Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Design and evaluation of an active electromechanical wheel suspension system

This paper presents an electromechanical wheel suspension, where the upper arm of the suspension has been provided with an electric levelling and a damper actuator, both are allowed to work in a fully active mode. A control structure for the proposed suspension is described. The complex design task involving the control of the electric damper and its machine parameters is tackled by genetic optimisation. During this process, these parameters are optimised to keep the power dissipation of the electric damper as low as possible, while maintaining acceptable comfort and road-holding capabilities. The results of the evaluations carried out demonstrate that the proposed suspension can easily adopt its control parameters to obtain a better compromise of performance than that offered by passive suspensions. If the vehicle is to maintain acceptable performance during severe driving conditions, the damper has to be unrealistically large. However, if the electric damper is combined with a hydraulic damper, the size of the electric damper is significantly reduced. In addition, the design of the electric damper with the suggested control structure, including how it regenerates energy, is discussed.     

TFT有源矩阵α－Si∶H薄膜PECVD淀积工艺研究

分析了α－Ｓｉ∶Ｈ薄膜的质量和厚度对α－Ｓｉ∶ＨＴＦＴ关键性指标的影响，深入、详细地讨论了其ＰＥＣＶＤ淀积工艺，并在实验的基础上确定了最佳淀积工艺参数，从而获得了高性能的７５ｍｍ３７２×２７６像素α－Ｓｉ∶ＨＴＦＴ有源矩阵     

Histidine in enzyme active centers.

The presence of histidine in the active center of an enzyme can be demonstrated by kinetic measurements, chemical modification, NMR spectroscopy or X-ray structure analysis. Histidine is the only naturally occurring amino acid to contain an imidazole residue as a side chain. The role of histidine in enzyme catalysis depends, inter alia, upon the special features of the imidazole residue: it thus tends to form hydrogen bonds, combines donor and acceptor properties and can take part in either nucleophilic or base catalysis. In some of these enzymes the position of each atom is known; however, the theories as to how the catalysis proceeds at a molecular level are controversial.     

SYNTHESIS AND CHARACTERIZATION OF PHTHALAZINONE POLY(ARYL ETHER SULFONE KETONE)WITH CARBONYL GROUP*

A kind of novel heat-resistant, high performance engineering thermoplastic phthalazinone poly(aryl ether sulfoneketone) (PPESK) containing a carboxyl group in its side chain was prepared by the nucleophilic displacement reaction of 4-(4-hydroxylphenyl)-1(2H)-phthalazinone with di(4-chlorophenyl) sulfone, 4,4'-difluoro-benzophenone and phenolphthalin insulfolane in the presence of K_2CO_3 to produce high molecular weight polymers which can be dissolved in some polarsolvents such as chloroform and nitrobenzene at room temperature and can be easily can into flexible, yellowish andtransparent films. PPESK is an amorphous polymer having a decomposition temperature above 400℃, which indicates that ithas high thermal stability. At the same time, the thermal properties of PPESKs with dicyandiamide (DICY) as curing agentindicated that the heat-resistance properties of the PPESKs are improved after curing. The apparent activation energy (ΔE) ofthe cross-linking reaction and the reaction order (n) of PPESK/DICY were found to be 52.2 kJ/mol and ca. 1.0, respectively.Therefore, the cross-linking reaction is approximately a first order reaction.     

Active transport of Na + and K + through animal cell membranes

The transport of Na⁺ out of the cell and K⁺ into the cell against a concentration gradient is catalyzed by a (Na⁺ + K⁺)-activated ATPase. The way in which the cations pass through the cell membrane has not yet been elucidated. Studies on the ATP hydrolysis revealed a Na⁺-dependent phosphorylation of the enzyme protein; the conformation of the enzyme also appears to change. The energy required for transport of the cations against their concentration gradients is probably provided by K⁺-dependent hydrolysis of the enzyme-bound phosphate. The enzyme can synthesize ATP from inorganic phosphate and ADP on reversal of the cation concentration gradient. By keeping the enzyme in a particular conformation, the cardiac glycoside ouabain specifically inhibits the Na⁺ pump.     

能动抛光磨盘的变形实验研究

 能动抛光磨盘实时产生不同的表面变形，在对大口径非球面光学元件进行精磨和抛光时实现与工件表面良好的大尺寸吻合，可以消除传统光学加工采用小尺寸磨头时带来的高频残余误差并提高加工效率。以加工直径1.3m左右，F/2的抛物面光学元件为例，对能动磨盘在不同离轴度时能够产生的变形进行了计算和实验。结果表明，能动磨盘能够以较高精度产生旋转对称或非对称的抛物面形状。对能动磨盘产生变形后的残余误差进行了分析。     

模拟电子技术课程的PBL选题设计与思考

PBL模式强调把学习设置于复杂的、有意义的问题情境中,是一种以自主学习为特色的教学模式。介绍了在重庆大学UC学院的 “Electronics” 全英文课程中开展PBL教学模式的实施过程,特别讲述了对问题设计的选择与思考。从教学观察和学生反馈来看,PBL模式有助于学生以点带面的知识建构,培养学生的自主学习、分工协作解决问题的能力和素质。     

Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals

In recent decades, emotion recognition has received considerable attention. As more enthusiasm has shifted to the physiological pattern, a wide range of elaborate physiological emotion data features come up and are combined with various classifying models to detect one’s emotional states. To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA) architecture with unsupervised pre-training, followed by supervised fine-tuning. In this paper, we compare the performances of different features and models through three binary classification tasks based on the Valence-Arousal-Dominance (VAD) affection model. Decision fusion and feature fusion of electroencephalogram (EEG) and peripheral signals are performed on hand-engineered features; data-level fusion is performed on deep-learning methods. It turns out that the fusion data perform better than the two modalities. To take advantage of deep-learning algorithms, we augment the original data and feed it directly into our training model. We use two deep architectures and another generative stacked semi-supervised architecture as references for comparison to test the method’s practical effects. The results reveal that our scheme slightly outperforms the other three deep feature extractors and surpasses the state-of-the-art of hand-engineered features.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.