Computational models for integrating linguistic and visual information: A survey

This paper surveys research in developing computational models for integrating linguistic and visual information. It begins with a discussion of systems which have been actually implemented and continues with computationally motivated theories of human cognition. Since existing research spans several disciplines (e.g., natural language understanding, computer vision, knowledge representation), as well as several application areas, an important contribution of this paper is to categorize existing research based on inputs and objectives. Finally, some key issues related to integrating information from two such diverse sources are outlined and related to existing research. Throughout, the key issue addressed is the correspondence problem, namely how to associate visual events with words and vice versa.     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.     

A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification

This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.     

Segmented object manufacturing

Rapid Prototyping (RP) is a technology based on a “divide-and-conquer” strategy that enables automatic physical realization of a design without any special tooling. However, existing RP processes suffer from staircase defects since they are all based on 2.5-axis kinematics. To minimize the error due to staircase defects parts are normally built from very thin layers typically with thickness values of 0.010 to 0.300 mm. Therefore, hundreds of layers are required to produce a typical object making RP a slow and costly process. To overcome these limitations, a new RP process called Segmented Object Manufacturing (SOM) is proposed in this paper. SOM makes use of three-axis kinematics in conjunction with a novel slicing method. Slicing in SOM is based on certain visibility-based considerations and is independent of the part accuracy. Since only a few thick layers are used in the SOM technique, a part can be produced faster and cheaper with an accuracy comparable to that of CNC machining.     

Size and morphology-controlled synthesis of mesoporous hydroxyapatite nanocrystals by microwave-assisted hydrothermal method

We report the rapid microwave-assisted hydrothermal synthesis of mesoporous hydroxyapatite (HAp) nanocrystals with controlled size, morphology, and surface area using various organic modifiers as regulators. The products were analyzed for their crystalline nature, phase purity, morphology, particle size and pore size distribution. Results indicated that ascorbic acid, cetyltrimethyl ammonium bromide (CTAB) and polyvinylpyrrolidone (PVP) play an important role to obtain needle like, rod like and fiber like mesoporous HAp nanocrystals with different specific surface area by controlling growth habit of HAp along c-axis. In addition, the prepared samples were B-type carbonated HAp similar to bone minerals. Therefore, the present approach can be a promising way to obtain precursor for making tissue engineering scaffolds, drug/protein delivery carriers and bone fillers with tunable characteristics.     

Cytogenetic analysis of quinoa chromosomes using nanoscale imaging and spectroscopy techniques

Here we present a high-resolution chromosomal spectral map derived from synchrotron-based soft X-ray spectromicroscopy applied to quinoa species. The label-free characterization of quinoa metaphase chromosomes shows that it consists of organized substructures of DNA-protein complex. The analysis of spectra of chromosomes using the scanning transmission X-ray microscope (STXM) and its superposition of the pattern with the atomic force microscopy (AFM) and scanning electron microscopy (SEM) images proves that it is possible to precisely locate the gene loci and the DNA packaging inside the chromosomes. STXM has been successfully used to distinguish and quantify the DNA and protein components inside the quinoa chromosomes by visualizing the interphase at up to 30-nm spatial resolution. Our study represents the successful attempt of non-intrusive interrogation and integrating imaging techniques of chromosomes using synchrotron STXM and AFM techniques. The methodology developed for 3-D imaging of chromosomes with chemical specificity and temporal resolution will allow the nanoscale imaging tools to emerge from scientific research and development into broad practical applications such as gene loci tools and biomarker libraries.     

An intelligent railway surveillance framework based on recognition of object and railway track using deep learning

Multimedia Tools and Applications - In high speed railways, the intelligent railway safety system is necessary to avoid the accidents due to collision between trains and obstacles on the railway...     

Non-destructive characterization of pulse flours—A review

The consumption of plant-based proteins sourced from pulses is sustainable from the perspective of agriculture, environment, food security, and nutrition. Increased incorporation of high-quality pulse ingredients into foods such as pasta and baked goods is poised to produce refined food products to satisfy consumer demand. However, a better understanding of pulse milling processes is required to optimize the blending of pulse flours with wheat flour and other traditional ingredients. A thorough review of the state-of-the-art on pulse flour quality characterization reveals that research is required to elucidate the relationships between the micro- and nanoscale structures of these flours and their milling-dependent properties, such as hydration, starch and protein quality, components separation, and particle size distribution. With advances in synchrotron-enabled material characterization techniques, there exist a few options that have the potential to fill knowledge gaps. To this end, we conducted a comprehensive review of four high-resolution nondestructive techniques (i.e., scanning electron microscopy, synchrotron X-ray microtomography, synchrotron small-angle X-ray scattering, and Fourier-transformed infrared spectromicroscopy) and a comparison of their suitability for characterizing pulse flours. Our detailed synthesis of the literature concludes that a multimodal approach to fully characterize pulse flours will be vital to predicting their end-use suitability. A holistic characterization will help optimize and standardize the milling methods, pretreatments, and post-processing of pulse flours. Millers/processors will benefit by having a range of well-understood pulse flour fractions to incorporate into food formulations.     

Role of Zinc (Zn) in Human Reproduction: A Journey from Initial Spermatogenesis to Childbirth

Zinc (Zn), the second-most necessary trace element, is abundant in the human body. The human body lacks the capacity to store Zn; hence, the dietary intake of Zn is essential for various functions and metabolism. The uptake of Zn during its transport through the body is important for proper development of the three major accessory sex glands: the testis, epididymis, and prostate. It plays key roles in the initial stages of germ cell development and spermatogenesis, sperm cell development and maturation, ejaculation, liquefaction, the binding of spermatozoa and prostasomes, capacitation, and fertilization. The prostate releases more Zn into the seminal plasma during ejaculation, and it plays a significant role in sperm release and motility. During the maternal, labor, perinatal, and neonatal periods, the part of Zn is vital. The average dietary intake of Zn is in the range of 8–12 mg/day in developing countries during the maternal period. Globally, the dietary intake of Zn varies for pregnant and lactating mothers, but the average Zn intake is in the range of 9.6–11.2 mg/day. The absence of Zn and the consequences of this have been discussed using critical evidence. The events and functions of Zn related to successful fertilization have been summarized in detail. Briefly, our current review emphasizes the role of Zn at each stage of human reproduction, from the spermatogenesis process to childbirth. The role of Zn and its supplementation in in vitro fertilization (IVF) opens opportunities for future studies on reproductive biology.