A smart spectrum utilization approach using multiantenna‐based cognitive relays in cognitive radio network

Efficient spectrum utilization is a promising technique for a prolonged unused radio frequency (RF) spectrum in a wireless network. In this paper, an adaptive spectrum sharing cognitive radio (CR) network has been proposed consisting of a primary user (PU) and secondary user transmitter (SU ? Tx) that communicates with secondary user receiver (SU ? Rx) via multiantenna‐based proactive decode‐and‐forward (DF) relay selection scheme. In our model, strategically an adaptable joint venture on underlay/overlay protocol is defined based on channel occupancy using spectrum sensing technique. Here, secondary transmitters (i.e., source transmitter) continuously sense the PU activities by energy detector and can simultaneously transmit to secondary receivers. Depending on sensing result secondary transmitters automatically switches in underlay mode if PU is active otherwise operates in overlay mode. The advantage of this scheme is that the joint mode of transmission allows the SUs to maximize their transmission rate. The outage performance at SU ? Rx and closed‐form expressions of joint underlay/overlay protocol has been evaluated. The power control policies at different transmitter nodes are taken care of. With the same diversity order, a trade‐off between multiantenna and multirelay is shown. This comparison shows improvement in outage behavior when the count in relays surpasses the number of antennas. Finally, the analytical model of smart efficient spectrum utilization without harming license users in CR is validated by MATLAB simulation.     

Black Phosphorus@Laser-Engraved Graphene Heterostructure-Based Temperature–Strain Hybridized Sensor for Electronic-Skin Applications

Smart electronic skin (e-skin) requires the easy incorporation of multifunctional sensors capable of mimicking skin-like perception in response to external stimuli. However, efficient and reliable measurement of multiple parameters in a single functional device is limited by the sensor layout and choice of functional materials. The outstanding electrical properties of black phosphorus and laser-engraved graphene (BP@LEG) demonstrates a new paradigm for a highly sensitive dual-modal temperature and strain sensor platform to modulate e-skin sensing functionality. Moreover, the unique hybridized sensor design enables efficient and accurate determination of each parameter without interfering with each other. The cationic polymer passivated BP@LEG composite material on polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) substrate outperforms as a positive temperature coefficient material, exhibiting a high thermal index of 8106 K (25–50  ° C) with high strain sensitivity (i.e., gauge factor, GF) of up to 2765 ( > 19.2%), ultralow strain resolution of 0.023%, and longer durability ( > 18 400 cycles), satisfying the e-skin requirements. Looking forward, this technique provides unique opportunities for broader applications, such as e-skin, robotic appendages, and health monitoring technologies.     

Polyaziridine-Encapsulated Phosphorene-Incorporated Flexible 3D Porous Graphene for Multimodal Sensing and Energy Storage Applications

Heteroatom-incorporated graphene represents a prominent family of materials utilized as active electrodes for multimodal sensing and energy storage applications. Herein, a novel polyaziridine-encapsulated phosphorene (PEP)-incorporated flexible 3D porous graphene (3DPG) electrode is developed using facile, cost-effective laser writing, and drop-casting techniques. Owing to the excellent electrochemical characteristics and surface functionality of the highly stable PEP, the fabricated PEP/3DPG is evaluated as a potential electrode for immunosensing, electrocardiogram (ECG) recording, and microsupercapacitors (MSCs). Under optimized conditions, the produced PEP/3DPG-based carcinoembryonic immunosensor exhibits linear ranges of 0.1–700 pg mL⁻¹ and 1–100 ng mL⁻¹ with a detection limit of 0.34 pg mL⁻¹ and high selectivity. The finger touch-based ECG sensor demonstrates a relatively low and stable impedance at the skin-electrode interface; therefore, the signal-to-noise ratio of the ECG signal received from the fabricated sensor (13.5 dB) is comparable to that of conventional Ag/AgCl electrodes (13.9 dB). Besides, the highest areal capacitance of the prepared MSC reached a magnitude of 16.94 mF cm⁻², which is six times higher than that of a non-doped 3DPG-based MSC. These results demonstrate the effectiveness of the described fabrication procedure and the high utilization potential of the encapsulated phosphorene-doped 3D graphene in multimodal applications.     

Design and simulation of optoelectronic oscillator with micro ring resonator and radio frequency amplifier modelling at 110 GHz

There is an increasing need for high performance oscillators as the faster transmission networks demand for high frequency signals. Opto-electronic oscillators (OEO) enable us to make better oscillators in terms of size, weight and power. In this paper, photonic integration is proposed for realizing the OEO with micro ring resonator (MRR) and radio-frequency (RF) amplifiers of monolithic microwave integrated circuit (MMIC), which can be used for generating 110 GHz sine wave. The OEO architecture is proposed and block diagram developed considering Silicon based MRR and three-stage RF amplifier based on GaN high-electron-mobility transistor (HEMT). A simulation model is developed according to the Klein model of MRR and is validated against the calculated performance parameters. MRR dimensions are calculated as with silicon on insulator (SOI) technology and a radius 5.27 μm for the device is derived. Free spectral range (FSR) of 48.52 nm and filter rejection ratio of 16.79 dB are obtained for this device. The proposed RF amplifier is modelled with GaN parameters derived from high frequency pinch-off model and with power amplifier considerations. The gain for this amplifier is obtained as 10.6 dB. The OEO design is developed in this project in such a way that the system can be manufactured with the existing methods.     

Erratum to: First-Principles Investigation of Electronic Properties of GaAsxSb1 – x Ternary Alloys

Semiconductors - erratum     

Epilepsy analysis using open source EDF tools for information science and data analytics

Electroencephalogram (EEG) is the signals that measure the electrical variances of brain using metal electrodes. We observe the EEG signals by using European Data Format (EDF) BROWSER and EEG STUDIO. By using EDF BROWSER, we can get the mean and frequency from the filtered output signal using band‐pass filter. Using EDF BROWSER, we can also perform Root Mean Square (RMS) and signal samples. Using EEG STUDIO, we can analyze the average frequency and standard deviation. Epileptic seizure prediction and detection are done by spike detection, frequency domain analysis, and nonlinear methods. EEG signal contains different artifacts like electrooculography (EOG), EKG, and electrocardiogram (ECG). ECG signals are produced by heart. EOG signals are produced by eyes. EMG signals are produced by muscle coordination.     

A Novel Moisture‐Insensitive and Low‐Corrosivity Ionic Liquid Electrolyte for Rechargeable Aluminum Batteries

Rechargeable aluminum batteries (RABs) are extensively developed due to their cost‐effectiveness, eco‐friendliness, and low flammability and the earth abundance of their electrode materials. However, the commonly used RAB ionic liquid (IL) electrolyte is highly moisture‐sensitive and corrosive. To address these problems, a 4‐ethylpyridine/AlCl₃ IL is proposed. The effects of the AlCl₃ to 4‐ethylpyridine molar ratio on the electrode charge–discharge properties are systematically examined. A maximum graphite capacity of 95 mAh g^?1 is obtained at 25 mA g^?1. After 1000 charge–discharge cycles, ≈85% of the initial capacity can be retained. In situ synchrotron X‐ray diffraction is employed to examine the electrode reaction mechanism. In addition, low corrosion rates of Al, Cu, Ni, and carbon‐fiber paper electrodes are confirmed in the 4‐ethylpyridine/AlCl₃ IL. When opened to the ambient atmosphere, the measured capacity of the graphite cathode is only slightly lower than that found in a N₂‐filled glove box; moreover, the capacity retention upon 100 cycles is as high as 75%. The results clearly indicate the great potential of this electrolyte for practical RAB applications.     

Remote Manipulation of Slidable Nano‐Ligand Switch Regulates the Adhesion and Regenerative Polarization of Macrophages

The development of materials capable of varying macroscale ligand distributions can emulate an extracellular matrix (ECM) remodeling and regulate the adhesion and polarization of macrophages. In this report, negatively charged slidable nano‐ligands are assembled and then conjugated to a positively charged substrate via electrostatic interaction. The negatively charged slidable nano‐ligands are prepared by coating magnetic nanoparticles with a polymer linker and negatively charged RGD ligand. The nano‐ligand sliding is characterized under an external magnetic field, which spatiotemporally alters macroscale ligand density. To the best of knowledge, this is the first demonstration that magnetic maipulation of the macroscale ligand density inhibits inflammatory M1 phenotype but stimulates the adhesion and regenerative M2 phenotype of host macrophages. Furthermore, it is elucidated that the magnetic attraction of the slidable nano‐ligand facilitates the assembly of adhesion structures in macrophages, thereby stimulating their regenerative M2 phenotype. The design of ECM‐emulating materials that allow remote, spatiotemporal, and reversible controllability of macroscale ligand density provides an appealing strategy in the spatiotemporal regulation of immunomodulatory tissue‐regenerative responses to implants in vivo.     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.

     

A comprehensive survey on image enhancement techniques with special emphasis on infrared images

Capturing of infrared images is an easy task but perceptual visualization is difficult due to environmental conditions such as light rain, partly cloudy, mostly cloudy, haze, poor lightening conditions, noise generated by the sensors, geographical distance and appearances of the objects. To improve the human perception and quality of the infrared images for further processing like image analysis, image enhancement is an essential process. This paper provides a detailed review of various image enhancement techniques from contrast stretching to optimization methods used in infrared images. It also discusses the existing infrared image enhancement techniques as group such as histogram based methods, filter based methods, transform domain based methods, morphological based methods, saliency extraction methods, fuzzy based methods, learning methods, optimization methods and its popular algorithms also address the countless issues. Some of the existing image enhancement methods (Histogram Equlization, Max-median filter, Top-Hat transform) and infrared image enhancement methods (multi-scale top-hat transform, adaptive infrared image enhancement) are implemented along with the adaptive fuzzy based infrared image enhancement method and its obtained results evaluation is done on subjective and objective ways. From the results observed that the fuzzy based method works well for both subjective and objective evaluation. The paper aims to provide a complete study on image enhancement techniques and how they specially utilized while dealing with infrared images. In addition, the paper helps the researchers to select the suitable infrared image enhancement techniques for their infrared image application needs.