A novel vacuum wastewater treatment plant integrated with a solar absorption system

In this investigation, a novel use of the solar absorption refrigeration systems was introduced by using it to enhance the operation of vacuum wastewater treatment plant. Wastewater treatment systems require a source of heat for evaporation, a cooling section for condensation and a mean of evacuation to facilitate evaporation. The solar absorption system can take over the first two tasks. Among the commercially available vacuum evaporators, one was selected and modified by replacing the conventional heat pump with the solar absorption system. Two validated mathematical models available in the literature, one for solar absorption subsystem and one for vacuum evaporation subsystem, were integrated together to perform the analysis. The impacts of solar absorption subsystem parameters along with vacuum evaporator subsystem parameters on the overall performance were investigated using the developed program. System performance was evaluated in terms of evaporation rate and condensate rate production. It was found that the degree of superheat had the greatest impact on the rate of evaporation. At low levels of supply temperature to the vacuum chamber, using only heat provided by the absorption system, the evaporation rate exceeded 60 kg/h. If the hot water was further heated by passing through the solar collector storage tank, the evaporation rate exceeded 200 kg/h.     

An accurate and simplified small signal parameter extraction method for GaN HEMT

In this paper, development of a small signal model for 2 × 200 μm GaN HEMT based on the conventional 20-element model is presented. The proposed model presents a direct parameter extraction algorithm, instead of the hybrid optimization approach, that provides simplification, accuracy, and less computational complexity. The extrinsic elements are extracted using a modified cold pinch-off condition while discarding the unwanted forward biasing of the gate. The negative drain to source capacitance C_ds is also observed in the ohmic region (for smaller V_DS). An excellent agreement found between the measured and modeled data for a wide range of frequencies and bias values shows the effectiveness of the proposed approach. The proposed modeling technique is validated with a good agreement between the achieved bias dependency of intrinsic parameter values and the respective theoretical parameter values.     

Optimum alternatives of tandem G/G/K queues with disaster customers and retrial phenomenon: interactive voice response systems

In this paper, we study a tandem queue with retrials where the queue experiences disasters. The probability of system failure depends on the strength of equipment, which makes servers idle and causes the removal of all customers in queues and service areas at once. The customers in the queue are forced to orbit in a retrial queue during the system failure where they decide whether or not to come back to the system. Reducing the disaster arrival rate (the probability of system failure) by employing more servers and reducing the number of lost customers is very costly. Moreover, it is important to service the customers with no interruption and reduce the time in system. The developed scenarios are compared in five dimensions including time in system, cost of lost customer, operator cost, the number of uninterrupted service customers and cost of reducing disaster arrival rate (or empowering system cost). The scenarios are modeled by computer simulation. Then, the optimal scenario is chosen using data envelopment analysis. The optimal scenario maximizes system efficiency in terms of disaster arrival rate, cost of lost customers and the number of satisfied customers. In the main problem, the disasters arrive at the system according to Poisson process; the effect of changing the distribution function of disaster arrival has been investigated finally. We are among the first ones to study and optimize G/G/K tandem queuing systems with system failures and retrial phenomena in interactive voice response systems.     

A secure search protocol for lightweight and low-cost RFID systems

In radio frequency identification (RFID) systems, search protocols are used to find a specific item in a large number of tagged products. These protocols should be secure against RFID attacks such as traceability, impersonation, DoS and eavesdropping. Sundaresan et al. (IEEE Trans Dependable Secure Comput, 2015) presented a server-less search protocol based on 128-bits PRNG function and claimed that their method can address all vulnerabilities of previous protocols. In this paper, we prove that Sundaresan et al.’s protocol is vulnerable to traceability attack with the high probability. In addition, we present an improved protocol to solve the proposed problem and analyze its security level informally and formally based on AVISPA tool and BAN logic.     

A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network

Recently, Underwater Wireless Sensor Networks (UWSNs) has witnessed significant attention from both academia and industries in research and development, due to the growing number of applications for wide range of purposes including commercial, scientific, environmental and military. Some of the major applications include pollution monitoring, tactical surveillance, tsunami warnings and offshore exploration. Efficient communication among sensors in UWSNs is a challenging task due to the harsh environments and peculiar characteristics of UWSNs. Therefore, design of routing protocol for efficient communication among sensors and sink is one of the fundamental research themes in UWSNs. In this context, this paper proposes a location-free Reliable and Energy efficient Pressure-Based Routing (RE-PBR) protocol for UWSNs. RE-PBR considers three parameters including link quality, depth and residual energy for balancing energy consumption and reliable data delivery. Specifically, link quality is estimated using triangle metric method. A light weight information acquisition algorithm is developed for efficient knowledge discovery of the network. Multi-metric data forwarding algorithm is designed based on route cost calculation which utilizes residual energy and link quality. Simulations are carried out in NS-2 with Aqua-Sim package to evaluate the performance of RE-PBR. The performance of the proposed protocol is compared with the stat-of-the-art techniques: DBR and EEDBR. The comprehensive performance evaluation attests the benefit of RE-PBR as compared to the state-of-the-art techniques in terms of network lifetime, energy consumption, end-to-end delay and packet delivery ratio.     

Structural,Thermal and Luminescence Properties of AlN:Tm Thin Films Deposited on Silicon Substrate and Optical Fiber

Semiconductors - Thin films of AlN:Tm are deposited on a Si(111) and Si(100) substrates and optical fiber by rf magnetron sputtering method. 200–400 nm thick films are deposited at various...     

Software‐defined networking approach for enhanced evolved packet core network

The evolved packet core (EPC) network is the mobile network standardized by the 3rd Generation Partnership Project and represents the recent evolution of mobile networks providing high‐speed data rates and on‐demand connectivity services. Software‐defined networking (SDN) is recently gaining momentum in network research as a new generation networking technique. An SDN‐based EPC is expected to introduce gains to the EPC control plane architecture in terms of simplified, and perhaps even software‐based, vendor independent infrastructure nodes. In this paper, we propose a novel SDN‐based EPC architecture along with the protocol‐level detailed implementation and provide a mechanism for identifying information fields exchanged between SDN‐EPC entities that maintains correct functionality with minimal impact on the conventional design. Furthermore, we present the first comprehensive network performance evaluation for the SDN‐based EPC versus the conventional EPC and provide a comparative analysis of 2 networks performances identifying potential bottlenecks and performance issues. The evaluation focuses on 2 network control operations, namely, the S1‐handover and registration operations, taking into account several factors, and assessing performance metrics such as end‐to‐end delay (E2ED) for completion of the respective control operation, and EPC nodes utilization figures.     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.