Simple sound transmission loss measurements using a modified impedance tube technique

A new image feature extraction method for face recognition called Tri-direction 2D-Fisher Discriminant Analysis (T2D-FDA) is proposed to deal with the Small Sample Size (SSS) problem in conventional 1D-Fisher Discriminant Analysis (1D-FDA). Moreover, the essence of T2D-FDA is investigated, and the equivalence of the left-multiplying 2D-FDA of the original image matrices and the left-multiplying D2D-FDA of diagonal image matrices is verified if each column is viewed as a computational unit. Different from the 1D-LDA based approaches, the T2D-FDA is based on 2D image matrices rather than column vectors, so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist anymore because within-class scatter matrices constructed is full-rank in T2D-FDA. The proposed method is applied to face recognition where only few training images exist for each subject. Experiment results show T2D-FDA outperforms the current linear subspace methods in SSS problem on four public databases: ORL, Yale, AR and FERET face databases.

Linear Discriminant Analysis (LDA) has been one of the popular subspace methods for face recognition. But this method suffers from the small sample size (SSS) problem, also known as 'curse of dimensionality'. Various techniques have been proposed in literature to overcome this limitation. But it is still unclear which method provides the best solution to SSS problem. In this paper, we have investigated the performance of some popular subspace methods such as principal component analysis (PCA), PCA + LDA, LDA via QR decomposition, Null-space LDA, Exponential Discriminant Analysis (EDA), PCA+EDA etc. Extensive experiments have been performed on five publically available face datasets viz. AR, CMU-PIE, PIX, Yale and YaleB. The performance is measured in terms of average classification accuracy. Experimental results show that the performance increases with the increase in the number of images per person in training set irrespective of the datasets. There is no clear winner among the subspace methods under investigation. But, the performance of PCA+LDA and PCA+EDA is consistent in tackling SSS problem irrespective of the dataset and can also handle the illumination variation in face recognition.

Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user’s finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research.

We made two contributions in this paper. First, a new method called two-dimensional fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in LDA based face recognition. Second, appearance and depth information are combined to improve face recognition rate. Different from the conventional 1D-FDA (PCA plus LDA) approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. 2D FDA and 1D FDA (PCA plus LDA) are evaluated respectively with a problem that combines appearance and depth information for face recognition. We investigate the complete range of linear combinations to reveal the interplay between these two paradigms. The recognition rate by the combination is better than either appearance alone or depth alone. It is verified that 2D-FDA outperforms 1D FDA

Fall is a major threat to stroke survivors with the problems of gait and balance disorders in the rehabilitation phase following severe consequences on quality of life and a heavy burden to their families. Many solutions have been proposed to assess fall risk for elders based on inertial sensor-based signals, however, there still exists a great challenge of transferring them from elderly populations to the stroke-survivors populations as gait disorder patterns are significant difference between elders and stroke survivors. In this study, we conduct a pilot study to collect inertial sensor-based signals from stroke survivors when they performed the timed up and go test, and build an automatic fall risk assessment model with the architecture of Siamese network, with a merit of mitigating the problem of small sample size. Specifically, the proposed automatic fall risk assessment model consists of two parallel convolutional neural networks, each of which is composed of three convolutional layers, two max-pooling layers, and three fully connected layers. To utilize the space relation among accelerator-based and gyroscope-based signals, two-dimensional discrete wavelet transform extracts image-like features, wavelet coefficients, from inertial sensor-based signals as the input. Experimental results show that the proposed fall risk assessment model has achieved a promising results, which outperform cutting-edge methods with a big margin. The proposed fall risk assessment model with low computational complexity and limited memory consuming can be deployed on an embedded system to provide fall risk assessment service for stroke survivors in point-of-care environments or community settings.

Due to the noise accompanied with fault signals, it is challenging to identify the discriminant information and the local geometric feature from the complex fault data for enhancing fault diagnosis accuracy. To address this challenge, this work proposed an anti-noise algorithm based on locally linear embedding integrated with diffusion distance and maximum correntropy criterion (DMLLE). In DMLLE, diffusion distance was adopted instead of the Euclidean distance for neighborhood construction. Meanwhile, the optimal weights were updated to reveal local geometry information based on the loss function of the maximum correntropy criterion. Subsequently, DMLLE is eventually developed to further restrain noise embedding into raw signals and obtain low dimensional features. Furthermore, weighted extreme gradient boosting is used to map the low dimensional features to the types of faults, which easily implements fault pattern recognition. Finally, two synthetic manifold datasets and fault data acquired from the transmit/receive (T/R) module are used to validate the performance of the proposed diagnosis methodology. Compared with the existing methods, the proposed diagnosis methodology generates a smoother flow structure by preserving the local neighborhood of the dataset with noises and realizes a higher accuracy of 94.41% on the T/R module dataset, which outperforms 3%–9% better than other classification models. Therefore, it can be concluded that the proposed diagnosis methodology can effectively extract intrinsic fault features by weakening the influence aroused by noise and achieve superior accuracy in fault diagnosis by addressing the problem of small sample size.

Underwater image recognition is a very challenging task. To overcome the problems of small sample size and poor image quality, we propose a new deep support vector machine (DSVM) algorithm. The method reduced the feature dimensions and eliminated noise. Moreover, the different kernel functions in the network ensemble are conducive to adapting to the feature distribution of multiple types of targets. In this paper, we firstly proposed a new DSVM network. Then, we use ensemble and “one-to-one”(O-v-O) method to achieve multi-classification. Finally, to evaluate the effectiveness of the proposed method, we conducted verification experiments on three data sets. The experimental results show that the proposed DSVM algorithm can effectively avoid over-fitting and improve the recognition rate in the recognition of multiple types of underwater target images.KeywordsDSVMEnsemble learningMulti-class SVMUnderwater image recognition

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

Lymph node status is a major determinant of disease recurrence after patients undergo curative resection for gastric carcinoma. A proportion of patients without lymph node metastasis develop systemic recurrences. Recent studies in a range of solid tumors have found a high incidence of micrometastases in the regional lymph nodes of patients with apparently negative lymph nodes. In patients with breast and colorectal carcinoma, the presence of micrometastases has been associated with a poorer prognosis. In patients with gastric carcinoma, the significance of micrometastases in lymph nodes remains controversial. Most published reports on this subject suffer from the problems of small sample size and selection bias. One hundred seven patients with pathologic T2N0M0 (tumor invades muscularis propria or subserosa [T2], no regional lymph node metastasis [N0], and no distant metastasis [M0]; pT2N0M0) gastric carcinoma who underwent gastric resection between 1984 and 1990 at the National Cancer Center Hospital were studied. Two consecutive sections were newly prepared from each lymph node for hematoxylin and eosin staining and immunohistochemical staining (IHC) with antibody against cytokeratin. Associations between clinicopathologic factors and the presence of micrometastases as well as micrometastases and survival were sought. Micrometastases were identified in 38 of 107 patients (35.5%) and in 87 of 4484 lymph nodes (1.94%) by IHC. The incidence of micrometastases was significantly higher in patients with infiltrative tumors than in patients with expansive, growing tumors (P = 0.02). Other clinicopathologic findings had no statistically significant correlation with the incidence of micrometastases. The 5-year survival rates of patients with and without micrometastases were 94% and 89%, respectively. Similarly, the 10-year survival rates were 79% and 74%, respectively. The survival curves of patients with or without micrometastasis were nearly superimposed (P = 0.86). The presence of immunohistochemically detected micrometastases in the regional lymph nodes did not affect the survival of Japanese patients with pT2N0M0 gastric carcinoma who had undergone gastrectomy with D2 lymph node dissection.

Foreign direct investment (FDI) plays an extraordinary role in developing countries and its fluctuations reflect the changes of influencing factors during time, and therefore the models to simulate and forecast the trends are of great significance. Model GM (1, 1) is used in this paper to overcome the problems of small sample size and poor data. In the pre-procession of the raw data, two sequences are generated from weakening operator and from logarithm process respectively, and the simulation results show that the model constructed from the logarithm sequence has better simulation accuracy. Then Markov Chain is introduced to original model to get more accurate forecast results, which indicate the growing trend of FDI inflows in the following three years. The grey system models applied in economic areas are proved to have accurate simulation and convincing results.

The small sample size (SSS) problem is one of the major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern recognition tasks. Different methods have been proposed to solve this problem. In this paper, we introduce a new kernel discriminant analysis algorithm, which is able to effectively address the SSS problem and extract a set of optimal discriminant vectors without any lose of useful discriminant information. Experiments performed on radar target recognition using range profiles indicate that the proposed method outperforms some existing kernel discriminant algorithms, such as generalized discriminant analysis and kernel direct discriminant analysis, in terms of recognition rate.

Complexity-reduced implementations of complete and null-space-based linear discriminant analysis

When locality preserving projection (LPP) method was originally proposed, it takes as the LPP solution the minimum eigenvalue solution of an eigenequation. After that, LPP has been used for image recognition problems such as face recognition. However, almost no researcher realizes that LPP usually encounters several difficulties when applied to the image recognition problem. For example, since image recognition problems are usually small sample size (SSS) problems, the corresponding eigenequation cannot be directly solved. In addition, it seems that even if one can obtain the solution of the eigenequation by using the numerical analysis approach, the obtained conventional LPP solution might produce the `presentation confusion' problem for samples from different classes, which is disadvantageous for the classification to procedure a high accuracy. In this paper we first thoroughly investigate the characteristics and drawbacks of the conventional LPP solution in the small sample size (SSS) problem in which the sample number is smaller than the data dimension. In order to overcome these drawbacks, we propose a new LPP solution for the SSS problem, which has clear physical meaning and can be directly and easily worked out because it is generated from a non-singular eigenequation. Experimental results the proposed solution scheme can produce a much lower classification error rate than the conventional LPP solution.

This chapter shows the solutions of LDA for small sample-size (SSS) problems. We first give an overview on the existing LDA regularization techniques. Then, a unified framework for LDA and a combined LDA algorithm for SSS problem are described. Finally, we provide the experimental results and some conclusions.