This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multifrequency dither, and dictionary-based waveform recognition for target detection and classi...
As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling me...
Self-localization is a crucial requirement for visual robot place recognition. Particularly, the 3D point cloud obtained from 3D laser rangefinders (LRF) is applied to it. The critical part is the efficiency and accuracy of place recognition of visua...
The performance of pattern classification is affected significantly by feature selection. However, for security applications, selecting proper features is difficult, as malicious software continuously changes its characteristics. Thus, compression-ba...
In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can ef...
This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects x and y through the compression ratio to compress x with y (’s dictionary). Specifically, we focu...
The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected view...
Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air unit...
This study utilized eye-tracking technology to explore the role of visual word segmentation cues in Tibetan reading, with a particular focus on the effects of dictionary-based and psychological word segmentation on reading and lexical recognition. Th...
Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries...
One of the primary challenges in Electroencephalogram (EEG) emotion recognition lies in developing models that can effectively generalize to new unseen subjects, considering the significant variability in EEG signals across individuals. To address th...
In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extrac...
Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity bound...
Biomedical Named-Entity Recognition (BioNER) has become an essential part of text mining due to the continuously increasing digital archives of biological and medical articles. While there are many well-performing BioNER tools for entities such as ge...
This paper presents the RaDICAL monostatic passive radar framework for target detection and localization using a sparse uniform circular array (SUCA), multifrequency dither, and dictionary-based waveform processing. Rather than forming conventional s...
Airport Economic Zones (AEZs) in China are largely guided by central and local government planning, and official planning documents provide important textual signals of industrial priorities. To identify these priorities, this study examines 17 natio...
This paper presents LinguoNER, a practical and extensible framework for bootstrapping Named Entity Recognition (NER) in extremely low-resource languages, demonstrated on Yambeta, a Bantu language spoken by a minority community in Cameroon. Due to sca...
Current accounting standards do not allow recognition of intangible assets for indigenously created properties, resulting in a discrepancy between the book value and market value of firms operating within digital economies, where investments like cyb...
Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. H...
Social media posts by individuals affected by disasters and their relatives provide a significant source of data for identifying emergencies and needs, assessing the situation, and determining affected areas. These posts often contain not only text b...
Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersectio...
This study aims to solve the problem of named entity recognition of complex mechanical equipment faults, especially the problems of many professional terms, long sentences, fuzzy entity boundaries, entity nesting, and abbreviation ambiguity, in mine...
The dictionary learning algorithm has been successfully applied to electronic noses because of its high recognition rate. However, most dictionary learning algorithms use l0-norm or l1-norm to regularize the sparse coefficients, which means that the...
In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively...
Many intensive care unit bedside devices lack interoperable digital outputs, so clinically important readings are transferred into records by intermittent manual charting. Camera-based optical character recognition (OCR) offers an interface-independe...
This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed whi...
Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking...
Due to their individual advantages, the integration of lexicon information and pre-trained models like BERT has been widely adopted in Chinese sequence labeling tasks. However, given their high demand for training data, efforts have been made to enha...
In recent years, the non-invasive load monitoring (NILM) method based on sparse coding has shown promising research prospects. This type of method learns a sparse dictionary for each monitoring target device, and it expresses load decomposition as a...
This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, w...
Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative le...
The noise suppression method based on dictionary learning has shown great potential in magnetotelluric (MT) data processing. However, the constraints used in the existing algorithm’s method need to set manually, which significantly limits its a...
In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask spars...
Existing structured-dictionary-learning (SDL)-based sparse synthetic aperture radar (SAR) imaging methods suffer from insufficient artifact suppression, limited parameter adaptivity, and inadequate structure preservation during refined imaging under...
Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recog...
In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning,...
Concepts empower cognitive intelligence. Extracting flat, nested, and discontinuous name entities and concept mentions from natural language texts is significant for downstream tasks such as concept knowledge graphs. Among the algorithms that uniform...
In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition...
Carbon-fiber aluminum honeycomb sandwich panels are vulnerable to low-velocity impacts, which can cause structural damage and failures that reduce the bearing performance and reliability of the structure. Therefore, a method for locating such impacts...
Sign language recognition is one of the most challenging tasks of today’s era. Most of the researchers working in this domain have focused on different types of implementations for sign recognition. These implementations require the development...
Speech recognition, as a key driver of artificial intelligence and global communication, has advanced rapidly in major languages, while studies on low-resource languages remain limited. Tongan, a representative Polynesian language, carries significan...
This paper presents an application of optical word recognition and fuzzy control to a smartphone automatic test system. The system consists of a robot arm and two webcams. After the words from the control panel that represent commands are recognized...
Location services based on address matching play an important role in people’s daily lives. However, with the rapid development of cities, new addresses are constantly emerging. Due to the untimely updating of word segmentation dictionaries and...
Images taken on rainy days often lose a significant amount of detailed information owing to the coverage of rain streaks, which interfere with the recognition and detection of the intelligent vision systems. It is, therefore, extremely important to r...
Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging...
This study aims to create an AI system that analyzes text to evaluate student engagement in STEAM education. It explores how sentiment analysis can measure emotional, cognitive, and behavioral involvement in learning. We developed an AI-based text se...
Communication languages convey information through the use of a set of symbols or units. Typically, this unit is word. When developing language technologies, as words in a language do not have the same prior probability, there may not be sufficient t...
Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field...
Deep learning techniques have been successfully applied in handwriting recognition. Oracle bone inscriptions (OBI) are the earliest hieroglyphs in China and valuable resources for studying the etymology of Chinese characters. OBI are of important his...
The purpose of this work is to develop a spoken language processing system for smart device troubleshooting using human-machine interaction. This system combines a software Bidirectional Long Short Term Memory Cell (BLSTM)-based speech recognizer and...
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