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Convex Analysisの二,三の進展について

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

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Flow The Psychology Of Optimal Experience

Thank you very much for downloading biomechanics and motor control of human movement. Maybe you have knowledge that, people have search hundreds times for their favorite books like this biomechanics and motor control of human movement, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.

Biomechanics And Motor Control Of Human Movement

In this paper, an sEMG-driven musculoskeletal model of human shoulder and elbow joints is built based on time delay neural network (TDNN). Six principal muscles of the upper arm and forearm are included, and the experiment was conducted under isometric contractions with the aid of a planar haptic interface. Both force amplitude and direction were regulated continuously, and the experiment results proved the effectiveness and performance of this modeling method. The model was proved to have less overfitting risk than the most-used basic multilayer forward networks, and the isometric model was proved to be still effective in estimation of slow movement cases.

An sEMG-driven musculoskeletal model of shoulder and elbow based on neural networks

In this paper, a strapdown inertial navigation system (SINS) error model is introduced, and the model observability is analyzed. Due to the weak observability of SINS error model, the azimuth error can not be estimated quickly by Kalman filter. To reduce the initial alignment time, a neural network method for the initial alignment of SINS on stationary base is presented. In the method, the neural network is trained based on the data preprocessed by a Kalman filter. To smooth the neural network output data, a filter is implemented when the trained neural network is adopted as a state observer in the initial alignment. Computer simulation results illustrate that the neural network method can reduce the time of initial alignment greatly, and the estimation errors of misalignment angles are within a satisfied range.

Application of neural network to the alignment of strapdown inertial navigation system

Intelligent systems for condition monitoring, alarm diagnosis and fault detection have been widely used in modern industrial manufacturing, as they are capable of providing more efficient decision making support. This paper proposes a distributed intelligent monitoring system based on the embedded technology. Using advanced RISC machines microprocessor embedded with a real-time multi-task micro kernel, the developed system possesses the functions such as data acquisition, signal preprocessing, mass storage, realtime monitoring, and remote intelligent control. In virtue of a wireless CDMA communication network, it is especially appropriate to be applied to the circumstance where building a wired network is not feasible. Design and implementation about the hardware and software of the system are presented in detail. The high performance, reliability and security of the system have been validated in the case of remote intelligent monitoring system in industrial oil exploration and production. Due to its flexible structures of both software and hardware, the developed system is easy to be adapted to other applications.

Intelligent Monitoring System Based on the Embedded Technology: A Case Study

Negotiation protocol is the basic principle in the electronic commerce, this principle have a direct impact on the efficiency of the negotiation. This study brings up the communication architecture with negotiation protocol on the Semantic Web. Precisely speaking, agents make computing with Ontology, and we define an agent's communication ontology for this communication framework. Semantic web use Ontology to describe the negotiation protocol, which will enable agent gain the necessary knowledge from the market. This will give agents the ability to understand each other enough to carry out their objectives. In this way, buyer or seller is able to improve semantic cognitive in process of negotiation. At the same time, it can provide an intelligent platform for the information exchange on the same understanding about the content of communication in the electronic negotiation service.

Adopting Ontology and agent in electronic negotiation service

In this paper, we address the problem of target tracking control for mobile robots with limited sensing range. An end-to-end Gaussian process regression learning control method is proposed to transfer the human control experiences to the controller. The end-to-end learning architecture directly learns the control mapping from the original sensing input space to the final control output space in an human-like manner. The non-parametric Gaussian process regression accurately transfers the complex human control experiences to the control model. In addition, realistic training data set are collected from human operators for the end-to-end control learning. The performance of the proposed target tracking control method is extensively evaluated on various real-world scenarios, experimental results have demonstrated the robustness, accuracy, and effectiveness of the proposed method.

Zeng-Guang Hou

Papers

An sEMG-driven musculoskeletal model of shoulder and elbow based on neural networks

Application of neural network to the alignment of strapdown inertial navigation system

Intelligent Monitoring System Based on the Embedded Technology: A Case Study

Adopting Ontology and agent in electronic negotiation service

Move like humans: End-to-end Gaussian process regression based target tracking control for mobile robots