The Internet of Things (IoT) being a promising technology the future is expected to connect billions devices. increased number communication generate mountains data and security can be threat. devices in architecture are essentially smaller size low powered. Conventional encryption algorithms generally computationally expensive due their complexity requires many rounds encrypt, wasting constrained energy gadgets. Less complex algorithm, however, may compromise desired integrity. In this...

In portable, 3-D, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high-quality images from a limited number radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, presence side lobe artifacts RF sub-sampling, standard beamformer often produces blurry with less contrast, which are unsuitable diagnostic purposes. Existing compressed sensing approaches require either hardware changes...

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio delay sum (DAS) beamformers. Unfortunately, performance these approaches degrades when underlying model is not sufficiently accurate number channels decreases. To address this problem, here, we propose a deep-learning-based beamformer generate significantly improved images over widely varying measurement conditions channel subsampling...

Information Security has become an important issue in modern world as the popularity and infiltration of internet commerce communication technologies emerged, making them a prospective medium to security threats. To surmount these threats data communications uses cryptography effective, efficient essential component for secure transmission information by implementing parameter counting Confidentiality, Authentication, accountability, accuracy. achieve different cryptographic algorithms...

Abstract Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (AFPs). Apart from being essential for various organisms sub-zero temperatures, AFPs have numerous applications different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST PSI-BLAST. Diverse found fishes (Type I, II, III, IV glycoproteins (AFGPs)), are sub-types show low sequence structural...

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences various AFPs exhibit a high degree heterogeneity, consequently prediction is considered be challenging task. this research, we propose handle arduous manifold learning task using notion localized processing. particular, an AFP sequence segmented into two sub-segments each which analyzed for amino acid di-peptide compositions. We...

Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate applicability US removal problems without matched reference data. Two types OT-CycleGAN employed: one with partial knowledge degradation physics and other...

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements cause problems that produce varying results. It raises concerns about the reproducibility Consequently, manual classification SERS spectrum requires carefully controlled experimentation further hinders large-scale adaptation. Recent advances machine learning offer decent opportunities to address these issues....

Cancer, with its complexity and numerous origins, continues to provide a huge challenge in medical research. Anticancer peptides are potential treatment option, but identifying synthesizing them on large scale requires accurate prediction algorithms. This study presents an intuitive classification strategy, named ACP-LSE, based representation learning, specifically, deep latent-space encoding scheme. ACP-LSE can demonstrate notable advancements outcomes, particularly scenarios limited sample...

Background: The extracellular matrix (ECM) is a dynamic, physiologically active component of all living tissues. It plays vital role in the functionality mutation ECM genes has shown to cause several diseases including cancer. A reliable prediction therefore prognostic significance. Keywords: Extracellular matrix, sparse representation, human proteome, protein prediction, mRMR feature selection, pattern classification.

In this paper, we propose an adaptive framework for the variable power of fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust FLMS (RVP-FLMS) dynamically adapts to achieve high convergence rate with low steady state error. For evaluation purpose, problems system identification and channel equalization are considered. experiments clearly show that approach achieves better lower steady-state error compared FLMS. MATLAB code related simulation is available...

Abstract A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional networks. The current generation network is equipped with multiple kernels which provide significant performance benefits compared to previous using only a single kernel. In existing multi-kernel RBF algorithms, formed by convex combination base/primary kernels. this paper, we propose novel RBFNN in every base kernel has its own (local)...

Recent proposals of deep learning-based beamformers for ultrasound imaging (US) have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, are versatile in that image post-processing algorithms can be readily combined. Unfortunately, with the existing technology, a large number need trained stored different probes, organs, depth ranges, operating frequency, desired target 'styles', demanding resources such training data,...

Simulators are the most dominant and eminent tool for analyzing investigating different type of networks. The simulations can be executed with less cost as compared to large scale experiment computational resources required if simulation model is carefully designed then it more practical than any well brought-up mathematical model. Generally P2P research based on principle simulate first in real world there no reason that results cannot reproducible. A lack standard documentation makes...