Design and analysis of 0.9 and 2.3-GHz concurrent dual-band CMOS LNA for mobile communication

A complementary metal-oxide-semiconductor (CMOS) dual-band low-noise amplifier (LNA) for 2G/3G/4G mobile communications is presented. It operates at 0.9 and 2.3 GHz of frequencies. The dual-band operation is achieved by adding a modified notch-filtering path in the wideband LNA. The modified notch-filtering path does not require additional power to cancel the signals of the stop band frequency. The impact of the filtering path in the proposed LNA is analyzed. Improved results are observed in dual bands of frequency. Sustainability of the LNA under process corner variation and temperature variation are examined, and it is found to be suitable for the application. The proposed LNA is designed at 90-nm technology in Cadence Virtuoso with 0.5 and 0.6-V supply. The post-layout simulation shows 22 dB of gain (S₂₁), 2 dB of Noise Figure (NF), and −5.5 dBm of IIP3 at the high band. In the low band, 24 dB of S₂₁, 2.7 dB of NF, and −6.65 dBm of IIP3 are reached. The circuit consumes 5.2 mW of power and 0.0918 mm² of area. The efficiency of the LNA is estimated by the figure of merit, and comparable results are secured in the proposed work.     

Comparative Assessment of Trypsin Inhibitor vis-à-vis Kunitz Trypsin Inhibitor and Bowman-Birk Inhibitor Activities in Soybean

Trypsin inhibitor activity (TIA) in soybean is attributed to two polypeptides, namely, Kunitz trypsin inhibitor (KTI) and Bowman-Birk inhibitor (BBI). Standard spectrophotometric protocol widely followed for estimation of TIA is cumbersome and does not distinguish KTI from BBI. In the present investigation, extraction conditions for KTI were optimized and different forms of this polypeptide were resolved in 180 soybean genotypes of Indian and exotic origin through native PAGE. This led to the identification of three KTI alleles, namely, Ti^a, Ti^b, and Ti^c, with Ti^a occurring in most of the Indian genotypes. Trypsin-KTI complex assay exhibited binding of Ti^a polypeptide with 2.51 fold concentration of trypsin. Subsequently, seeds of selected genotypes were subjected to estimation of KTI and BBI activity through densitometry and enzyme-linked immunosorbent assay (ELISA), respectively; and total TIA through standard spectrophotometric protocol. Summation of KTI and BBI was significantly (P?<?0.05) lower than that of TIA determined through the spectrophotometric method.     

A robust watermarking scheme exploiting balanced neural tree for rightful ownership protection

This paper presents a novel semi-blind watermarking scheme in which a balanced neural tree (BNT) is exploited for embedding and extracting the watermark. The BNT is trained to learn the watermark and the synapses (optimal weights) of the trained BNT are embedded in the host image instead of watermark image. As a result, the proposed scheme is able to embed a large size of watermark in the host image. In the watermark extraction phase, the embedded synapses are extracted from the transmitted image and then watermark is recovered from these extracted synapses. In this way, the original image is not required in the extraction phase. The proposed scheme is tested to withstand various kinds of attacks and found to be robust against various image processing and geometrical attacks.     

Evaluation of Safety of Stewart’s Wood Fern (Dryopteris stewartii) and Its Anti-Hyperglycemic Potential in Alloxan-Induced Diabetic Mice

Diabetes has become a critical challenge to the global health concerns. Cytotoxicity and development of resistance against available drugs for management of diabetes have shifted the focus of global scientific researchers from synthetic to herbal medications. Therefore, the current study was conducted to investigate the possible anti-hyperglycemic potential of Dryopteris stewartii using Swiss albino mice. To evaluate any possible toxic effect of the plant, acute oral toxicity test was performed while the anti-diabetic effects of aqueous and ethanol extracts at 500 mg/kg, positive, negative and normal control were assessed simultaneously. The anti-diabetic study revealed that aqueous extract has higher anti-diabetic potential than ethanol extract while lowered blood glucose level at second week reaching 150 mg/dL, exerting stronger anti-diabetic effects, compared to ethanol extract (190 mg/dL). Oral glucose tolerance findings revealed that aqueous extract decreased blood glucose level by −0.41-fold, compared to ethanol extract showing a decrease by only −0.29-folds. The histopathological evaluation of liver and pancreas of all groups revealed normal cell architecture with no morphological abnormalities. These results suggested the possible use of D. stewartii as anti-diabetic herbal drug in near future. However, these recommendations are conditioned by deep mechanistic studies.     

Emergency management systems using mobile cloud computing: A survey

Among today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource-limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.     

Nanostructured Spinel Manganates and Their Composites for Electrochemical Energy Conversion and Storage

Spinel manganates (AMn₂O₄; A = Co, Ni, Cu, Zn, and Fe; collectively referred to as AMO) are promising electrode materials for water electrolyzers, pseudocapacitors, and batteries owing to their inherent advantages such as valence variability, high catalytic activity, conductivity, stability, low-cost, and environmental friendliness. Nanostructured materials, with a large surface area and short ion diffusion length, offer great potential for achieving enhanced electrochemical performance. This review summarizes spinel manganates with various nanostructured morphologies and discusses the impact of the structure and composition on the electrochemical performance. The review demonstrates that nanostructured spinel manganates with preferred A-site cation significantly improve the thermodynamics and electrochemical reaction kinetics at solid–liquid and solid–solid interfaces. Notably, faceted, hollow, 1D nanostructured CoMn₂O₄ and its nanocomposites (CoMn/CoMn₂O₄ and NiMn₂O₄/C) exhibit outstanding electrochemical performance. The review also provides an overview of the importance of energy conversion and storage, and the advantages of spinel manganates as electrode materials. Additionally, the review describes feasible methods of synthesizing AMO nanostructures and nanocomposites. The insights provided in this review are expected to contribute to the synthesis of spinel manganates with desired morphologies and compositions, enabling the future development of efficient electrode materials for energy conversion and storage devices.     

Efficient photocatalytic degradation of bisphenol A by green synthesized CuO decorated nickel hexacyanoferrate nanocomposite

Bisphenol A (BPA), a chemical additive in polycarbonate plastics and epoxy resins, is suspected to be an endocrine-disrupter. Extensive use and irregular treating methods have led to frequent detection of BPA in wastewater, raising demand for their removal by efficient nanomaterials-based technique. Nanocomposite CuO@NiHCF was synthesized via green method using Citrus aurantium peel extract. Crystalline structure (particle size <50 nm) of CuO@NiHCF was analysed by microscopic and spectroscopic analysis. This nanocomposite showed 97% degradation of BPA (50 mg L⁻¹) at neutral pH in sunlight. Moreover, improved particle stability (zeta potential: −56.2 mV; 2.0 eV) and high surface area, pore volume (81 m²g⁻¹, 13.9 nm) resulted from synergism of NiHCF (−26.3 mV; 2.4 eV), and CuO (−11.5 mV; 1.9 eV) led to efficient photodegradation of BPA. Degradation of BPA was found photo-adsorptive. Moreover, degradation was carried out by OH radicals based with ring-opening mechanisms by GC–MS. High efficiency and sustainability of CuO@NiHCF were revealed by its multiple reusability (n = 10), leading to a promising and sustainable photocatalyst.     

Deep Fuzzy SegNet-based lung nodule segmentation and optimized deep learning for lung cancer detection

Pattern Analysis and Applications - Globally, lung cancer has a high fatality rate and is a lethal disease. Since lung cancer affects both men and women, it requires extra consideration when...     

Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate. Hence to improve the precision of classification, Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient. The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets. The validation of testing sets is evaluated using RL that provides feedback to Classifiers. Finally, at the user interface, the signals are played by decoding the signal after being retrieved from the classifier back based on the input query. The results are evaluated in the form of accuracy, recall, precision, f-measure, and error rate, where generative adversarial network attains an increased accuracy rate than other methods: Multi-Layer Perceptron, Recurrent Neural Networks, Deep belief Networks, and Convolutional Neural Networks.