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In the process of hypothermic chemotherapy, the small difference of the liquor temperature would make a direct influence on the treatment effect of the sick. Therefore, it requires that the thermal medicine filler has higher precision of temperature control. The diversity of individual patient and their conditions causes that the control process could not establish an accurate mathematical model. By this reason, the combination of intelligent fuzzy control algorithm and the traditional PID control algorithm could give full play to their advantages, solve problems better and even achieve an accurate effect of temperature control.

In order to improve the signal to noise ratio of image, a local adaptive Wiener filter in wavelet domain is applied to a 2-D image. Several 1-D windows could be constructed on the direction character of sub-image in the filter window after wavelet transform. Then the adaptive filters could be obtained from the 1-D windows. With the effective weighted combination of filters in different 1-Dwindows, the sub-image local adaptive filters are generated. And the simulation of the adaptive filter shows it could remove the white noise of image and preserve the edge of images effectively.

In this paper we design a Chaos Bidirectional Associative Memory (CBAM) network model based on Aihara chaos neuron for its sensitivity to noise. It includes an inside associative memory construct on its every layer. The simulation shows that the model improves the anti-noise ability of the BAM effectively.

The situation of delay network is complex. To adjust the image coding compression ratio based on real-time network conditions can make the image transmission smooth. EZW coding is a kind of coding that supporting for progressive transmission, and suitable for network transmission. In order to adjust compression ratio according to network congestion situation and improve image transmission speed on the network, this paper proposes a EZW coding algorithm that can adjust compression rate according to TRR, a key network conditions parameter. The algorithm allows images to be transmitted on the network in quick succession. Experimental results show that the algorithm is feasible.

The platform is used to collect related information about human body motion timely and accurately, so that more penetrating scientific study will be carried out about human body motions. It utilizes multiple-knot network technology to gather multiple-point accelerations information which can be processed or transmitted to advanced computers to be comprehensively analyzed. The platform has already applied successfully in computer games' operation and got good comments from the users who has tried to use it. Numerous experiment data indicate that the platform gather more accurate information about human body motions which reflect truthfully human body motions. So deeper development based on this platform can be applied in fields such as body recovery training, physical exercises, computer games' controlling.

On the basis of the characteristic and requirement of the continued hyperthermic celiac perfusion (CHCP) temperature process. A model is set up, the generalized predictive self-tuning control (GPSC) algorithm is used to control the whole system to get high accuracy. The parameters identification and the design of generalized predictive self-tuning controller have been realized. Experimental result shows that this method can satisfy the requirement of fast response and high accuracy. The algorithm also has the characteristic of fast convergence, better stability and robustness.

Part perfusion hyperthermia is an effective approach to tumor curing through which the tumor cells are killed by the high temperature, which is created by medicament perfuse to the pathological change area. Since the human body is a complicated system of heat exchange. It is difficult to obtain its accurate mathematic model and accurately measure the temperature of cancer tissue applying the conventional method. The paper proposed a novel intelligent modeling and measuring scheme based on RBF neural network, which can conduct the accurate measurement of the temperature of cancer tissue. The neural network model is designed and the training algorithm with high-precision of learning is formulated based on neural networks techniques. Compared with the conventional scheme, the proposed intelligent scheme has the following advantage: good robustness and adaptive ability for different cancer tissues, simple implementation and high-precision. A lot of numerical simulations have been conducted, and simulation results have shown that the method is feasible and efficient.