Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.     

Exploratory learning structures in artificial cognitive systems

The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning.In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception–action cycles. We present a system diagram which explains this process in more detail.We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (’teacher’) and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems.We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.     

Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education

In this paper, several recent theoretical conceptions of technology-mediated education are examined and a study of 2159 online learners is presented. The study validates an instrument designed to measure teaching, social, and cognitive presence indicative of a community of learners within the community of inquiry (CoI) framework [Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2, 1–19; Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23]. Results indicate that the survey items cohere into interpretable factors that represent the intended constructs. Further it was determined through structural equation modeling that 70% of the variance in the online students’ levels of cognitive presence, a multivariate measure of learning, can be modeled based on their reports of their instructors’ skills in fostering teaching presence and their own abilities to establish a sense of social presence. Additional analysis identifies more details of the relationship between learner understandings of teaching and social presence and its impact on their cognitive presence. Implications for online teaching, policy, and faculty development are discussed.     

Home-based communication system for older adults and their remote family

As in many Latin–American countries, in Mexico many older adults live alone as a result of the migration of one or more of their relatives, mostly to the USA. Thus, not only do they live alone, but they might seldom see these relatives for long periods, even though they often depend on them financially. With the goal of designing appropriate communication technology for seniors and their relatives experiencing this situation, we conducted interviews and evaluated scenarios and prototypes to reveal the practical ways they maintain emotional ties despite the distance. Based on those findings, we envisioned a communication system through which seniors and their relatives can maintain close social ties by sharing information, personal reminiscences and stories. We found that older adults perceived the system as a richer, natural form of communication with their relatives that could facilitate their integration into the networks that currently connect members of their families.     

On neighbor discovery in cognitive radio networks

A cognitive radio node is a radio device capable of operating over multiple channels. As a result, a network consisting of one or more cognitive radio nodes can adapt to varying channel availability in its geographical region by dynamically changing the channel (or channels) nodes are using for communication.     

Robot learning language — Integrating programming and learning for cognitive systems

One central property of cognitive systems is the ability to learn and to improve continually. We present a robot control language that combines programming and learning in order to make learning executable in the normal robot program. The language constructs of our learning language RoLL rely on the concept of hierarchical hybrid automata to enable a declarative, explicit specification of learning problems. Using the example of an autonomous household robot, we point out some instances where learning–and especially continued learning–makes the robot control program more cognitive.     

认知网络中基于用户需求的功率控制算法研究*

为了解决认知网络功率控制问题,根据非合作博弈论和干扰温度,设计出一种新型效用函数,该函数综合考虑授权用户和认知用户功率分配的要求,满足用户需求。并通过此效用函数提出一个基于非合作博弈论的新颖功率控制算法,分析了该算法的收敛性、纳什均衡解的存在性和唯一性。仿真结果表明该算法在保证认知用户需求前提下,传输功率更加合理,体现出算法的有效性和公平性。与参考算法相比,节省30.12%的功耗,并且具有较好的抗背景噪声。最后,与KG算法相比,该算法具有较大的网络容量。     

基于多种群进化与粒子群优化混合的频谱分配算法

为了解决认知无线网络中的频谱分配问题,提出一种基于多种群进化与粒子群优化混合的频谱分配算法。它采用图论着色模型,首先使用遗传算法将多个种群进行独立进化,以提高种群的全局搜索能力;然后选出每个种群中的最优的个体作为粒子群优化的粒子,并通过控制每个粒子的初始速度方向来加快算法的收敛速度。最后以系统总收益最大化和用户间的公平性为优化目标与遗传算法和粒子群算法进行了对比实验,仿真结果表明,该算法在收敛速度、认知用户接入公平性和系统总收益3个方面的性能均优于遗传算法和粒子群算法。     

一种联盟博弈频谱感知机制

为了在主用户具有异构性且认知用户处于多路径衰减、阴影效应等低感知性能的认知环境中,使认知用户公平有效地进行频谱感知,提出一种联盟博弈频谱感知机制,将频谱感知问题建模为效用不可转移的联盟博弈。首先,将认知用户划分成多个子联盟,根据头结点选取原则选出各个子联盟的头结点;其次,根据规则进行子联盟合并或分裂;最后,子联盟头结点利用“OR”规则进行数据融合,得到最终感知结果并广播给子联盟内的所有认知用户。仿真实现和性能评价结果表明,该机制具有好的性能。     

Predictive capability of cognitive ability and cognitive style for spaceflight emergency operation performance

This study explores the effects of cognitive ability (information seeking, inference, spatial recognition, attention span, and attention allocation) and cognitive style (active-reflective, sensing-intuitive, visual-verbal, and sequential-global) on task performance of simulated spaceflight emergency operations that require judgment and operation on a Chinese spaceflight instrument board and the possible interaction effect with training experience. The performance criteria included task completion time and number of human errors. It was found that inference ability, spatial recognition ability, and attention span had significant effects on task completion time, while attention allocation ability had significant effect on the number of error. The participants with a sequential cognitive style made significantly fewer errors than those with a global cognitive style. Training experience significantly decreased task completion time. The participants with sequential cognitive style learnt faster than those with global cognitive style in the spaceflight instrument operations. With increasing training experience, the predictive capability of cognitive ability on performance decreased, whereas the predictive capability of the sequential-global cognitive style on performance increased.