Microwave-Assisted Pretreatment of Sago Palm Bark

Three types of microwave-assisted diluted solvents were employed using 0.1 N H₂SO₄ (MSA), 0.1 N NaOH (MSH), and 0.01 N NaHCO₃ (MSB). These solvents were evaluated as possible pretreatment routes for sago palm bark (SPB) with their effects on the pretreated substrate. A variety of analyses, consisting of fiber analysis, energy dispersive X-ray spectroscopy (EDX), X-ray fluorescence spectrometer (XRF), X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and high-performance liquid chromatography (HPLC), were performed to understand the pretreatment effects on the chemical and physical characteristics of SPB and pretreatment liquor. The thermal analysis has revealed that higher hemicellulose degradation was also found in MSA pretreatment. In the analyses of the pretreatment liquid for the extracted monomeric sugar, a higher amount of glucose was found (9 mg/g) using MSH pretreatment and the highest xylose level was found (4 mg/g) using MSA pretreatment. The analysis of the formation of inhibitors has shown that acetic acid was only found in the MSH pretreatment.     

Least-Square Collaborative Beamforming Linear Array for Steering Capability in Green Wireless Sensor Networks

This paper presents a collaborative beamforming (CB) technique to organize the sensor node's location in a linear array for green wireless sensor network (WSN) applications. In this method, only selected clusters and active CB nodes are needed each time to perform CB in WSNs. The proposed least-square linear array (LSLA) manages to select nodes to perform as a linear antenna array (LAA), which is similar to and as outstanding as the conventional uniform linear array (ULA). The LSLA technique is also able to solve positioning error problems that exist in the random nodes deployment. The beampattern fluctuations have been analyzed due to the random positions of sensor nodes. Performances in terms of normalized power gains are given. It is demonstrated by a simulation that the proposed technique gives similar performances to the conventional ULA and at the same time exhibits lower complexity.     

A novel method of S-box design based on discrete chaotic maps and cuckoo search algorithm

Multimedia Tools and Applications - Substitution-boxes (S-boxes) are unique nonlinear elements, which are used to achieve the property of confusion in modern symmetric ciphers and offer resistance...     

Novel Nano Structure for Green Energy Harvesting at 10 μm Thermal Radiation

In recent years, the potential of renewable green energy sources has been extensively studied. The proven technology which is photovoltaic solar cells strictly depends on daylight and produces low-efficiently. To overcome the restrictions, one technology studied is through harvesting the thermal radiation energy which can provide a 24-hour energy source. The continuity of energy sources promises very good energy conversion especially for military applications. This article presents a new structure that can harvest the abundant thermal radiation energy into usable energy at the wavelength of 10 m. A rectangular structure with a perturbation slit was designed to integrate with a rectifier circuit for green energy conversion. The slit tunnel junction guided the electromagnetic field into a junction where the energy could be collected and converted. An enhancement factor of approximately 110.6 can be achieved by a perturbation slit length of 1.0 m. The results extracted from the proposed design promise a better candidate to overcome the disadvantages of photovoltaic solar cells for energy harvesting devices.     

Process Noise Parameters of Beamforming Green Nodes

Signals arrive out of phase at the intended receiver from collaborative beamforming (CB) nodes due to the instability in the output frequency signals of the universal software radio peripheral's (USRP) local oscillator (LO). These nodes including the target must synchronize their oscillator frequencies for coherent signal reception. In order to do this, frequencies and phases of the signals should be estimated in software defined radio (SDR) and smoothen with nonlinear filters such as the extended Kalman filter (EKF). The process noise parameters of the NI USRP-2920 nodes will have to be calculated and used with the EKF process noise covariance matrix. These nodes are green communication hardware devices where most of the hardware units are now software defined. This article uses the direct spectrum method to obtain the phase noise values at various frequency offsets of the NI USRP-2920 in order to calculate the power spectral density of fractional frequency fluctuation. By applying the power-law noise model to this obtained value, the generated white frequency noise and random walk frequency noise values are q1=1.9310-21 and q2=5.8610-18, respectively.     

Development of a hybrid PSO–ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R ²) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R ² of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model.

     

Failure behavior of quasi-isotropic carbon fiber-reinforced polyamide composites under tension

In this paper, the microscopic failure behavior of quasi-isotropic carbon fiber-reinforced polyamide-6 (CF/PA6) laminates under tension was investigated experimentally. Laminates of two layups, namely [45°/0°/?45°/ 90°]_s and [45°/0°/?45°/ 90°]_2s, were made from CF/PA6 tapes of two different manufacturers and then subjected to tensile testing. Crack initiation and progression on the polished free edge of specimens were examined using optical microscopy, under several load levels. Crack growth behavior through the specimen width was also traced by observing the crack configurations in different sections in the specimen width direction. The effects of the spatial distribution of fiber on the microscopic damage events were elucidated. The difference in failure behavior between the present CF/PA6 laminates and conventional thermosetting CF/Epoxy laminate is discussed.     

Real-Time Frequency Estimation of Complex GMSK Signal of Green Communications Devices

Parameter estimation of signals of universal software radio peripheral (USRP) devices is crucial to solve the problem of phase offsets of received signals in distributed beamforming. For systems that will utilize the closed loop feedback algorithm where the receiver needs to send the received signal strength (RSS) values periodically to the beamforming node so as to take advantage of energy conservation, the frequency and phase of these signals should be estimated before smoothening by nonlinear filters. This article presents the estimation of the frequency offsets of a Gaussian minimum shift keying (GMSK) signal from N210 USRP devices in real time by using the Radix-2 fast Fourier transform (FFT) algorithm in GNURadio. For these green communications devices, most of the needed hardware parts have been software defined, thereby reducing the supposed energy consumption. The frequency offsets from reference carrier frequencies of 900 MHz and 2.4 GHz are less than 3 kHz each before the estimation, but the average offsets are 45 Hz and 100 Hz after the estimation, respectively. The high offset value experienced with the 2.4 GHz carrier was due to consistent interference from devices on that same frequency.