The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.

https://iopscience.iop.org/article/10.1088/0964-1726/25/5/053001/pdf

Guided wave based structural health monitoring: A review

Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.

https://scholar.harvard.edu/files/humayun/files/2014_rmbe_nucleisegmentation.pdf

Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

The visual inspection of bridges demands long inspection time and also makes it difficult to access all areas of the bridge. This paper presents a visual-based crack detection technique for the automatic inspection of bridges. The technique collects images from an aerial camera to identify the presence of damage to the structure. The images are captured without controlling angles or positioning of cameras so there is no need for calibration. This allows the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors. The images can detect cracks regardless of the size or the possibility of not being visible. The effectiveness of this technique can be used to successfully detect cracks near bolts.

Vision‐Based Automated Crack Detection for Bridge Inspection

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

/pdf/seizure-prediction-ready-for-a-new-era-g12ztouxh5.pdf

Seizure prediction - ready for a new era

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning, which affects performance and throughput; 2) the increased training complexity; and 3) the lack of rigirous guarantee of compression ratio and inference accuracy.To overcome these limitations, this paper proposes CirCNN, a principled approach to represent weights and process neural networks using block-circulant matrices. CirCNN utilizes the Fast Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) from $\mathrm {O}(n^{2})$ to $\mathrm {O}(n$ log n) and the storage complexity from $\mathrm {O}(n^{2})$ to O(n), with negligible accuracy loss. Compared to other approaches, CirCNN is distinct due to its mathematical rigor: the DNNs based on CirCNN can converge to the same “effectiveness” as DNNs without compression. We propose the CirCNN architecture, a universal DNN inference engine that can be implemented in various hardware/software platforms with configurable network architecture (e.g., layer type, size, scales, etc In CirCNN architecture: 1) Due to the recursive property, FFT can be used as the key computing kernel which ensures universal and small-footprint implementations. 2) The compressed but regular network structure avoids the pitfalls of the network pruning and facilitates high performance and throughput with highly pipelined and parallel design. To demonstrate the performance and energy efficiency, we test CIR-CNN in FPGA, ASIC and embedded processors. Our results show that CirCNN architecture achieves very high energy efficiency and performance with a small hardware footprint. Based on the FPGA implementation and ASIC synthesis results, CirCNN achieves 6 - 102X energy efficiency improvements compared with the best state-of-the-art results.CCS Concepts• Computer systems organization$\rightarrow $ Embedded hardware;

CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices

In this paper, a novel multimodal biometric recognition system using three modalities including face, ear and gait, based on Gabor+PCA feature extraction method with fusion at matching score level is proposed. The performance of our approach has been studied under three different normalization methods (min-max, median- MAD and z-score) and two different fusion methods (weighted sum and weighted product). Our new method has been successfully tested using 360 images corresponding to 120 subjects from three databases including ORL face database, USTB ear database, and CASIA gait database. Because of these biometric traits, our proposed method requires no significant user cooperation and also can work from a long distance. According to the experimental results our proposed method exhibits excellent recognition performance and outperforms unimodal systems. The best recognition performance that our proposed method achieved is %97.5.

Multimodal biometric system using face, ear and gait biometrics

This brief presents novel parallel pipelined architectures for the computation of the fast Fourier transform (FFT) of real signals and inverse FFT of Hermitian-symmetric signals using only real datapaths. The real FFT structure is transformed by transferring twiddle factors to subsequent stages, such that each stage in the proposed flow graph contains one column of butterfly units and one column of twiddle factor blocks, and each column of the flow graph contains only N samples. This is a key requirement for the design of architectures that are based on only real datapaths. This structure is then mapped to pipelined architectures. The proposed architectures can be used with any FFT size or level of parallelism, which is a power of two. A systematic method to design architectures for FFTs with different levels of parallelism and radix values is presented. By modifying the FFT flow graph for real-valued samples, this methodology leads to architectures with fewer adders, delays, and interconnections.

Pipelined Architectures for Real-Valued FFT and Hermitian-Symmetric IFFT With Real Datapaths

An inverse analysis using artificial intelligence based on the guided ultrasonic waves is proposed for effective identification of damage in thick steel beams for the purpose of structural health monitoring applications. Parameterized modeling for finite element analysis is applied to constitute the damage parameter database cost-effectively. For signal processing and feature extraction, wavelet transform is employed. A novel feature extraction technique, damage characteristic points, is applied to constitute the database for pattern recognition procedures. Using the extracted metrics, a multilayer feedforward artificial neural network under supervision of an error-backpropagation algorithm is developed and trained. The generalization performance of the artificial neural network has been examined experimentally. Results illustrate that the proposed metrics together with artificial neural network technique are powerful tools for effective identification of damage in the case of thick structures.

An effective damage identification approach in thick steel beams based on guided ultrasonic waves for structural health monitoring applications

Immuonohistochemically images of meningioma which are stained by ki67 marker contain positive and negative cells. Accurate counting the number of positive and negative cells in such images play a critical role in diagnosing diffrent type of meningioma cancer. Since pathological images of meningioma contain complex cell cluster accurate cell counting methodology is a major challenge for pathologist physicians. In this paper we provide a computer aided algorithm for detecting proliferative cells and mitosis index in immunohistochemically images of meningioma. In the first stage of the algorithm fuzzy c-means clustering was used to extract positive and negative cells based on CIElab color space. In the second stage, ultraerosion operation was applied to count the number of individual and overlapped cells. Experimental result show that the proposed algorithm is able to overcome some disadvantage of traditional approaches with acceptable accuracy by pathologist physicians.

Rasoul Amirfattahi

Papers

Multimodal biometric system using face, ear and gait biometrics

Pipelined Architectures for Real-Valued FFT and Hermitian-Symmetric IFFT With Real Datapaths

An effective damage identification approach in thick steel beams based on guided ultrasonic waves for structural health monitoring applications

Computer-aided detection of proliferative cells and mitosis index in immunohistichemically images of meningioma

Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions