The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments.

In this paper, we describe a system for online detecting MP3 music transmissions. Packet mirroring technique is used for capturing the MP3 packets transmitted over the Internet. A multi-resolution local description for the captured MP3 fragment is computed for fast identifying and aligning possible tones within it. Four kinds of MP3 features, i.e., MDCT, MFCC, MPEG-7, and chroma vectors are extracted from the MP3 bitstreams for computing the MP3 fingerprints for further precisely matching. To efficiently search for an unknown MP3 fragment in the high dimensional feature space, an indexing scheme, which utilizes the PCA technique, the QUC-tree, and the MP3 signatures, is proposed. Experiments are performed on the RWC music database to show the robustness and efficiency of the proposed MP3 sniffer system.

In this paper, we describe an efficient and robust MP3 fingerprinting system. Four kinds of MP3 features, i.e., MDCT, MFCC, MPEG-7, and chroma vectors are extracted from the compressed bitstream of the MP3 fragments. To efficiently search for an unknown MP3 fingerprint in the high dimensional feature space, an indexing scheme which is composed of the PCA technique and the QUC-tree structure is applied. Inverted lists of MP3 signatures are also constructed to solve the problem of subspace overlapping. Experiments are performed on the RWC music database to show the efficiency and robustness of the proposed MP3 fingerprinting system.

We propose an approach to summarize MP3 music objects automatically. The MP3 music summary is constructed in three steps. First, coefficients extracted from the output of polyphase filters are used to compute the MP3 features. Based on these features, an MP3 music object can be automatically segmented into a sequence of MP3 phrases. Then, the phrase clustering technique is applied to group similar phrases into a cluster. The phrases in a cluster can be considered as repeating patterns in the MP3 song. Finally, the RP-tree algorithm is applied to find the nontrivial repeating patterns such as chorus, refrains, themes, and motives of the song. Experiments are also performed and analyzed to show the effectiveness of the proposed method.

As there is a growing amount of MP3 music data available on the Internet today, the problems related to music classification and content-based music retrieval are getting more attention recently. In this paper, we propose an approach to automatically classify MP3 music objects according to their singers. First, the coefficients extracted from the output of the polyphase filters are used to compute the MP3 features for segmentation. Based on these features, an MP3 music object can be decomposed into a sequence of notes (or phonemes). Then for each MP3 phoneme in the training set, its MP3 feature is extracted and used to train an MP3 classifier which can identify the singer of an unknown MP3 music object. Experiments are performed and analyzed to show the effectiveness of the proposed method.

A video query model based on the content of video and iconic indexing is proposed. We extend the notion of two-dimensional strings to three-dimensional strings (3D-Strings) for representing the spatial and temporal relationships among the symbols in both a video and a video query. The problem of video query processing is then transformed into a problem of three-dimensional pattern matching. To efficiently match the 3D-Strings, a data structure, called 3D-List, and its related algorithms are proposed. In this approach, the symbols of a video in the video database are retrieved from the video index and organized as a 3D-List according to the 3D-String of the video query. The related algorithms are then applied on the 3D-List to determine whether this video is an answer to the video query. Based on this approach, we have started a project called Vega. In this project, we have implemented a user friendly interface for specifying video queries, a video index tool for constructing the video index, and a video query processor based on the notion of 3D-List. Some experiments are also performed to show the efficiency and effectiveness of the proposed algorithms.

In recent years, the searching and indexing techniques for multimedia data are getting more attention in the area of multimedia databases. As many research works were done on the content-based retrieval of image and video data, less attention was received to the content-based retrieval of audio data. In this paper, we propose an approach to retrieve MP3 music objects based on their content. In our approach, the coefficients extracting from the output of the polyphase filters are used to compute the MP3 features for indexing the MP3 objects. We also propose an MP3 similarity measuring function to provide users the ability to approximately retrieve the desired MP3 objects. Experiments are performed and analyzed to show the efficiency and the effectiveness of the proposed method.

A repeating pattern in music data is defined as a sequence of notes which appears more than once in a music object. The themes are a typical kind of repeating patterns. The themes and other nontrivial repeating patterns are important music features which can be used for both content-based retrieval of music data and music data analysis. In this paper, we propose two approaches for fast discovering nontrivial repeating patterns in music objects. In the first approach, we develop a data structure called correlative matrix and its associated algorithms for extracting the repeating patterns. In the second approach, we introduce a string-join operation and a data structure called RP-tree for the same purpose. Experiments are performed to compare these two approaches with others. The results are further analyzed to show the efficiency and the effectiveness of our approaches.

Proposes an approach for the fast discovery of all non-trivial repeating patterns in music objects. A repeating pattern is a sequence of notes which appears more than once in a music object. The longest repeating patterns in music objects are typically their themes. The themes and other non-trivial repeating patterns are important musical features which can be used both for content-based retrieval of music data and for music data analysis. We present a data structure called an RP-tree (repeating pattern tree) and its associated algorithms for the fast extraction of all non-trivial repeating patterns in a music object. Experiments are performed to compare this method with related approaches. The results are further analysed to show the efficiency and effectiveness of our approach.

As the progress in cellular communications, video transmission over wireless links is getting more important. To achieve this, one of the most important problems have to be solved is to protect video quality against burst errors due to channel fading and jamming. In this paper, an ARQ scheme called Multi-ARQ and its associated rescheduling method for transmitting video streams over wireless channels are proposed. Based on the concept of unequal protection, we use ARQ-scheme with higher reliability for higher sensitive portions of video data. Thus the overall video quality is well protected against transmission errors in wireless channel and the delay and buffer size needed for possible retransmission can be minimized. The proposed approach can also be combined with the channel coding approach and the error concealment approach to get better video quality.

We propose an approach for building an object front-end on top of a relational database. The object front-end can be used as the interface for integration with other object databases. The users are allowed to access data stored in the relational database using an object query language. A set of derivation rules is provided to construct an object view from the relational schema. Transformation strategies from object queries to corresponding relational queries are also presented. This object front-end has been implemented in a heterogeneous database project.>