下一代无线网络架构

无线局域网(WLAN)技术为家庭和企业用户描绘了美好的蓝图,也吸引着众多半导体和系统设备厂商涌入这一市场。WLAN技术的不断演变,将标准制订团体、互操作性论坛(互操作性是市场进一步接受的关键要求)、产品OEM厂商和相应的半导体厂商紧密联系在一起。     

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

Simultaneous optimization of design and operation of multicomponent batch distillation column—single and multiple separation duties

A method is presented for the simultaneous optimization of a batch distillation column design and its operation, for single and multiple separation duties, each involving different multicomponent mixtures and complex operations with intermediate cuts. For operation structures selected a priori, the formulation presented permits the use of general distillation design and cost models. The objective function and constraints include capital and operating cost. In particular, the number of internal plates is optimized along with the most significant operating variables (recoveries in various cuts and reflux ratio profiles and times). The multiple duty formulation presented accounts for the different importance of each duty and setup time between batches. Application of the method to single duty multicomponent separation from the literature shows that significant profit improvements can be achieved within acceptable computing times. For multiple separation duties (two binary mixtures), the method clearly shows the importance of including allocation time to each duty and setup time for each batch in the objective function.     

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.     

Adaptive Automated Teller Machines

Nowadays, Automated Teller Machines (ATMs) provide significant online support to bank customers. A limitation of ATM usage is that customers often have to wait in a queue, especially at ATMs installed at busy locations. Also, old people tend to consume more ATM usage time, possibly frustrating customers in the queue. In these situations, ATMs should “adapt” to the behavior of the customers to minimize the usage time. To this end, we apply data mining techniques to an ATM transaction dataset obtained from an international bank based in Kuwait. We pre-process this dataset, and convert it into a specific XML format to mine it through the ProM (process mining) tool. Our results reveal that customers withdraw money most frequently, followed by purchases (through an ATM card) and balance inquiry transactions. Customers re-do these transactions frequently, and also employ them one after the other. We acquire the distributions of the withdrawn amount, based on individual customers, the location (ATM terminal) and time of the withdrawl. Based on these results, we have proposed a set of five adaptive ATM interfaces, which show only frequent transactions and frequently-withdrawn amounts, display the current balance autonomously, and query explicitly for viewing purchase history, or for performing another withdrawl. An online survey on 216 ATM customers reveals that a majority of customers are willing to use these interfaces for minimizing their usage time. Our work has been approved by the banking authority of Pakistan, and we are currently implementing our interfaces for a Pakistani bank.     

Experimental quadrotor flight performance using computationally efficient and recursively feasible linear model predictive control

A new linear model predictive control (MPC) algorithm in a state-space framework is presented based on the fusion of two past MPC control laws: steady-state optimal MPC (SSOMPC) and Laguerre optimal MPC (LOMPC). The new controller, SSLOMPC, is demonstrated to have improved feasibility, tracking performance and computation time than its predecessors. This is verified in both simulation and practical experimentation on a quadrotor unmanned air vehicle in an indoor motion-capture testbed. The performance of the control law is experimentally compared with proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers in an unconstrained square manoeuvre. The use of soft control output and hard control input constraints is also examined in single and dual constrained manoeuvres.     

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance. In this research, a novel control technique-based Hybrid-Active Power-Filter (HAPF) is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor (PF) and Total–Hormonic Distortion (THD) and the performance of a system. This work proposed a soft-computing technique based on Particle Swarm-Optimization (PSO) and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods. Moreover, the control algorithms are implemented for an instantaneous reactive and active current (I_d-I_q) and power theory (Pq0) in SIMULINK. To prevent the degradation effect of disturbances on the system's performance, PS0-PI is applied in the inner loop which generate a required dc link-voltage. Additionally, a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions. The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system. Therefore, the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller (FLC) are optimal that will detect reactive power and harmonics much faster and accurately. The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI (Voltage Source Inverter), and thus the simulation has been taken in SIMULINK/MATLAB. The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation. As a result of the comparison, it can be concluded that the PSO-based Adaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.     

The carboxyl terminus of interferon-gamma contains a functional polybasic nuclear localization sequence

Cytokines such as interferon-gamma (IFN-gamma), which utilize the well studied JAK/STAT pathway for nuclear signal transduction, are themselves translocated to the nucleus. The exact mechanism for the nuclear import of IFN-gamma or the functional role of the nuclear translocation of ligand in signal transduction is unknown. We show in this study that nuclear localization of IFN-gamma is driven by a simple polybasic nuclear localization sequence (NLS) in its COOH terminus, as verified by its ability to specify nuclear import of a heterologous protein allophycocyanin (APC) in standard import assays in digitonin-permeabilized cells. Similar to other nuclear import signals, we show that a peptide representing amino acids 95-132 of IFN-gamma (IFN-gamma(95-132)) containing the polybasic sequence 126RKRKRSR132 was capable of specifying nuclear uptake of the autofluorescent protein, APC, in an energy-dependent fashion that required both ATP and GTP. Nuclear import was abolished when the above polybasic sequence was deleted. Moreover, deletions immediately NH2-terminal of this sequence did not affect the nuclear import. Thus, the sequence 126RKRKRSR132 is necessary and sufficient for nuclear localization. Furthermore, nuclear import was strongly blocked by competition with the cognate peptide IFN-gamma(95-132) but not the peptide IFN-gamma(95-125), which is deleted in the polybasic sequence, further confirming that the NLS properties were contained in this sequence. A peptide containing the prototypical polybasic NLS sequence of the SV40 large T-antigen was also able to inhibit the nuclear import mediated by IFN-gamma(95-132). This observation suggests that the NLS in IFN-gamma may function through the components of the Ran/importin pathway utilized by the SV40 T-NLS. Finally, we show that intact IFN-gamma, when coupled to APC, was also able to mediate its nuclear import. Again, nuclear import was blocked by the peptide IFN-gamma(95-132) and the SV40 T-NLS peptide, suggesting that intact IFN-gamma was also transported into the nucleus through the Ran/importin pathway. Previous studies have suggested a direct intracellular role for IFN-gamma in the induction of its biological activities. Based on our data in this study, we suggest that a key intracellular site of interaction of IFN-gamma is the one with the nuclear transport mechanism that occurs via the NLS in the COOH terminus of IFN-gamma.     

Qualified,But Not Choosing STEM at University: Unconscious Influences on Choice of Study

Abstract

This article offers explanations as to why good candidates for mathematics or physics degrees might opt to study subjects other than STEM (science, technology, engineering, mathematics) subjects at university. Results come from analysis, informed by psychoanalytic theory and practice, of narrative-style interviews conducted with first-year undergraduates and from survey data. It is argued that psychoanalytic interpretations have a role in educational research. Also, it is shown that unconscious forces influenced young peoples’ decision making. Implications for policy are discussed, in particular, the issues of (a) the role of commitment and (b) being good enough to study a STEM discipline.