A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Light microscopy of Pakistani Berberis leaf cuticles and its taxonomic implications

Taxonomy of the genus Berberis is quite complex, due to overlapping morphological characters, making it very difficult to differentiate the species within the genus. In order to resolve this taxonomic complexity, the foliar anatomy of 10 Berberis L. species was carried out, for the first time from Pakistan, using light microscopy (LM). Significant variation in terms of epidermal cells shape, size, cell wall pattern, and stomata type was observed. B. baluchistanica has the largest epidermal cells, Adaxial: length = 45–(53.9 ± 3.6)–62.5 μm; and width = 22.5–(26.3 ± 1.3)–30 μm; Abaxial: length = 37.5–(43.25 ± 2.5)–50 μm; and width = 20–(22.6 ± 0.8)–25. The highest number of stomata was observed in B. glaucocarpa as 62 on the abaxial surface while the lowest number of stomata was recorded in B. baluchistanica as 8 on the adaxial surface. Of 10 investigated species, 6 possess anomocytic type stomata, while 2 species that is, B. aitchisonii and B. parkeriana have both anomocytic and anisocytic stomata while B. baluchistanica and B. calliobotrys have only paracytic type stomata. The highest number of cells per unit area was present on the adaxial surface of B. calliobotrys ranging from 245–(252.4)–260 followed by B. parkeriana with 209–(227.8)–250 on the abaxial surface. Stomatal index (SI) also varied considerably and was the lowest (2.6) percentage in B. baluchistanica and highest (31.9) percentage in B. kunawurensis. A taxonomic key based on micro‐morphological characters is provided for species identification.     

Micromorphology,pharmacognosy, and bio‐elemental analysis of an important medicinal herb: Verbascum thapsus L.

The present study was conducted on characterization of morpho‐anatomical, phytochemical, and bio‐elemental analysis of root, stem, and leaf of Verbascum thapsus. Morphologically Verbascum is a biennial plant that flowers for a month and a half in mid‐ to late summer. Various organoleptic features of root, leaf, and stem were recorded. Anatomically the T. S of the root, stem, and leaf showed a typical dicot histological differentiation. Leaf possessed anomocytic stomata, crescent shape vascular bundles, and covered with long and stellate type trichomes while, stem contained collateral type of vascular bundles and a well‐developed pith to store phytochemicals responsible for various pharmacological activities. The powder drug study through scanning electron microscopy revealed the presence of various types of tissues. Branched, tree like and stellate trichomes in root and leaf help in absorption and reduce loss of water. These anatomical features are responsible for the survival of the plant as biennial. Four macro elements (Na, K, Ca, and Mg) and seven microelements (Cr, Cu, Fe, Mn, Ni, Zn, and Cd) and their concentrations in ppm were also studied using Atomic Absorption Spectroscopy. Phytochemical screening of methanolic extract showed existence of various secondary metabolites, while mucilage and anthraquinones was not detected. The present study helps to understand the taxonomic identification of the plant based on morpho‐anatomical features and throws the attention of the researchers to carry out the work for developing its various formulations, which can ultimately be beneficial for the human beings as well as animals.     

Evaluating the lucrative role of probiotics in the aquaculture using microscopic and biochemical techniques

The present study focused to elucidate the promising role of probiotics in the fish culture, by employing microscopic and biochemical parameters to rationalize the possible outcomes. After preliminary feeding trial histopathological analysis revealed clear and visible changes in the intestinal and liver cells of CTRL (control group) compared to probiotics supplemented group, the bacterial inoculations resulted in shorten and swelled villi, while liver kupffer cells increased many folds in the CTRL. Moreover, there was clear hypertrophy and lamellar fusion in the gills tissues examined under microscope. Furthermore, intriguing results were obtained, such as a substantial increase in the weight gain, specific growth rate (%BW/day), FCE%, survival%, and improved secretions of amylase, cellulase, and protease activities. Likewise, high crude protein, fats content, while low moisture and total Ash was noticed in EXP I (Probiotics supplemented) compared to CTRL. The microscopic and biochemical analysis disclosed the positive role of probiotics on the fish health, thus we suggest its supplementations in the fish diet.     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Zr‐Doped Indium Oxide (IZRO) Transparent Electrodes for Perovskite‐Based Tandem Solar Cells

Parasitic absorption in transparent electrodes is one of the main roadblocks to enabling power conversion efficiencies (PCEs) for perovskite‐based tandem solar cells beyond 30%. To reduce such losses and maximize light coupling, the broadband transparency of such electrodes should be improved, especially at the front of the device. Here, the excellent properties of Zr‐doped indium oxide (IZRO) transparent electrodes for such applications, with improved near‐infrared (NIR) response, compared to conventional tin‐doped indium oxide (ITO) electrodes, are shown. Optimized IZRO films feature a very high electron mobility (up to ≈77 cm² V^?1 s^?1), enabling highly infrared transparent films with a very low sheet resistance (≈18 Ω □^?1 for annealed 100 nm films). For devices, this translates in a parasitic absorption of only ≈5% for IZRO within the solar spectrum (250–2500 nm range), to be compared with ≈10% for commercial ITO. Fundamentally, it is found that the high conductivity of annealed IZRO films is directly linked to promoted crystallinity of the indium oxide (In₂O₃) films due to Zr‐doping. Overall, on a four‐terminal perovskite/silicon tandem device level, an absolute 3.5 mA cm^?2 short‐circuit current improvement in silicon bottom cells is obtained by replacing commercial ITO electrodes with IZRO, resulting in improving the PCE from 23.3% to 26.2%.     

Netted radar: Network communications design and optimisation

Networks of phased array radars are generally able to provide better counter stealth target detection and classification. Each radar sensor (or node) generates information which requires transmission to a central authority that is able to evaluate the information. This requires a communications network to be established to allow transmission of information to and from any node. Each radar node is limited by range and degree and relies on the formation of a multi-hop network to facilitate these transmissions.This paper presents a model whereby the radar beam itself is used in the formation of a multi-hop network. The phased array’s multi-functional nature allows rapid switching between communications and radar function. A model of how the communication system could operate is presented, and an evolutionary optimisation algorithm based upon the concept of Pareto optimality is used for the topological design of the network. Finally, a simulation environment is presented to show the simulated performance of the communication model and designed networks.