In this paper, we are proposing a pilot-aided channel estimator for the multiple-input multiple-output (MIMO) directsequence code-division multiple access (DS-CDMA) communication systems based on the minimum mean-square error (MMSE) estimate of the attenuated transmitted signals in the presence of Rayleigh fading channel coupled with the white Gaussian noise. The proposed receiver consists of bank of matched filters matched to the signature waveform of the desired user followed by a nonlinear estimator, resulting from the MMSE estimate of the attenuated transmitted signal. Simulation results carried out for multiple values of signal to noise ratio (SNR) and number of users show that the theoretical predictions are very well substantiated.

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab–based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.

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The identification of an effective network which can efficiently meet the service requirements of the target, while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium. The wrong selection can contribute to unwanted situations like frustrated users, slow service, traffic congestion issues, missed and/or interrupted calls, and wastefulness of precious network components. Conventional schemes estimate the handoff need and cause the network screening process by a single metric. The strategies are not effective enough because traffic characteristics, user expectations, network terminology and other essential device metrics are not taken into account. This article describes an intelligent computing technique based on Multiple-Criteria Decision-Making (MCDM) approach developed based on integrated Fuzzy AHP-TOPSIS which ensures flexible usability and maximizes the experience of end-users in miscellaneous wireless settings. In different components the handover need is assessed and the desired network is chosen. Further, fuzzy sets provide effective solutions to address decision making problems where experts counter uncertainty to make a decision. The proposed research endeavor will support designers and developers to identify, select and prioritize best attributes for ensuring flexible usability in miscellaneous wireless settings. The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the usability and experience of end-users.