Demand side management (DSM) is one of the key features of electricity networks of the future, as it is a promising way to control energy use in order to allocate more energy efficiency and stable power supplies. In addition, using DSM provides an opportunity to integrate large amount of renewable resources in the power system generation mix and promising solution for maintaining generation and demand balance in the presence of uncertainty in operation. As a result, it can reduce penalty cost due to imbalance between demand and production for the electricity market participants. This paper presents a general structure for an electricity retail market within Microgrids based on game theory concept. A combination of Nikaido-Isoda algorithm (based on non-cooperative game theory) and a relaxation algorithm (NIRA) is employed in this study (Called REM-NIRA algorithm). In the proposed approach, consumers aim to minimize market-clearing prices whilst producers aim to maximize profit through local operations and distributed generation management, energy storage resource, and responsive load demands in relation to the upstream network.
A new low-power capacitive structure and its switching scheme for Successive Approximation Register (SAR) Digital to Analog Converter (DAC) is presented. The proposed method reduces power consumption by 93% and area by 50% compared to the conventional binary-weighted DAC. Moreover, only one reference voltage is used to avoid the effect of common mode voltage (Vcm) non-idealities on precision.
There is a growing trend toward using resting-state functional magnetic resonance imaging (rs-fMRI) data in studying brain network, and finding altered brain regions in neurological and psychiatric disorders. In this paper, we investigated the brain network of 15 normal and 15 Alzheimer subjects, using rs-fMRI data. To overcome the shortcomings of anatomical atlases in functional connectivity studies, we defined the regions based on functional atlases. We produced two functional parcellations: an individual parcellation for each subject separately, and a group-wise parcellation based on the whole dataset. For each subject, two functional graphs were constructed through these atlases. Finally, common network measures such as clustering coefficient and also assortativity coefficient were extracted from the resulted graphs. Comparison between corresponding network measures in patients and normal groups indicate that assortativity coefficient is significantly lower in both group-wise atlas-driven graphs (p-value = 0.0429), and individual atlas-driven graphs (p-value = 0.0334). Reduced assortativity coefficient, which might reveal disturbed primary order in vertices' degrees, can help in better distinguishing Alzheimer's subjects from normal ones.
This paper addresses integral input-to-state stability (iISS) analysis for interconnected systems via small-gain conditions. In particular, we unify and generalise existing results for continuous-time systems and discrete-time systems to the case of hybrid systems, namely those that combine continuous time and discrete time. As an application of our results, we verify iISS for a reset control system.
: While the demand for bandwidth increases exponentially with the growing number of internet based devices, traditional TCP algorithms are turning to be sub optimal. Delay-based congestion control algorithms which are designed to overcome this challenge, are proven to be more satisfactory than loss-based congestion algorithms. On the one hand, Loss base congestion detection technique depends solely on packet loss. On the other hand delay-based algorithms have the advantage of proactively detecting congestion occurrences based on packet delays and as a result avoiding unnecessary packet loss. TCP-Vegas as the most referred variant of delay-based algorithms is promised to achieve between 40 and 70 percent better throughput. However there is some problems which are preventing TCP-Vegas to become widespread. One of these problems is lack of fairness in bandwidth allocation while delay and loss-based connection share a link. In this paper we made some modifications on the original TCP-Vegas which enabled the new algorithm to compete fairly with loss-based algorithm.
In this paper, we examine discrete memoryless Multiple Access Channels (DM-MACs) with feedback in the presence of two correlated channel states, each known to one encoder. Depending on feedback is two-sided or one-sided and also states are known to the transmitters non-causally or causally or strictly causally, we have six scenarios. Therefore, we present six achievable rate regions for these six scenarios by combining block Markov encoding, superposition coding and Gelf'and-Pinsker binning. We also show that our achievable rate region for the two-sided feedback and non-causal states subsumes Cover-Leung's achievable rate region for the DM-MAC with two-sided feedback as its special case.
In this paper, in order to increase the efficiency, to reduce the cost and to prevent the failures of wind turbines, which lead to an extensive break down, a robust fault diagnosis system is proposed for V47/660kW wind turbine operated in Manjil wind farm, Gilan province, Iran. According to the acquired data from Iran wind turbine industry, common faults of the wind turbine such as sensor faults, actuator faults and component faults are identified and considered in Fault Detection and Isolation (FDI) system design. Various Faults in abrupt and incipient natures can be detected and isolated using the indicators of faults, namely residuals, that are derived based on Unknown Input Observer (UIO) approach. Moreover, some thresholds are exploited to evaluate the produced residuals. The robustness of the proposed method against parameter uncertainties is shown as well. Simulations are performed in Matlab/Simulink environment to demonstrate the effectiveness of the proposed method using the actual parameters derived from the turbine model.
:هلاقم هصلاخ Deep learning is an appreciate framework to deal with high-level data in order to -find meaningful relationships between the features of the data. One of the well known architecture of deep learning scheme is Convolutional Neural Network (CNN) which includes one or more convolutional layers with fully connected layers. Despite of the abilities of CNN, it suffers from parameter setting where the parameters with different values have high impacts on the performance of CNN. In this paper, parameter adaptation of CNN using new metaheuristic algorithm is proposed. The used metaheuristic algorithm is Harmony Search (HS) which is improved in this paper. Finally, the proposed method which is name Adaptive Convolutional Neural Network (ACNN) is applied on handwritten digit recognition field. Experimental results on MNIST dataset prove the superiority of the proposed ACNN to CNN.
IEEE 802.15.4 is an industry standard for wireless sensor networks (WSNs) and wireless personal area networks (WPANs). This standard specifies the medium access control (MAC) and physical (PHY) layers of the OSI reference model. This paper analyzes the carrier sensing multiple access (CSMA) mechanism of the MAC layer of IEEE 802.15.4. It especially focuses on the distribution of interarrival time, which is a critical network performance metric to analyze network queue model and anomaly detection in the network. A simple but accurate analytic model is developed in order to derive the distribution of interarrival time. The analytic model deploys a discrete time Markov chain to model the behavior of network nodes. Extensive simulations are performed, using ns-2 simulator, to validate the developed analytic model. Simulation results are in good agreement with analytic results.
Software Defined Networks (SDN) are proposed as a new solution to facilitate dealing with some major network management issues. Just like other networks, SDNs are considered as a communication resource which can be shared between several tenants. However several virtualization solutions are proposed in the past to provide necessary mechanisms to slice SDN between several virtual networks, but virtualizing SDN can cause some performance degradation issues (e.g., additional packet forwarding latency). In this paper a novel network calculus based analytical model is proposed to get the upper bounds on some performance parameters of virtualized SDNs.
Recently, the relationship between muscles' electrical activity and body movements has been considered in many medical applications. In these applications, uses of non-expensive and portable of electromyography (EMG) electrodes have advantageous compared to the use of force sensors and cameras which are often very expensive and require massive structures. In this paper, we evaluate the ability of the Fast Orthogonal Search (FOS) methodology to predict jaw motion using Electromyography (EMG) signals recorded from two masticatory muscles, namely masseter and temporalis. Results show the efficiency of FOS in predicting the kinematic parameters (position and orientation) based on EMG signals. Additionally, the proposed model can be utilized to control masticatory robots employing recorded EMG signals.
Embedding time encoding into the quantizers, has been proven to be as a promising technique to overcome the data converter's resolution problems in low-voltage CMOS circuits. In this paper, an NTF-enhanced time-based continuous-time sigma delta modulator (TCSDM) with second-order noise-coupling is presented. The structure takes advantages from a combination of an asynchronous pulse width modulator (APWM) as a voltage to-time converter (VTC) and a time-to-digital converter (TDC) as a sampler to realize the time quantization. By using a novel implementation of the noise-coupling technique, the modulator's noise-shaping order is improved by two. The concept is elaborated for an NTF-enhanced second-order TCSDM and behavioral simulation results are presented to verify the performance. To further confirm the effectiveness of the structure, the circuit-level implementation of the modulator is provided in TSMC 90nm CMOS technology. The simulation results show that the proposed modulator achieves a dynamic range of 84 dB over a 30 MHz bandwidth while consuming less than 25 mW from a 1 V supply voltage. With the proposed time based noise-coupling structure, both the order and the bandwidth requirements of the loop filter can be relaxed, which in turn the analog complexity of the modulator is significantly reduced.
The broadcast nature of wireless communications makes the propagation medium vulnerable to security attacks such as eavesdropping and jamming from adversarial or unauthorized users. Applying physical layer secrecy approaches will enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without using any secret keys. However, physical layer security approaches are typically feasible only when the source-eavesdropper channel is weaker than the source-destination channel. Cooperative jamming can be used to overcome this challenge and increase the secrecy rate. In this paper, the security of two-phase relaying system with multiple intermediate nodes and in the presence of an eavesdropper is investigated. To enhance the system secrecy rate, a joint cooperative beamforming and jamming combined with relay and jammer selections is proposed. In phase I, the source node broadcasts a signal to relays while three intermediate nodes (which act as jammers) help the source node by transmitting random jamming signals to confuse the eavesdropper. Since the friendly (cooperative) jammers create interference for both the intended relays and the eavesdropper, optimal beamforming is applied such that no interference is caused at two preselected desired relays (that are going to receive the confidential massage in phase I). In phase II, two preselected relays transmit the source message with beamforming coefficients such that the received signal at the eavesdropper is completely nulled out. Our goal in this paper is to minimize the received SNR at the eavesdropper while increasing it at the destination as much as possible by applying different methods such as cooperative beamforming, cooperative jamming and relay selection. To avoid operational complexity, we consider the minimum number of intermediate nodes that are necessary without losing the performance. Numerical results demonstrate the advantage of our proposed scheme compared with the scheme with no cooperative jamming.
Linearity and efficiency are two challenging parameters in an RF power amplifier. This is because there is a trade-off between linearity of a PA and its efficiency. This can be a bottleneck in many of modern wireless systems especially those are to be designed for transmission of non-constant envelope signals. This paper presents an efficient predistortion method based on digital baseband injection of appropriate signals to the input to lower the Adjacent Channel Power Ratio (ACPR) parameter. A mathematical method is given to describe the nonlinear relationship between input and output of the PA The optimum values for the injection signals at the intermodulation frequencies are obtained. In comparison with previous works, the proposed method is relatively low cost and high efficient. Simulation results show that the proposed method is capable of remarkably reducing spectral regrowth in adjacent channels for different non-constant envelope signals. For a QPSK signal, the spectral regrowth and ACPR of the PA are decreased by 20dB and 26dB, respectively.
Fast-growing application of transcranial direct current stimulation (tDCS) as an electrotherapy technique has been motivated researchers to rationalize dosage of primary care protocol. Some important aspects in this field are pertained to modification of electrodes regarding size, position and shape of them. Recently, fractal electrodes have shown the potential to enhance neural stimulation efficiency. The purpose of current study was to address the efficacy of this newly introduced electrode on tDCS via numerical methods. An individual high resolution finite element human head model was created based on MR-scanning images. We simulated induced current density in the brain for conventional and fractal electrodes. The results demonstrate that geometry of fractal electrodes has an impact on the magnitude of current density. The peak current density for the same inward stimulus was higher (~1.3 times) for fractal electrodes in comparison with conventional type. Fractal shapes could be considered as an efficient way to provide more penetration of current density across the human brain.
In this paper, we consider a two-way cooperative communication framework to provide secure communications for secondary users within an Orthogonal Frequency-Division Multiple Access (OFDMA) based underlay cognitive network. By proposing a radio resource allocation problem with the aim of maximizing the secrecy sum-rate of secondary users and solving it, we show that deployment of relays is vital to achieve a non-zero secrecy sum rate. Also, impact of two-way relay in system performance improvement is clearly visible in comparison with one-way relay such that it can roughly double the resulting system secrecy sum-rate. The impact of different system parameters on the achievable secrecy sum rate for both one-way and two-way relay is also investigated and compared through simulations.
An adaptive nonlinear control based on separation principle is designed for a second-order mechanical system which incorporates Coulomb frictional effect. Due to the nonlinearity of the system, this modularity is made possible by a strong input-tostate stability (ISS) property of the ISS controller with respect to the parameter estimation error as input. This input is guaranteed to be bounded by the passive identifier. We employ a passive identifier that uses the observer with passive error system and unnormalized gradient-type update law which is called x-scheme identifier. It is based on the close loop plant model. This design is more flexible than the Lyapunov-based design and lead to lower control effort. The enhancement of the transient performance of system is achieved with trajectory initialization technique. Simulation results show the validity and efficiency of the proposed friction compensator for the position tracking control under the influence of Coulomb friction.
This paper focuses on adaptive output feedback tracking control of nonlinear systems, in which the functions of system are unknown. A two-layered feedforward Neural Network (NN) is employed to approximate the desired control signal. In order to estimate system states, an NN-based high gain observer is introduced, which does not suffer from peaking phenomenon. Also, the stability analysis of the overall system is provided based on the non-separation principle. As compared to most of the previous approaches which concentrate on affine systems, the presented method is applicable to nonaffine nonlinear systems. Furthermore, the presented method does not rely on having a lot of a priori knowledge about the system dynamics, the corresponding adaption laws are simple, easy to implement and reliable. Finally, simulation results are presented to verify the significant potential of the proposed controller.
This paper presents an introduction of factorial speech processing models for noise-robust automatic speech processing tasks. Factorial models try to use more noise information rather than other robustness techniques for better generative modeling of speech and noise and the way they are combine together. Since factorial models were not completely successful in noise-robust speech processing applications while they have significant achievements in other speech processing areas in the past, we decide to reconsider them and evaluate their effects in the Aurora 2 task. In addition to Aurora noises, two more regular noises are examined in our experiments including Helicopter and Locomotive engine noises. Experiments show that these models are successful when we faced with destructive noises in addition to their unexpected improvements for non-regular non-stationary noises like Babble.
