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Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities

Poster: Cloud Computing with AI-empowered Trends in Software...

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ieee International symposium on World of Wireless Mobile and multimedia Networks (WoWMoM)

作者： Ekta Sharma Ravinesh C. Deo Christopher P. Davey Brad D. Carter Sancho Salcedo-Sanz School of Mathematics Physics and Computing University of Southern Queensland Springfield Queensland Australia Centre for Astrophysics University of Southern Queensland Toowoomba Queensland Australia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Artificial intelligence (AI) and Software Defined Radio (SDR) are transforming the field of signal intelligence. However, the full extent of the capabilities is unknown. This poster presents a paper in development that introduces a cloud-based platform leveraging artificial intelligence to detect and apply 11 modulation schemes (8 digital and 3 analog) to complex or quadrature radio signals. The SNR values analysed range from 0.0 to 40.0, with moderate drift, slight fading, and labelled increments. A comprehensive synthetic database developed by DeepSig is used to train four AI models. These will be integrated with the Google Cloud AI platform to enhance flexibility and processing power. The system will undergo testing with an SDR platform in GNU Radio, showcasing its potential for real-world signal processing applications. Cloud-based platforms offer the adaptability and computational power needed to replace traditional computers for AI-driven signal processing. Initial results indicate successful identification and accurate modulation type detection, with convenient access to the system through internet-connected devices.

关键词： Training Computers Wireless communication Cloud computing Modulation System integration Artificial intelligence

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JPEG AI Compressed Domain Face Detection

JPEG AI Compressed Domain Face Detection

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ieee Workshop on multimedia signal processing

作者： Ayman Alkhateeb Alessandro Gnutti Fabrizio Guerrini Riccardo Leonardi João Ascenso Fernando Pereira Dep. of Information Engineering CNIT - University of Brescia Italy Dep. of Electronics Engineering University of Rome Tor Vergata Italy Instituto Superior Técnico Universidade de Lisboa - Instituto de Telecomunicações Lisbon Portugal

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Learning-based image coding has achieved competitive performance in terms of compression efficiency, while also gaining a key advantage in the ability to carry out computer vision tasks directly in the compressed domain. In fact, the latent representation which is generated using deep learning techniques may natively encapsulate all visual features needed for processing tasks, thereby eliminating the need to perform the expensive synthesis transform process at the decoder side. In this paper, it is proposed to perform face detection using the latent code present in the JPEG AI architecture. First, some experiments show how decoded images can be efficiently processed for face detection without retraining, albeit with some performance degradation. Then, for the first time a compressed domain RetinaFace-based detector applied to JPEG AI latent representations is competitively proposed. The performance achieved is comparable to the performance of the original RetinaFace applied to the reconstructed JPEG AI images, while reducing computational complexity since it bypasses the image decoding process. It is expected that this approach might be extended to other vision tasks since the JPEG AI representation format is not tailored specifically for any computer vision task.

关键词： Visualization Image coding Transform coding Transforms signal processing Decoding Face detection Artificial intelligence Image reconstruction Faces

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Patient level analytics using self-organising maps: A case study on Type-1 Diabetes self-care survey responses

Patient level analytics using self-organising maps: A case s...

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ieee symposium on computational intelligence and Data Mining (CIDM)

作者： Santosh Tirunagari Norman Poh Kouros Aliabadi David Windridge Deborah Cooke University of Surrey Guildford Surrey United Kingdom Center for Vision Speech and Signal Processing Department of Computing Department of Computer Science Middlesex University The Burroughs London School of Health Sciences

Survey questionnaires are often heterogeneous because they contain both quantitative (numeric) and qualitative (text) responses, as well as missing values. While traditional, model-based methods are commonly used by clinicians, we deploy Self Organizing Maps (SOM) as a means to visualise the data. In a survey study aiming at understanding the self-care behaviour of 611 patients with Type-1 Diabetes, we show that SOM can be used to (1) identify co-morbidities; (2) to link self-care factors that are dependent on each other; and (3) to visualise individual patient profiles; In evaluation with clinicians and experts in Type-1 Diabetes, the knowledge and insights extracted using SOM correspond well to clinical expectation. Furthermore, the output of SOM in the form of a U-matrix is found to offer an interesting alternative means of visualising patient profiles instead of a usual tabular form.

关键词： Data visualization Diabetes Neurons Correlation Retinopathy Vectors Proteins

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Identification of irregular motion in automotive navigation systems using novelty detection

Identification of irregular motion in automotive navigation ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Martin Pöllot Dominic Springer Ralph Schleifer Dieter Niederkorn André Kaup Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany Audi AG Ingolstadt Ingolstadt Germany

Automated display testing for visual unpleasant and erroneous navigation sequences is an important step to preserve a high quality standard for premium vehicle manufacturers. This paper presents a novel error detection algorithm for navigation sequences based on novelty detection on motion parameters obtained from real world navigation sequences. Motion parameters are accumulated through key point matching using BRISK and subsequent homography calculation. With these parameters one is able to describe the motion between two successive frames. Combinations of translational and rotational components allow the novelty detection algorithm to predict outliers. These outliers either show positioning errors or abnormal motion behaviour which are both unacceptable for high quality. Experimental results demonstrate that this algorithm works significantly better than state of the art, where one has to know errors before analysing the data set in order to determine thresholds for particular errors. The gains in precision and recall are 49.67% and 6.06% respectively, the accuracy is 1.37% higher compared to optimized threshold results.

关键词： Automobiles Navigation Testing Robustness Transmission line matrix methods Algorithm design and analysis

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Probabilistic Semantic Mapping with a Virtual Sensor for Building/Nature detection

Probabilistic Semantic Mapping with a Virtual Sensor for Bui...

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ieee International symposium on computational intelligence in Robotics and Automation (CIRA)

作者： Martin Persson Tom Duckett Christoffer Valgren Achim Lilienthal Centre of Applied Autonomous Sensor Systems Department of Technology Örebro University Sweden University of Lincoln Lincoln UK

In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We believe that access to semantic maps will make it possible for robots to better communicate information to a human operator and vice versa. The main contribution of this paper is a method that fuses data from different sensor modalities, range sensors and vision sensors are considered, to create a probabilistic semantic map of an outdoor environment. The method combines a learned virtual sensor (understood as one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) for building detection with a standard occupancy map. The virtual sensor is applied on a mobile robot, combining classifications of sub-images from a panoramic view with spatial information (location and orientation of the robot) giving the likely locations of buildings. This information is combined with an occupancy map to calculate a probabilistic semantic map. Our experiments with an outdoor mobile robot show that the method produces semantic maps with correct labeling and an evident distinction between 'building' objects from 'nature' objects.

关键词： Humans Mobile robots Robot sensing systems Sensor fusion Sensor systems Indoor environments Data mining Sensor phenomena and characterization Fuses signal processing

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Mining visual complexity of images based on an enhanced feature space representation

Mining visual complexity of images based on an enhanced feat...

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ieee International symposium on Intelligent signal processing

作者： Abdullah M. Iliyasu Awad Kh. Al-Asmari Mohamed A. AbdelWahab Ahmed S. Salama Mohamed A. Al-Qodah Asif R. Khan Phuc Q. Le Fei Yan Department of Computational Intelligence & Systems Science Tokyo Institute of Technology Japan Salman Bin Abdul-Aziz University Al Kharj Kingdom of Saudi Arabia Image and Signal Processing Lab King Saud University Kingdom of Saudi Arabia

An enhanced feature space to represent visual complexity of images, as would the HVS, is presented. Specifically, the ratio between the coherent and incoherent pixels in an image was used as a measure of the chromatic contributions to the visual complexity of an image. Similarly, the contrast, energy, entropy and homogeneity were modelled as the textural attributes of an image's visual complexity. Integrated into the SND feature space, these new (chromatic and textural) features facilitate a better and enhanced representation of visual complexity. Using the Corel 1000A dataset to validate the veracity of the proposal, the enhanced visual complexity space, the SND + space, improves the capability to better represent visual complexity by a 16.7% increase in the exact correlation with a subjective (human) evaluation of the same dataset over the original SND space. Pursued further, the effective representation of visual complexity would have profound impacts in many areas of image processing and computer vision.

关键词： Complexity theory Visualization Feature extraction Image color analysis Computer vision Noise Correlation

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Pose-Based Multi-Target Tracking

Pose-Based Multi-Target Tracking

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ieee International symposium on Parallel and Distributed processing with Applications and ieee International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiangbin Shi Xiaoyu Yang Deyuan Zhang Jing Bi Zhaokui Li Fang Liu School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Human multi-target tracking in video is an important issue in the field of computer vision. It is necessary to detect the target on each frame, and to connect the targets of all frames into a target sequence. For target matching among different frames, we propose a tracking algorithm for constructing object pose sequence(COPS) based on Openpose. The position status and the ORB feature of the target pose are dynamically weighted and fused into new features. Target pose is searched in the corresponding target pose sequence by comparing the new features between the target pose in the sequence and every pose in current frame. When the target pose is matched, the influence of the position feature on the pose similarity could be enhanced when the target motion is continuously detected. When the target scale changes too much, the method can expand the contribution of the ORB feature to the pose similarity comparison. The experiments of human multitarget tracking algorithm are carried out on the PoseTrack and MOT datasets, and the results show that the proposed tracking algorithm overcomes the problem of target matching between frames.

关键词： computer vision feature extraction image fusion image matching image motion analysis image sequences object tracking pose estimation target tracking video signal processing Target tracking images computer vision Targets image motion analysis pose estimation Video signal processing image matching image sequences image fusion Feature extraction Frame FRAMES tracking objects

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Fuzzy integral for leaf image retrieval

Fuzzy integral for leaf image retrieval

引用

ieee International Conference on Fuzzy Systems (FUZZ-ieee)

作者： Zhiyong Wang Zheru Chi Dagan Feng Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Kowloon Hong Kong China

Generally, the more features utilized, the better the retrieval performance. However, it is a very challenging task to combine different feature sets in a way reflecting human perception. This paper presents the combination of different shape based feature sets using fuzzy integral for leaf image retrieval. The feature sets used in our system include centroid-contour distance curve, eccentricity, and angle code histogram. The fuzzy integral approach can release the user's burden from tuning the combination parameters. In order to reduce the matching time in the retrieval process, a thinning based method is proposed to locate the start point of a leaf contour. Experimental results on 440 leaf images from 44 plant species (10 samples from each plant species) show that the fuzzy integral approach can achieve a comparable retrieval performance with the best case of the weighted summation combination. The results also indicate that our approach, which are more efficient, can achieve a better retrieval performance than both the curvature scale space (CSS) method and the modified Fourier descriptor (MFD) method.

关键词： Image retrieval Shape Plants (biology) Histograms Computer vision Humans Fuzzy sets Cascading style sheets Image recognition signal processing

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Efficient vision Transformer Deployment on Google Coral Through Knowledge Distillation

Efficient Vision Transformer Deployment on Google Coral Thro...

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East-West Design & Test symposium (EWDTS)

作者： Vazgen Melikyan Tigran Khachatryan Davit Galstyan Eduard Harutyunyan National Polytechnic University of Armenia Synopsys Armenia CJSC Yerevan Armenia Synopsys Armenia Educational Department National Polytechnic University of Armenia Yerevan Armenia

ISBN: (数字)9798331515768

ISBN: (纸本)9798331515775

With the development of AI, the models are becoming more complex and resource intensive. There is a need to create smaller but at the same time satisfactory performance models that will run on resource-constrained devices. This paper explores enhancing the efficiency of the MobileNetV2 architecture using the power of the vision Transformer through knowledge distillation. The goal was to leverage the complex insights of the vision Transformer to improve the capabilities of MobileNetV2. As a result, the refined model outperformed its original version by about 5% (from 81.7% to 86.5%). The model was then placed on the Coral Dev Board edge device and due to the weight adjustments, there was a slight decrease in accuracy to 84.3%. Despite this, it reduced its processing latency by more than half, from 0.0076 seconds to 0.0037 seconds, proving a valuable approach for efficient and real-time edge computing applications.

关键词： Performance evaluation Computer vision Accuracy Quantization (signal) computational modeling Transformers Real-time systems Internet Artificial intelligence Edge computing

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Deep Learning-Based computational Model for Disease Identification in Cocoa Pods

Deep Learning-Based Computational Model for Disease Identifi...

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symposium of Image, signal processing, and Artificial vision (STSIVA)

作者： Cristian Zambrano-Vega Darlyn Buenaño Vera Washington Chiriboga Casanova Byron Oviedo Department of Engineering Science State Technical University of Quevedo Quevedo Ecuador

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

The early identification of diseases in cocoa pods is an important task to guarantee the production of high-quality cocoa. The use of artificial intelligence techniques such as machine learning, computer vision and deep learning are promising solutions to help identify and classify diseases in cocoa pods. In this paper we introduce the development and evaluation of a deep learning computational model applied to the identification of diseases in cocoa pods, focusing on “monilia” and “black pod” diseases. An exhaustive review of state-of-the-art of computational models was carried out, based on scientific articles related to the identification of plant diseases using computer vision and deep learning techniques. As a result of the search, EfficientDet-Lite4, an efficient and lightweight model for object detection, was selected. A dataset, including images of both healthy and diseased cocoa pods, has been utilized to train the model to detect and pinpoint disease manifestations with considerable accuracy. Significant enhancements in the model training and evaluation demonstrate the capability of recognizing and classifying diseases through image analysis. Furthermore, the functionalities of the model were integrated into an Android native mobile with an user-friendly interface, allowing to younger or inexperienced farmers a fast and accuracy identification of health status of cocoa pods.

关键词： Deep learning Training Computer vision Image recognition Accuracy Reviews computational modeling Computer architecture Search problems Diseases

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