Effects of fiber surface grafting by functionalized carbon nanotubes on the interfacial durability during cryogenic testing and conditioning of CFRP composites

Carbon fiber reinforced epoxy (CE) composite is ideal for a cryogenic fuel storage tank in space applications due to its unmatched specific strength and modulus. In this article, inter-laminar shear strength (ILSS) of carbon fiber/epoxy (CE) composite is shown to be considerably improved by engineering the interface with carboxyl functionalized multi-walled carbon nanotube (FCNT) using electrophoretic deposition technique. FCNT deposited fibers from different bath concentrations (0.3, 0.5, and 1.0 g/L) were used to fabricate the laminates, which were then tested at room (30°C) and in-situ liquid nitrogen (LN) (−196°C) temperature as well as conditioning for different time durations (0.25, 0.5, 1, 6, and 12 h) followed by immediate RT testing to study the applicability of these engineered materials at the cryogenic environment. A maximum increment in ILSS was noticed at bath concentration of 0.5 g/L, which was ~21% and ~ 17% higher than neat composite at 30°C and − 196°C, respectively. Short-term LN conditioning was found to be detrimental due to developed cryogenic shock, which was further found to be compensated by cryogenic interfacial clamping upon long-term exposure.     

Mechanical behavior of electrophoretically modified CFRP composites at elevated temperatures: An assessment of the influence of graphene carboxyl bath concentration

The study aims at investigating the mechanical behavior of carbon fiber reinforced polymer (CFRP) composites modified with graphene carboxyl at elevated temperature (ET-110°C) and understanding the effect of electrophoretic deposition bath concentration (0.5 g/L, 1.0 g/L, and 1.5 g/L) on their mechanical behavior at ET. The 1.5 g/L composite has revealed a maximum improvement in energy absorbed before failure of 33.25% at RT and 22.54% at ET for flexural testing and ∼35% at RT for short beam shear testing, over neat CFRP composite. The modified composites have shown an improved flexural strain to failure at both RT and ET, with 1.5 g/L composite exhibiting maximum enhancement of 12.41% at RT and 26.52% at ET over neat composite. However, at ET, modified composites exhibited lower flexural strength and interlaminar shear strength values in comparison to that of neat. Viscoelastic behavior of all composites was studied to understand bath concentration's effect on thermal behavior via dynamic mechanical thermal analysis. Differential scanning calorimetry was employed for governing the glass transition temperature of composites. Fractography of tested samples (both ET and RT) was performed utilizing a scanning electron microscope to determine the prominent failure mode.     

Effect ofθ-alumina formation on the growth kinetics of alumina-forming superalloys

Alumina-forming ODS superalloys are excellent oxidation-resistant materials. Their resistance relies upon the establishment of a stable, slow-growing, and adherent -alumina. In the present investigation, these alloys exhibited unstable and relatively less adherent -alumina phase, which increased the oxidation rate in the transient stage and converted into -alumina in the later part of the exposure. The oxide-growth process was found to depend upon various parameters such as temperature, time, and presence of an active elecment in the superalloy. Characterization carried out by XRD, SEM/EDAX, and AES on oxidized ODS and non-ODS alloys demonstrated a significant influence of the active element, Y, on the transformation of - to -alumina. SIMS analysis of two-stage oxidation at 900°C for two different durations evidently showed that the change in the transport process is due to -to--alumina transformation. On the basis of these results, a new and consistent mechanism is proposed to explain the influence of -alumina and its transformation on growth kinetics and the effect of yttrium on the transformation leading to good scale adherence and oxidation resistance.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Chemical-bath deposition of band-gap-tailored CdxPb1−xS films

An attempt was made to modify the band gap of CdS ( 2.4 eV) by preparing a mixed lattice with a low-band-gap material, PbS (0.3 eV), giving a new set of materials, Cd _x Pb_1–x S. Band gaps as low as 1.9 eV were achieved with increasing x. The preparation of Cd _x Pb_1–x S was carried out by chemical-bath deposition. Structural characterization studies using X-ray diffraction (XRD), energy dispersion analysis by X-rays (EDAX), and optical microscopy were performed. The optical-absorption studies used to find the band gap are also described.     

Fast Ion Transport in Li-Rich Alloy Anode for High-Energy-Density All Solid-State Lithium Metal Batteries

All-solid-state Li batteries (ASSLBs) with solid-polymer electrolytes are considered promising battery systems to achieve improved safety and high energy density. However, Li dendrite formation at the Li anode under high charging current density/capacity has limited their development. To tackle the issue, Li-metal alloying has been proposed as an alternative strategy to suppress Li dendrite growth in ASSLBs. One drawback of alloying is the relatively lower operating cell voltages, which will inevitably lower energy density compared to cells with pure Li anode. Herein, a Li-rich Li₁₃In₃ alloy electrode (LiRLIA) is proposed, where the Li₁₃In₃ alloy scaffold guides Li nucleation and hinders Li dendrite formation. Meanwhile, the free Li can recover Li's potential and facilitate fast charge transfer kinetics to realize high-energy-density ASSLBs. Benefitting from the stronger adsorption energy and lower diffusion energy barrier of Li on a Li₁₃In₃ substrate, Li prefers to deposit in the 3D Li₁₃In₃ scaffold selectively. Therefore, the Li–Li symmetric cell constructed with LiRLIA can operate at a high current density/capacity of 5 mA cm⁻²/5 mAh cm⁻² for almost 1000 h.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.