RPHF into the stable low-rank decomposition EVBTF, the proposed algorithm allows to improve the detectability of defects in thermographic NDT using a periodic heat flux with low-rank spatial distribution. The study verifies the capacity of the proposed method by theoretical analysis. Then, experiments were conducted on a carbon fiber composite panel with foreign inserts buried up to 5 mm deep. The sampled data are processed by the proposed method. The results are compared with existing methods such as phase-locked RPHF and EVBTF. The experimental results demonstrated that defects with normalized diameter-to-depth ratios as small as 0.9, barely detected with other available techniques, can reliably be detected by EVBTF-RPHF. The signal to noise ratio and the contrast are used as figure of merit to quantitatively compare the capacity of the proposed method with existing methods. However, the computation efficiency of the proposed algorithms needs further improvement.

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the tensor in a frequency domain, has been found useful in solving low-rank tensor recovery problems. Existing TNN-based methods use either fixed or data-independent transformations, which may not be the optimal choices for the given tensors. As the consequence, these methods cannot exploit the potential low-rank structure of tensor data adaptively. In this article, we propose a framework called self-adaptive learnable transform (SALT) to learn a transformation matrix from the given tensor. Specifically, SALT aims to learn a lossless transformation that induces a lower average-rank tensor, where the Schatten- p quasi-norm is used as the rank proxy. Then, because SALT is less sensitive to the orientation, we generalize SALT to other dimensions of tensor (SALTS), namely, learning three self-adaptive transformation matrices simultaneously from given tensor. SALTS is able to adaptively exploit the potential low-rank structures in all directions. We provide a unified optimization framework based on alternating direction multiplier method for SALTS model and theoretically prove the weak convergence property of the proposed algorithm. Experimental results in hyperspectral image (HSI), color video, magnetic resonance imaging (MRI), and COIL-20 datasets show that SALTS is much more accurate in tensor completion than existing methods. The demo code can be found at https://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.

As pipelines take an increasingly important role in energy transportation, their health management is necessary. In-pipe inspection is a common pipeline life maintenance method. The signal obtained through internal inspection contains strong noise and interference where the internal environment of the pipeline is extremely complicated. Thus, it is challenging to accurately identify the defect signal. In this article, a defect detection framework based on feature boosting is proposed by using the multisensing pipeline pig as the detection signals. Through boosting construction of features and hierarchical classification, the framework can not only correctly classify various signals in the internal detection signals but also realize the accurate identification of defect signals. Concurrently, in order to demonstrate the high flexibility and robustness of the detection framework, experiments, and verifications have been carried out on specimens in three different environments, i.e., 1) laboratory environment, 2) simulated environment, and 3) actual environment. In the classification of actual environmental detection signals, quantitative evaluation with different algorithms have been undertaken using the F-score to demonstrate the effectiveness of the proposed framework.

This article presents a novel semisupervised learning method for wearable sensors to recognize human activities. The proposed method is termed a tri-very fast decision tree (VFDT). The proposed method is a more efficient version of the Hoeffding tree and three VFDTs are generated from the original labeled example set and refined using unlabeled examples. Based on the heuristic growth characteristics of VFDT, a tri-training framework is proposed which uses unlabeled data to update the model without labeled data. This significantly reduces the computational time and storage of the data processing. In addition, the proposed method is embedded into wearable devices for online learning, while the test data flow is regarded as the unlabeled data to update the model. The experiment collects data stream of 16 min with motion state switching frequently while the wearable devices recognize motions in real time. An experimental comparison has also been undertaken for performance evaluation between the wearable and computation using a desktop computer. The obtained results show that only minor difference in terms of the$f1$-score rendered by the proposed method online or offline. This is a prominent characteristic for wearable computing within the Internet of Things (IoT). Data set can be linked ashttps://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.

From the sparsity of vector to the sparsity of singular values, which essentially characterizes the low rank property of matrix. The sparsity measure based model is of significant interest in a range of contemporary applications in data analysis. However, there are different measurement strategies for sparse characterization of high dimensional tensor data. Albeit, most of the existing sparsity measures only consider the number of non-zero factor components, but ignore the geometric position distribution structure of non-zero elements in high-dimensional space. In this paper, based on the fact that sliced sparse distribution of the core tensor, a novel high order structure sparsity measure is proposed. More specifically, the sparsity measure unifies Tucker and CP tensor decomposition into a framework for general tensor. The CP decomposition of the core tensor with factor group sparse constraint realizes modeling the global low CP rank and the sliced sparse distribution of the non-zeros elements of the core tensor simultaneously. We apply minimizing high order structure sparse measurement to multispectral image denoising and deduce Alternating Direction Method of Multipliers (ADMM) optimization method to solve the model effectively. The subsequent experimental results show that the proposed algorithm is competitive with state-of-the art denoising methods.1

With the increasing use of induction thermography (IT) for nondestructive testing in the mechanical and rail industry, it becomes necessary for the Manufacturers to rapidly and accurately monitor the health of specimens. The most general problem for IT detection is due to strong noise interference. In order to counter it, general postprocessing is carried out. However, due to the more complex nature of noise and irregular shape specimens, this task becomes difficult and challenging. In this article, a low-rank tensor with a sparse mixture of Gaussian (LRTSMoG) decomposition algorithm for natural crack detection is proposed. The proposed algorithm models jointly the LRST pattern by using a tensor decomposition framework. In particular, the weak natural crack information can be extracted from strong noise. Low-rank tensor based iterative sparse MoG noise modeling is carried out to enhance the weak natural crack information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted for natural crack detection on a variety of specimens. A comparative analysis is presented with general tensor decomposition algorithms. The algorithms are evaluated quantitatively based on signal-to-noise-ratio along with the visual comparative analysis.

Eddy current testing (ET) and eddy current thermography (ECT) are both important nondestructive testing methods that have been widely used in the field of conductive materials evaluation. Conventional ECT systems have often employed to test static specimens even though they are inefficient when the specimen is large. In addition, the requirement of high-power excitation sources tends to result in bulky detection systems. To mitigate these problems, in this article, a moving detection mode of multiphysics structured ET and ECT is proposed in which a novel L-shape ferrite magnetic yoke circumambulated with array coils is designed. The theoretical derivation model of the proposed method is developed which is shown to improve the detection efficiency without compromising the excitation current by ECT. The specimens can be speedily evaluated by scanning at a speed of 50-250 mm/s while reducing the power of the excitation current due to the supplement of ET. The unique design of the excitation-receiving structure has also enhanced the detectability of omnidirectional cracks. Moreover, it does not block the normal direction visual capture of the specimens. Both numerical simulations and experimental studies on different defects have been carried out and the obtained results have shown the reliability and detection efficiency of the proposed system.

This article proposes a hybrid multi-dimensional features fusion structure of spatial and temporal segmentation model for automated thermography defects detection. In addition, the newly designed attention block encourages local interaction among the neighboring pixels to recalibrate the feature maps adaptively. A Sequence-PCA layer is embedded in the network to provide enhanced semantic information. The final model results in a lightweight structure with smaller number of parameters and yet yields uncompromising performance after model compression. The proposed model allows better capture of the semantic information to improve the detection rate in an end-to-end procedure. Compared with current state-of-the-art deep semantic segmentation algorithms, the proposed model presents more accurate and robust results. In addition, the proposed attention module has led to improved performance on two classification tasks compared with other prevalent attention blocks. In order to verify the effectiveness and robustness of the proposed model, experimental studies have been carried out for defects detection on four different datasets. The demo code of the proposed method can be linked soon: http://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.

Long-term wellbeing monitoring is an underlying theme for evaluating health status by collecting physiological signs through behavioral traits. In alignment with Internet of Things (IoT), nonintrusive and trustworthy wearable social sensing technology holds a potential way for researchers to find and establish the interrelationships between unobtrusive social cues and physical mental health. This paper implements an IoT structured wearable social sensing platform with the integration of privacy audio feature, behavior monitoring, and environment sensing in a naturalistic environment. Particularly, four privacy protected audio-wellbeing features are embedded into the platform to automatically evaluate speech information without preserving raw audio data. Four weeks of long-term monitoring experimental studies have been conducted. A series of well-being questionnaires in conjunction with a group of students are engaged to objectively investigate the relationships between physical and mental health by utilizing the feature fusion strategy from speech, behavioral activities, and ambient factors.

With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP during the manufacturing process as well as the in-service procedure. The most common types of defects in the CFRP are debonds and delaminations. It is difficult to detect the inner defects on a complex-shaped specimen using conventional nondestructive testing (NDT) methods. In this paper, an unsupervised machine learning method based on wavelet-integrated alternating sparse dictionary matrix decomposition is proposed to extract the weaker and deeper defect information for CFRP by using the optical pulse thermography (OPT) system. We propose to model the low-rank and sparse decomposition jointly in an alternating manner. By incorporating the low-rank information into the sparse matrix and vice versa, the weaker defects will be more efficiently extracted from noise and background. In addition, the integration of wavelet analysis with dictionary factorization enables an efficient time-frequency mining of information and significantly removes the high frequency noise as well as boosts the speed of computations. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular- and irregular-shaped CFRP specimens. A comparative analysis has also been undertaken to study the proposed method against the general OPTNDT methods. The MATLAB demo code can be linked: http://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.

This paper presents an adaptive heartrate-dependent heartwave-signal-based biometric identification. A reliable and continuous heartwave extraction method featuring the hybridized discrete waveform transform method with heartrate adaptive QT and PR intervals to perform comprehensive heartwave features extractions on more than 35 000 heartwave signal. The size of training data was determined and the hybridized Gaussian-mixture-model-hidden-Markov-model classification method was used in the classification. Dynamic thresholding criterial incorporating user-specific scores and heartrate were adopted. The identification process using dynamic thresholding criterial achieved a remarkable receiver operating characteristic of 0.89 in true positive rate and an equal error rate of 0.11.

This paper presents a heartwave based authentication method that utilizes an ensemble of deep belief networks (DBNs) under different parameters to increase the reliability of feature extraction. The multiview outputs are further embedded into a single view before inputting into a stacked DBN for classification. The result of the proposed novel architecture achieved a classification rate of 98.3% with 30% training data. Importantly, it is able to perform user classification using heartwave signals acquired under intense physical exercise where heart rate ranges from 50 bpm to as high as 180 bpm. Under extreme physical duress, the heartwave from an individual experiences extreme morphological variations that render conventional classification approaches nonapplicable.

A novel unsupervised sparse component extraction algorithm for diagnosing micro defects in thermography imaging system is presented. The approach is optimized under Variational Bayesian framework, which is fully automated and does not require manual selection of the parameters in the solution. An internal sub sparse grouping mechanism and adaptive fine-tuning have been built into the proposed algorithm to control the sparsity. The proposed method is used to automatically detect the micro defects on metals. Other contending defect feature extraction and sparse pattern extraction methods are employed for comparison. The algorithm has been shown to improve the detection precision of both artificial and natural cracks.

Internet of Things (IoT) is a new technology which offers enormous applications that make people’s lives more convenient and enhances cities’ development. In particular, smart healthcare applications in IoT have been receiving increasing attention for industrial and academic research. However, due to the sensitiveness of medical information, security and privacy issues in IoT healthcare systems are very important. Designing an efficient secure scheme with less computation time and energy consumption is a critical challenge in IoT healthcare systems. In this paper, a lightweight online/offline certificateless signature (L-OOCLS) is proposed, then a heterogeneous remote anonymous authentication protocol (HRAAP) is designed to enable remote wireless body area networks (WBANs) users to anonymously enjoy healthcare service based on the IoT applications. The proposed L-OOCLS scheme is proven secure in random oracle model and the proposed HRAAP can resist various types of attacks. Compared with the existing relevant schemes, the proposed HRAAP achieves less computation overhead as well as less power consumption on WBANs client. In addition, to nicely meet the application in the IoT, an application scenario is given.

This paper presents a novel computational intelligence vision sensing approach to estimate nutrient content in wheat leaves by analyzing color features of the leaves images captured on field with various lighting conditions. We propose the development of deep sparse extreme learning machines (DSELM) fusion and genetic algorithm (GA) to normalize plant images as well as to reduce color variability due to a variation of sunlight intensities. We also apply the DSELM in image segmentation to differentiate wheat leaves from a complex background. In this paper, four moments of color distribution of the leaves images (mean, variance, skewness, and kurtosis) are extracted and utilized as predictors in the nutrient estimation. We combine a number of DSELMs with committee machine and optimize them using the GA to estimate nitrogen content in wheat leaves. The results have shown the superiority of the proposed method in the term of quality and processing speed in all steps, i.e., color normalization, image segmentation, and nutrient prediction, as compared with other existing methods.

As a key problem for multisensor attitude determination, Wahba's problem has been studied for almost 50 years. Different from existing methods, this paper presents a novel linear approach to solve this problem. We name the proposed method the fast linear attitude estimator (FLAE) because it is faster than known representative algorithms. The original Wahba's problem is extracted to several 1-D equations based on quaternions. They are then investigated with pseudoinverse matrices establishing a linear solution to n-D equations, which are equivalent to the conventional Wahba's problem. To obtain the attitude quaternion in a robust manner, an eigenvalue-based solution is proposed. Symbolic solutions to the corresponding characteristic polynomial are derived, showing higher computation speed. Simulations are designed and conducted using test cases evaluated by several classical methods, e.g., Shuster's quaternion estimator, Markley's singular value decomposition method, Mortari's second estimator of the optimal quaternion, and some recent representative methods, e.g., Yang's analytical method and Riemannian manifold method. The results show that FLAE generates attitude estimates as accurate as that of several existing methods, but consumes much less computation time (about 50% of the known fastest algorithm). Also, to verify the feasibility in embedded application, an experiment on the accelerometer-magnetometer combination is carried out where the algorithms are compared via C++ programming language. An extreme case is finally studied, revealing a minor improvement that adds robustness to FLAE, inspired by Cheng et al.

Reciprocating impact load leads to plastic deformation on the surface of the kinematic chains in an aircraft brake system. As a result, this causes fatigue and various complex natural damages. Due to the complex surface conditions and the coexistence damages, it is extremely difficult to diagnose microcracks by using conventional thermography inspection methods. In this paper, the thermal pattern contrast method is proposed for weak thermal signal detection using eddy current pulsed thermography. In this process, the extraction and subsequent separation differentiate a maximum of the thermal spatial-transient pattern between defect and nondefect areas. Specifically, a successive optical flow is established to conduct a projection of the thermal diffusion. This directly gains the benefits of capturing the thermal propagation characteristics. It enables us to build the motion context connected between the local and the global thermal spatial pattern. Principal component analysis is constructed to further mine the spatial-transient patterns to enhance the detectability and sensitivity in microcrack detection. Finally, experimental studies have been conducted on an artificial crack in a steel sample and on natural fatigue cracks in aircraft brake components in order to validate the proposed method.

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

An unsupervised machine learning algorithm based on nonnegative matrix factor Two-dimensional deconvolution (NMF2D) with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update. As the number of parameters in the NMF2D grows exponentially the number of frequency basis increases linearly, the issues of model-order fitness, initialization, and parameters estimation become ever more critical. This paper proposes a variational Bayesian method to optimize the number of components in the NMF2D by using the Gamma-Exponential process as the observation-latent model. In addition, it is shown that the proposed Gamma-Exponential process can be used to initialize the NMF2D parameters. Finally, the paper investigates the issue and advantages of using different window length. Experimental results for the synthetic convolutive mixtures and live recordings verify the competence of the proposed algorithm.

This paper proposes an unsupervised method for diagnosing and monitoring defects in inductive thermography imaging system. The proposed method is fully automated and does not require manual selection from the user of the specific thermal frame images for defect diagnosis. The core of the method is a hybrid of physics-based inductive thermal mechanism with signal processing-based pattern extraction algorithm using sparse greedy-based principal component analysis (SGPCA). An internal functionality is built into the proposed algorithm to control the sparsity of SGPCA and to render better accuracy in sizing the defects. The proposed method is demonstrated on automatically diagnosing the defects on metals and the accuracy of sizing the defects. Experimental tests and comparisons with other methods have been conducted to verify the efficacy of the proposed method. Very promising results have been obtained where the performance of the proposed method is very near to human perception.

This paper proposes a new system for the unsupervised diagnostic and monitoring of defects in waveguide imaging. The proposed method is automatic and does not require manual selection of specific frequencies for defect diagnostics. The core of the method is a computational intelligent machine learning algorithm based on sparse non-negative matrix factorization. An internal functionality is built into the machine learning algorithm to adaptively learn and control the sparsity of the factorization, and to render better accuracy in detecting defects. This is achieved by using Bayesian statistics methodology. The proposed method is demonstrated on automatic detection of defect in metals. In addition, we show that the extraction of the spectrum signature corresponding to the defect is significantly more efficient with the proposed optimal sparsity, which subsequently led to better detection performance. Experimental tests and comparisons with other sparse factorization methods have been conducted to verify the efficacy of the proposed method.

A novel unsupervised machine learning algorithm for single channel source separation is presented. The proposed method is based on nonnegative matrix factorization, which is optimized under the framework of maximum a posteriori probability and Itakura-Saito divergence. The method enables a generalized criterion for variable sparseness to be imposed onto the solution and prior information to be explicitly incorporated through the basis vectors. In addition, the method is scale invariant where both low and high energy components of a signal are treated with equal importance. The proposed algorithm is a more complete and efficient approach for matrix factorization of signals that exhibit temporal dependency of the frequency patterns. Experimental tests have been conducted and compared with other algorithms to verify the efficiency of the proposed method.

Developing audio processing tools for extracting social-audio features are just as important as conscious content for determining human behavior. Psychologists speculate these features may have evolved as a way to establish hierarchy and group cohesion because they function as a subconscious discussion about relationships, resources, risks, and rewards. In this paper, we present the design, implementation, and deployment of a wearable computing platform capable of automatically extracting and analyzing social-audio signals. Unlike conventional research that concentrates on data which have been recorded under constrained conditions, our data were recorded in completely natural and unpredictable situations. In particular, we benchmarked a set of integrated algorithms (sound speech detection and classification, sound level meter calculation, voice and nonvoice segmentation, speaker segmentation, and prediction) to obtain speech and environmental sound social-audio signals using an in-house built wearable device. In addition, we derive a novel method that incorporates the recently published audio feature extraction technique based on power normalized cepstral coefficient and gap statistics for speaker segmentation and prediction. The performance of the proposed integrated platform is robust to natural and unpredictable situations. Experiments show that the method has successfully segmented natural speech with 89.6% accuracy.

The authors of the paper "Wearable Audio Monitoring: Content-Based Processing Methodology and Implementation" (ibid., vol. 44, no. 2, pp. 222-233) would like to acknowledge that the research reported was funded by the UK Medical Research Council grant "A monitoring device to objectively assess functional/psychosocial impairment in older-age adults with major depression" (G1001828/1).

A novel single-channel blind source separation (SCBSS) algorithm is presented. The proposed algorithm yields at least three benefits of the SCBSS solution: 1) resemblance of a stereo signal concept given by one microphone; 2) independent of initialization and a priori knowledge of the sources; and 3) it does not require iterative optimization. The separation process consists of two steps: 1) estimation of source characteristics, where the source signals are modeled by the autoregressive process and 2) construction of masks using only the single-channel mixture. A new pseudo-stereo mixture is formulated by weighting and time-shifting the original single-channel mixture. This creates an artificial mixing system whose parameters will be estimated through our proposed weighted complex 2-D histogram. In this paper, we derive the separability of the proposed mixture model. Conditions required for unique mask construction based on maximum likelihood are also identified. Finally, experimental testing on both synthetic and real-audio sources is conducted to verify that the proposed algorithm yields superior performance and is computationally very fast compared with existing methods.

A novel approach for adaptive regularization of 2-D nonnegative matrix factorization is presented. The proposed matrix factorization is developed under the framework of maximum a posteriori probability and is adaptively fine-tuned using the variational approach. The method enables: (1) a generalized criterion for variable sparseness to be imposed onto the solution; and (2) prior information to be explicitly incorporated into the basis features. The method is computationally efficient and has been demonstrated on two applications, that is, extracting features from image and separating single channel source mixture. In addition, it is shown that the basis features of an information-bearing matrix can be extracted more efficiently using the proposed regularized priors. Experimental tests have been rigorously conducted to verify the efficacy of the proposed method.

A novel approach to solve the single-channel source separation (SCSS) problem is presented. Most existing supervised SCSS methods resort exclusively to the independence waveform criteria as exemplified by training the prior information before the separation process. This poses a significant limiting factor to the applicability of these methods to real problem. Our proposed method does not require training knowledge for separating the mixture and it is based on decomposing the mixture into a series of oscillatory components termed as the intrinsic mode functions (IMFs). We show, in this paper, that the IMFs have several desirable properties unique to SCSS problem and how these properties can be advantaged to relax the constraints posed by the problem. In addition, we have derived a novel sparse non-negative matrix factorization to estimate the spectral bases and temporal codes of the sources. The proposed algorithm is a more complete and efficient approach to matrix factorization where a generalized criterion for variable sparseness is imposed onto the solution. Experimental testing has been conducted to show that the proposed method gives superior performance over other existing approaches.