This paper reviews the development and application of HHT in the field of SHM in the last two decades. The challenges and future trends in the development of HHT based techniques for the SHM of civil engineering structures are also put forward. It also reviews the basic principle of the HHT method, which contains the extraction of the intrinsic mode function (IMF), mechanism of the EMD, and the features of HT; shows the application of HHT in the system identification, which contains the introduction of theoretical method, the identification of modal parameters, and the system identification on real structures; and discusses the structural damage detection using HHT based approaches, which includes the detection of common damage events, sudden damage events, and cracks and flaws.

The accelerated growth of the molecular sequencing data has generated a pressing need for advanced sequence annotation tools. This paper reports a new method, termed MOTIFIND (Motif Identification Neural Design), for rapid and sensitive protein family identification. The method is extended from our previous gene classification artificial neural system and employs two new designs to enhance the detection of distant relationships. These include an n-gram term weighting algorithm for extracting local motif patterns, and integrated neural networks for combining global and local sequence information. The system has been tested with three protein families of electron transferases, namely cytochrome c, cytochrome b and flavodoxin, with a 100% sensitivity and more than 99.6% specificity. The accuracy of MOTIFIND is comparable to the BLAST database search method, but its speed is more than 20 times faster. The system is much more robust than the PROSITE search which is based on simple signature patterns. MOTIFIND also compares favorably with the BLIMPS search of BLOCKS in detecting fragmentary sequences lacking complete motif regions. The method has the potential to become a full-scale database search and sequence analysis tool.

A protein class (ProClass) database is developed as a "value-added" "second-generation" database organized according to family relationships. The database collects non-redundant protein sequence entries from SwissProt and PIR databases, and classifies them in families defined collectively by the ProSite protein groups and PIR superfamilies. The major objectives of the database are to maximize family information retrieval, to provide speedy family identification, and to help organizing existing protein sequence databases. The database has two sub-databases: PCFam (ProClass Family) to define protein families and provide links to ProSite patterns and PIR superfamilies, and PCSeq (ProClass Sequence) to describe sequence entries and provide links to PCFam, SwissProt, PIR, and ProSite databases. The current ProClass release has a total of 85,165 sequence entries, about half of which are classified in 3072 ProClass families; it also contains 10,431 newly established SwissProt-PIR links. The database can help reveal domain structures of related families, define new ProSite and PIR families, and provide family assignments for unclassified sequence entries. New ProSite and PIR family members are readily identified via database cross-reference, including 9437 SwissProt entries and 8522 PIR entries. False negative family members missed by both ProSite and PIR are detected using a neural network family identification system. The newly identified superfamily memberships are being incorporated into the current PIR database releases in a collaborative effort with the PIR. The ProClass database is accessible through anonymous FTP and on-line search on the World Wide Web.

A new lightweight stream cipher, SVH, is proposed. The design targets hardware environments where gate count, power consumption and memory is very limited. It is based on dual pseudo-random transformation and output feedback. The block of key size is 64 bits and SVH can achieve sufficient security margin against known attacks, such as linear cryptanalysis, differential cryptanalysis, impossible differential cryptanalysis. Hardware implementation of SVH is around 1171GE, which is comparable with the 1458 GE hardware implementation of Grain. The software implementation of SVH on 8-bit microcontroller is about 19.55Mb/s, and its efficiency is 30 times as much as that of Grain in RFID environment. The hardware complexity and throughput compares favourably to other hardware oriented stream ciphers like Grain.

At present, the environmental problems in the water are becoming more and more prominent, and many teams are working on the research of the automation of garbage collection at sea. In order to a ready-made sea on the garbage recycling equipment automation, intelligent recognition for all white plastic and in identifying regional planning the path of the recycling of recycling equipment, aiming at different times during the day under the sea lighting situation, puts forward a visual processing and multi-step sea boundary identification method. After the image is processed by using two peak method normalized Canny boundary scan and other methods, the vertical and horizontal scanning and boundary recognition are carried out to obtain the closed Marine garbage main body area. Finally, Zigzag algorithm is used to carry out recovery path planning in the scanned area, and the results under different search radii are compared and analysed. The experimental results show that this method is applicable to a wide range of applications and has high accuracy and practicability, and the recognition results are satisfactory.