NS-2 based simulation framework for cognitive radio sensor networks

In this paper, we propose a simulation model for cognitive radio sensor networks (CRSNs) which is an attempt to combine the useful properties of wireless sensor networks and cognitive radio networks. The existing simulation models for cognitive radios cannot be extended for this purpose as they do not consider the strict energy constraint in wireless sensor networks. Our proposed model considers the limited energy available for wireless sensor nodes that constrain the spectrum sensing process—an unavoidable operation in cognitive radios. Our model has been thoroughly tested by performing experiments in different scenarios of CRSNs. The results generated by the model have been found accurate which can be considered for realization of CRSNs.     

Neuro-adaptive cooperative tracking control with prescribed performance of unknown higher-order nonlinear multi-agent systems

This paper is concerned with the design of a distributed cooperative synchronisation controller for a class of higher-order nonlinear multi-agent systems. The objective is to achieve synchronisation and satisfy a predefined time-based performance. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using neural networks. The proposed robust neuro-adaptive controller drives different states of nodes systematically to synchronise with the state of the leader node within the constraints of the prescribed performance. The nodes are connected through a weighted directed graph with a time-invariant topology. Only few nodes have access to the leader. Lyapunov-based stability proofs demonstrate that the multi-agent system is uniformly ultimately bounded stable. Highly nonlinear heterogeneous networked systems with uncertain parameters and external disturbances were used to validate the robustness and performance of the new novel approach. Simulation results considered two different examples: single-input single-output and multi-input multi-output, which demonstrate the effectiveness of the proposed controller.     

Dye-tolerant marine Acinetobacter baumannii-mediated biodegradation of reactive red

The objective of this study was to isolate a potent dye-degrading microbe that can be used to reduce the pollution caused by industrial dyes.Reactive red 198 is an extensively used textile dye and is a major environmental pollutant in water bodies. In this study, a bacterial strain was isolated from sea sediments and identified as Acinetobacter baumannii with 16S rRNA sequencing. The isolated bacteria were immobilized in calcium alginate and decolorization studies were carried out to determine the optimum pH, temperature, dye concentration, inoculum volume,and static/agitated condition using the one factor at a time(OFAT) approach. The Box-Behnken design, a type of response surface methodology,was adopted to improve the degradation efficiency. At 37℃ using an inoculum volume of six beads, 96.20% decolorization was observed in 500 mg/L of reactive red 198 after 72 h. Dye degradation was confirmed with UV-visible spectroscopy and Fourier-transform infrared(FTIR)spectroscopy studies of the dye and degraded metabolites. Microbial toxicity studies using Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa and phytotoxicity studies using Vigna radiata proved that the toxicity of the dye was significantly reduced after degradation. We can conclude that the isolated A. baumannii strain is an efficient dye-degrading microbe that can be used to reduce the pollution caused by industrial dyes.     

Dynamical Comparison of Several Third-Order Iterative Methods for Nonlinear Equations

There are several ways that can be used to classify or compare iterative methods for nonlinear equations, for instance; order of convergence, informational efficiency, and efficiency index. In this work, we use another way, namely the basins of attraction of the method. The purpose of this study is to compare several iterative schemes for nonlinear equations. All the selected schemes are of the third-order of convergence and most of them have the same efficiency index. The comparison depends on the basins of attraction of the iterative techniques when applied on several polynomials of different degrees. As a comparison, we determine the CPU time (in seconds) needed by each scheme to obtain the basins of attraction, besides, we illustrate the area of convergence of these schemes by finding the number of convergent and divergent points in a selected range for all methods. Comparisons confirm the fact that basins of attraction differ for iterative methods of different orders, furthermore, they vary for iterative methods of the same order even if they have the same efficiency index. Consequently, this leads to the need for a new index that reflects the real efficiency of the iterative scheme instead of the commonly used efficiency index.     

A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

With the rapid development of the mobile internet and the internet of things (IoT), the fifth generation (5G) mobile communication system is seeing explosive growth in data traffic. In addition, low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands. Millimeter wave (mmWave) technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks. Importantly, it has an abundant resource spectrum, which can significantly increase the communication rate of a mobile communication system. As such, it is now considered a key technology for future mobile communications. MmWave communication technology also has a more open network architecture; it can deliver varied services and be applied in many scenarios. By contrast, traditional, all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption. This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption. The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition (GMD). In this process, the objective function of the spectral efficiency is derived, then the basic tracking principle and least square (LS) techniques are deployed to design the proposed hybrid precoding. Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45% compared to traditional algorithms.     

Green isolation and physical modification of pineapple stem waste starch as pharmaceutical excipient

The waste of inedible parts of pineapple, particularly in tropical countries, contributes to environmental burden. This study aimed to utilize pineapple stem waste as a source of starch-based pharmaceutical excipient. The starch was isolated from pineapple stem waste using a simple process without applying harsh chemicals. The isolated starch (PSS) was then physically modified through gelatinization and spray drying to improve its physical properties. Starch characteristics were identified by FTIR, TGA, and XRD analysis. The SEM imaging showed morphological change with reduced surface roughness due to physical modification of the starch. Decreased crystallinity of modified starch (MPS) was confirmed by our XRD results: the peaks of A-type crystalline at 2θ of 13°, 15°, 18°, and 23° were present in PSS, yet mostly absent in MPS. Thermogravimetric analysis showed that MPS behaved differently from PSS and the degradation events occurred at lower temperature. When the starch was spray-dried without prior gelatinization process, the physicochemical characteristics of spray-dried starch resembled untreated starch. Moisture content in PSS (10.66%) decreased after gelatinization to 7.3%. Potential use of MPS was demonstrated by its powder flowability (Student’s t test, p?<?0.05), swelling capacity (Student’s t test, p?<?0.05), and compaction profile. In summary, our findings demonstrated that modified pineapple starch showed better physical characteristics and quite promising as a tablet binder and disintegrant.     

Design and Experimental Verification of a 3-DOF Spherical Electromagnetic Brake for Haptic Interface

Electromagnetic brake is an efficient device that provides damping to stop the undesired motion of the manipulator. A spherical design of electromagnetic b     

A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

     

Sensitive pH detection on gold interdigitated electrodes as an electrochemical sensor

Microsystem Technologies - Miniaturized in-house high-performance sensors are mandatory for implementing the current needs efficiently. With the microsystems, electrode material is crucial to...     

An overview of spectrum sensing techniques for cognitive LTE and LTE-A radio systems

Advanced communication systems, such as long term evolution (LTE) and LTE-advanced (LTE-A) systems, promise to increase the number of users with high-speed data exchange. However, it leads to spectrum scarcity because of the huge size of data exchange with limited spectrum resources. Cognitive radio (CR) technique is considered the best solution for this spectrum scarcity problem. Spectrum sensing (SS), one of the CR techniques is used to detect the spectrum hole of primary user (PU) without interference with PU. In this paper, several SS approaches for LTE and LTE-A systems are investigated in the CR system. These SS approaches are based on two techniques, namely energy detection and cyclostationary feature detection techniques. The first technique includes four approaches of auto-correlation based advanced energy, time domain detection, Welch periodogram and two-stage model algorithms, while the second technique contains two approaches, namely pilot induced cyclostationary and second order cyclostationary algorithms. According to the analysis, the two-stage model and the second order cyclostationary algorithms are better than the other algorithms because they produce accurate results at the expense of system complexity. Hence, in general a good SS algorithms would require some trade-off between complexity and accuracy.