Challenges in Internet of Things Applications

IoT (Internet of Things) is a system where objects are embedded with sensor technology to interact with each other over a wireless communication medium to generate, exchange and transfer data without human interaction. Now Internet of Things(IoT) is getting so much popular than any other trending topic. Up to our best knowledge, it is the first study about vulnerabilities in the application of the Internet of Things. IoT is also getting industrialized using sensors in a simple work also for sake of ease of working and computation but no one thinks about its vulnerabilities and drawback which can affect in future and may result in a leak of private details breaking privacy of the whole world. In this paper, we will talk about all these vulnerabilities and drawbacks of the Internet of Things with a lot of examples

Comparison of the spatial QRS-T angle derived from digital ECGs recorded using conventional electrode placement with that derived from Mason-Likar electrode position

Background: The spatial QRS-T angle is ideally derived from orthogonal leads. We compared the spatial QRS-T angle derived from orthogonal leads reconstructed from digital 12-lead ECGs and from digital Holter ECGs recorded with the Mason-Likar (M-L) electrode positions. Methods and results: Orthogonal leads were constructed by the inverse Dower method and used to calculate spatial QRS-T angle by (1) a vector method and (2) a net amplitude method, in 100 volunteers. Spatial QRS-T angles from standard and M-L ECGs differed significantly (57° ± 18° vs 48° ± 20° respectively using net amplitude method and 53° ± 28° vs 48° ± 23° respectively by vector method; p < 0.001). Difference in amplitudes in leads V4–V6 was also observed between Holter and standard ECGs, probably due to a difference in electrical potential at the central terminal. Conclusion: Mean spatial QRS-T angles derived from standard and M-L lead systems differed by 5°–9°. Though statistically significant, these differences may not be clinically significant

A characterization of particular symmetric (0,1) matrices

AbstractA characterization of a class of symmetric (0, 1) matrices A such that AP is a symmetric matrix too, where P is a permutation matrix, is given, and an application to double coverings of graphs is considered

Occupational noise: auditory and non-auditory consequences

Occupational noise exposure accounts for approximately 16% of all disabling hearing losses, but the true value and societal costs may be grossly underestimated because current regulations only identify hearing impairments in the workplace if exposures result in audiometric threshold shifts within a limited frequency region. Research over the past several decades indicates that occupational noise exposures can cause other serious auditory deficits such as tinnitus, hyperacusis, extended high-frequency hearing loss, and poor speech perception in noise. Beyond the audiogram, there is growing awareness that hearing loss is a significant risk factor for other debilitating and potentially life-threatening disorders such as cardiovascular disease and dementia. This review discusses some of the shortcomings and limitations of current noise regulations in the United States and Europe

Dynamic changes in synaptic plasticity genes in ipsilateral and contralateral inferior colliculus following unilateral noise-induced hearing loss

Unilateral noise-induced hearing loss reduces the input to the central auditory pathway disrupting the excitatory and inhibitory inputs to the inferior colliculus (IC), an important binaural processing center. Little is known about the compensatory synaptic changes that occur in the IC as a consequence of unilateral noise-induced hearing loss. To address this issue, Sprague–Dawley rats underwent unilateral noise exposure resulting in severe unilateral hearing loss. IC tissues from the contralateral and ipsilateral IC were evaluated for acute (2-d) and chronic (28-d) changes in the expression of 84 synaptic plasticity genes on a PCR array. Arc and Egr1 genes were further visualized by in situ hybridization to validate the PCR results. None of the genes were upregulated, but many were downregulated post-exposure. At 2-d post-exposure, more than 75% of the genes were significantly downregulated in the contralateral IC, while only two were downregulated in the ipsilateral IC. Many of the downregulated genes were related to long-term depression, long-term potentiation, cell adhesion, immediate early genes, neural receptors and postsynaptic density. At 28-d post-exposure, the gene expression pattern was reversed with more than 85% of genes in the ipsilateral IC now downregulated. Most genes previously downregulated in the contralateral IC 2-d post-exposure had recovered; less than 15% remained downregulated. These time-dependent, asymmetric changes in synaptic plasticity gene expression could shed new light on the perceptual deficits associated with unilateral hearing loss and the dynamic structural and functional changes that occur in the IC days and months following unilateral noise-induced hearing loss

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field