Speech endpoint detection is one of the key problems in the practical application of speech recognition system. In this paper, speech signal contained chirp is decomposed into several intrinsic mode function (IMF) with the method of ensemble empirical mode decomposition (EEMD). At the same time, it eliminates the modal mix superposition phenomenon which usually comes out in processing speech signal with the algorithm of empirical mode decomposition (EMD). After that, selects IMFs contained major noise through the adaptive algorithm. Finally, the IMFs and speech signal contained chirp are input into the independent component analysis (ICA) and pure voice signal is separated out. The accuracy of speech endpoint detection can be improved in this way. The result shows that the new speech endpoint detection method proposed above is effective, and has strong anti-noises ability, especially suitable for the speech endpoint detection in low SNR.

String matching is a key problem in many network processing applications. Current implementations of this process using software are time consuming. This paper presents a string matching system that based on FPGA. This paper uses DM9000A to receive network data and uses Snort rule and HashMem function to match pattern. With software simulation the conflict pattern of Snort rules found out and processed separately. In the system, conflict can be high-speed solved. With the PC handle string matching that more than 16 Byte, the FPGA processing speed has improved greatly .The experimental results show that the system throughput is 1.22Gbps, more than 20 times of the software method. When processing more Snort rules system throughput is not affected. Experimental results show that the system can quickly adapt to the demand for hardware reconfiguration and meet the network application requirements.

This paper presented a high real time target tracking algorithm – mixed difference tracking algorithm MDT. In the proposed algorithm, frame difference and background difference algorithms are combined to get the location of the target. With background difference algorithm the shape of the target can be extracted. Due to the affection of the dynamic background, single background difference algorithm can not get the location of the moving target. To solve this issue the frame difference algorithm is used to estimate the location, and then combine the results of the background difference algorithm and the frame difference algorithm the location and the size of the target can be extracted. And then filtering algorithm is used to remove noise and isolated points. In the experiment it can be seen that the proposed algorithm can tracking object precisely in real time.

Intrusion detection for network security is an application area demanding high throughput. The pattern matching in intrusion detection requires extremely high performance to process string matching. Most of pattern matching using software has many time complexities and cannot reach the requirements of high throughput. The pattern matching using hardware considerably improves the speed of matching and has several other advantages. This paper describes a FPGA-based pattern matching architecture, using hashing method called XOR Hashing. The proposed method updates new patterns without reconfiguration and processes the collision and has high matching performance. The proposed system implements the pattern matching by using Snort rule-set, an open source Network Intrusion Detection and has simulation processing on PC. Compared with existing hardware method, the results explained that our method has relatively high performance for the pattern matching and can else process the pattern matching with high performance on low–cost FPGA device.