Analysis of error in the fixed-point implementation of two-dimensional discrete wavelet transforms

Computational error due to the fixed-point implementation of two-dimensional (2-D) discrete wavelet transform (DWT) is analyzed. This analysis is based on the exact knowledge of the DWT analysis and synthesis filters and the word length of the original image. In the fixed-point implementation, it is crucial to understand and analyze effects of finite precision in filters coefficients as well as rounding of intermediate calculations for the purpose of storage and/or transmission. Analyses and formulations are presented for both convolution and lifting approaches and they are validated by Monte Carlo simulations. The specific example used throughout this work is the lossy wavelet transformation used in the JPEG2000 compression standard.     

Speech frame recognition based on less shift sensitive wavelet filter banks

The wavelet transform possesses multi-resolution property and high localization performance; hence, it can be optimized for speech recognition. In our previous work, we show that redundant wavelet filter bank parameters work better in speech recognition task, because they are much less shift sensitive than those of critically sampled discrete wavelet transform (DWT). In this paper, three types of wavelet representations are introduced, including features based on dual-tree complex wavelet transform (DT-CWT), perceptual dual-tree complex wavelet transform, and four-channel double-density discrete wavelet transform (FCDDDWT). Then, appropriate filter values for DT-CWT and FCDDDWT are proposed. The performances of the proposed wavelet representations are compared in a phoneme recognition task using special form of the time-delay neural networks. Performance evaluations confirm that dual-tree complex wavelet filter banks outperform conventional DWT in speech recognition systems. The proposed perceptual dual-tree complex wavelet filter bank results in up to approximately 9.82 % recognition rate increase, compared to the critically sampled two-channel wavelet filter bank.     

Constraining Si Particles within Graphene Foam Monolith: Interfacial Modification for High‐Performance Li+ Storage and Flexible Integrated Configuration

Pulverization of electrode materials and loss of electrical contact have been identified as the major causes for the performance deterioration of alloy anodes in Li‐ion batteries. This study presents the hierarchical arrangement of spatially confining silicon nanoparticles (Si NPs) within graphene foam (GF) for alleviating these issues. Through a freeze‐drying method, the highly oriented GF monolith is engineered to fully encapsulate the Si NPs, serving not only as a robust framework with the well‐accessible thoroughfares for electrolyte percolation but also a physical blocking layer to restrain Si from direct exposure to the electrolyte. In return, the pillar effect of Si NPs prevents the graphene sheets from restacking while preserving the highly efficient electron/Li⁺ transport channels. When evaluated as a binder‐free anode, impressive cycle performance is realized in both half‐cell and full‐cell configurations. Operando X‐ray diffraction and in‐house X‐ray photoelectron spectroscopy confirm the pivotal protection of GF to sheathe the most volume‐expanded lithiated phase (Li₁₅Si₄) at room temperature. Furthermore, a free‐standing composite film is developed through readjusting the pore size in GF/Si monolith and directly integrated with nanocellulose membrane (NCM) separator. Because of the good electrical conductivity and structural integrity of the GF monolith as well as the flexibility of the NCM separator, the as‐developed GF/Si‐NCM electrode showcases the potential use in the flexible electronic devices.     

Automatic facial expression recognition: feature extraction and selection

In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features using different methods including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC) and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different methods.     

The sinuous antenna-A dual polarized feed for reflector-based searching systems

The sinuous antennas have a numerous applications in military and civil systems such as direction finding systems and reflector feeds due to their superior broadband characteristics and simultaneous polarization capability. In this paper, design, construction and measurements of planar sinuous antennas are investigated for 1–5 GHz frequency range. Feeding sections of this antenna are realized by using microstrip tapered baluns. We have determined the microstrip tapered balun dimensions using Ansoft HFSS-simulation program. The performances of the manufactured antenna are measured using HP vector network analyzer. Polarization patterns and return loss characteristics of the designed antenna that have been measured in anechoic chamber, are presented.     

On the necessary and sufficient requirement of a CIOQ switch to emulate an Output Queued switch

There has been much interest to emulate the behavior of Output Queued switches. The early result of such attempts was reported by Prabhakar and McKeown using the CIOQ switches with speedup factor of 4. Subsequently, Stoica and Zhang and independently Chuang et al. showed that a speedup of 2 in conjunction with their scheduling schemes would be sufficient for CIOQ switches to emulate Output Queued switches.Additionally, Chuang et al. showed that in “Average Sense” a speedup of 2?1/N is necessary and sufficient for CIOQ to emulate Output Queued switch behavior.Our paper reports that in the “Strict Sense” a speedup of 2 is both necessary and sufficient. We show this requirement using examples for 2x2 and 3x3 switches. Then, with a constructed traffic pattern, it is proved that in the “Strict Sense” a speedup of 2 is necessary to emulate the behavior of an Output Queued switch for any switch size N.Combining this result with the previous scheduling schemes, we conclude that in the “Strict Sense”, a speedup of 2 is the necessary and sufficient condition to emulate the behavior of an Output Queued switch, using a CIOQ switch.Additionally, easing the assumptions and allowing the packet segmentation, it is shown that the speedup requirement to emulate the behavior of an Output Queued switch can be reduced to values even smaller than 2?1/N. For this case a lower bound of 3/2 and an upper bound of 2 is proved.     

MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks

The hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. Markov processes are widely used in performance modelling of wireless and mobile communication systems. We address the problem of optimal wireless network selection during vertical handover, based on the received information, by embedding the decision problem in a Markov decision process (MDP) with genetic algorithm (GA), we use GA to find a set of optimal decisions that ensures the best trade-off between QoS based on their priority level. Then, we emerge improved genetic algorithm (IGA) with simulated annealing (SA) as leading methods for search and optimization problems in heterogeneous wireless networks. We formulate the vertical handoff decision problem as a MDP, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the AHP method are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithm that avoid the slow and massive computations. This paper compares the formulation and results of five recent optimization algorithms: artificial bee colony, GA, differential evolution, particle swarm optimization and hybrid of (GA–SA). Simulation results indicated that choosing the SA rules would minimize the cost function, and also that, the IGA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.     

Optimal SMDP‐Based Connection Admission Control Mechanism in Cognitive Radio Sensor Networks

Traffic management is a highly beneficial mechanism for satisfying quality‐of‐service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end‐to‐end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi‐Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs.     

Personalization of cardiac motion and contractility from images using variational data assimilation

Personalization is a key aspect of biophysical models in order to impact clinical practice. In this paper, we propose a personalization method of electromechanical models of the heart from cine-MR images based on the adjoint method. After estimation of electrophysiological parameters, the cardiac motion is estimated based on a proactive electromechanical model. Then cardiac contractilities on two or three regions are estimated by minimizing the discrepancy between measured and simulation motion. Evaluation of the method on three patients with infarcted or dilated myocardium is provided.     

Available and Waiting Times for Cognitive Radios

Cognitive radios (CRs) have been recently proposed for the problem of spectrum scarcity. The principle of CRs?? operation is based on the opportunistic access to the frequency spectrum mainly dedicated to primary users (PUs). The statistical time pattern of PUs?? channel usage and arrival can affect the usability of specific frequency bands for CRs. In this note, the effect of the arrival rate and channel holding time of PUs on the available times for CRs is analyzed. To this end, first, based on Poissonian arrivals, the available time for CRs is calculated. Then, assuming a gamma distribution for the inter-arrival times and a uniform distribution of channel holding time of PU in these intervals, the probability density function and moments of the available time for CRs are derived. Next, the effect of PUs statistical parameters on the average number of packets and the average symbol rate that a CR can transmit is analyzed. Also, taking that CR needs at least T seconds, the average waiting time is calculated.