Compressive spectrum sensing using two-stage scheme for cognitive radio networks

The modern applications of communications that use wideband signals suffer the lacking since the resources of this kind of signals are limited especially for fifth generation (5G). The Compressive Spectrum Sensing (COMPSS) techniques address such issues to reuse the detected signals in the networks and applications of 5G. However, the raw techniques of COMPSS have low compression ratio and high computational complexity rather than high level of noise variance. In this paper, a hybrid COMPSS scheme has been developed for both non-cooperative and cooperative cognitive radio networks. The proposed scheme compiles on discrete wavelet transform – single resolution (DWT-SR) cascaded with discrete cosine transform (DCT). The first is constructed according to the pyramid algorithm to achieve 50% while the second performed 30% compression ratios. The simulation and analytic results reveal the significant detection performance of the proposed technique is better than that of the raw COMPSS techniques

Estimation of the fuzzy reliability function using two-parameter exponential distribution as prior distribution

In this research, the fuzzy reliability function of the series system has been estimated using Bayes approach and Mellin transformation. It is based on the existence of two parameter exponential distribution as a previous distribution with the existence of a similar quadratic loss function, square loss function and non- asymmetric precautionary loss function. To apply the Bayes approach, the distribution parameters are assumed to be "random variables", and the traditional Bayes approach was used to obtain Bayes fuzzy capabilities by using Resolution Identity Theory in the fuzzy set. The simulation approach has been applied in this study to know the effect of α value on the fuzzy reliability function capabilities. The experiment has been carried out by assuming different values of the parameters as well as the sizes of the different samples. Furthermore, the applied part has dealt with the fuzzy reliability function estimation of both the quadratic loss function and the precautionary loss function with different α values using nonlinear membership functions. Some mathematical equations have been used to calculate the membership scores of the Bayes estimated points. This purpose has been achieved by converting the original problem into a non-linear programming problem and then divided it into eight secondary problems. The results have been obtained using the LINGO and GAMS programs

A new cooperative spectrum sensing scheme based on discrete cosine transform

Recently, the need to extra frequency bands is increasing significantly due to modern devices and applications. This problem can be addressed by exploiting the idle spectra. Thus, the spectrum exploiting is performed using the technique of spectrum sensing. In this paper, a new cooperative spectrum sensing scheme is proposed based on discrete cosine transform periodogram. This scheme is applied on both DVB models for AWGN channel and various S NR values. The obtained results reveal that the proposed scheme has a good performance for ten secondary users and low SNR

A performance analysis of a new periodogram for spectrum sensing

The cognitive radio is considered a best solution for the limited spectrum resources problem. The periodogram based energy detection can be used for spectrum estimation in cognitive radio. It does not need any prior information about the primary signal. This paper presents a new periodogram by using the Discrete Cosine Transform (DCT). In addition, it analyses and compares the performance with raw periodogram. The result reveals the DCT based periodogram is better than the traditional one due to its low variance. Consequently, the proposed system has a high probability of detection with low probability of false alarm even in case of lower SNR

Effect of Aluminum Doping on Zinc Oxide Thin Film Properties Synthesis by Spin Coating Method

A sol-gel spin coating method has been successfully used to synthesize pure zinc oxide (ZnO) and Al-doped ZnO thin films. Synthesis of pure phase of ZnO thin films with polycrystalline hexagonal wurtzite structure with the lattice parameters a=3.2568A and c=5.2108A have been revealed using XRD analysis. There is no change in the phase structure with Al doped ZnO thin films and the lattice parameters decreased with increased in Al-doping concentration. The crystallite size, lattice constants and strain are decreased, as the Al dopant concentration increases in ZnO lattice which is attributed to the interstitial substitution of Al ions in Zn sites into ZnO lattice as confirmed by EDX results. SEM studies show that with Al-doping the growth of the films takes place to be nanowire in structure at Al concentration up to 3% wt and the size reduced more at 5% wt. This indicates that Al-doping has an influence on the surface morphology of the films. Bandgap energy of ZnO is 3.37 eV with direct band to band transitions and decreased to 3.25 eV with increased Al-doping concentration. These properties of Al doped ZnO thin films make it a promising materials to be effectively used in many optoelectronic devices and application such as solar cell,  photocatalysis, gas sensor and so on.

Sustainable design of self-consolidating green concrete with partial replacements for cement through neural-network and fuzzy technique

In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results

The impact of M-ary rates on various quadrature amplitude modulation detection

The 5G system-based cognitive radio network is promised to meet the requirements of huge data applications with spectrum. However, the M-ary effect on the detection has not been thoroughly investigated. In this paper, an M-ary of quadrature amplitude modulation detection system is studied. Many rates are used in this study 4, 16, 64, and 256 constellation points. The detection system is applied to cooperative spectrum sensing to enhance the performance of detection for various rates of M-ary with low signal-to-noise ratio (SNR). Further, three kinds of signals based 5G system are sensed: filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), and universal filtered multi-carrier (UFMC). The best detection performance is obtained when the M-ary=4 and number of SUs=50 user, whereas the worst detection performance is obtained when the M-ary=256 and number of SUs=10 user, as revealed in the simulation results. In addition, the detection performance for the F-OFDM signal is better than that of UFMC and FBMC signals for SNR <0 dB

Synthesis and Applications of Organotin (IV) Compounds: Mini Review

Organotin (IV) compounds have seen a marked increase in industry utilization over the years and exhibited enormous economic benefits as well as environmental costs due to their numerous industrial, medical, and agricultural uses and other applications. The present review is a continuation of a series of reviews on the use of organotin (IV), chemicals, synthesis, characteristics and geometry as well as the industrial and biological applications

Introducing adaptive machine learning technique for solving short-term hydrothermal scheduling with prohibited discharge zones

The short-term hydrothermal scheduling (STHTS) problem has paramount importance in an interconnected power system. Owing to an operational research problem, it has been a basic concern of power companies to minimize fuel costs. To solve STHTS, a cascaded topology of four hydel generators with one equivalent thermal generator is considered. The problem is complex and non-linear and has equality and inequality constraints, including water discharge rate constraint, power generation constraint of hydel and thermal power generators, power balance constraint, reservoir storage constraint, initial and end volume constraint of water reservoirs, and hydraulic continuity constraint. The time delays in the transport of water from one reservoir to the other are also considered. A supervised machine learning (ML) model is developed that takes the solution of the STHTS problem without PDZ, by any metaheuristic technique, as input and outputs an optimized solution to STHTS with PDZ and valve point loading (VPL) effect. The results are quite promising and better compared to the literature. The versatility and effectiveness of the proposed approach are tested by applying it to the previous works and comparing the cost of power generation given by this model with those in the literature. A comparison of results and the monetary savings that could be achieved by using this approach instead of using only metaheuristic algorithms for PDZ and VPL are also given. The slipups in the VPL case in the literature are also addressed

Dragonfly algorithm-based optimization for selective harmonics elimination in cascaded H-bridge multilevel inverters with statistical comparison

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms’ search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem