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image Generation from Sketches and Text-Guided Attribute Edition 1

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9th Annual international conference on Information Management and Big Data, SIMBig 2022

作者： Sumiri Fernandez, Dennis Marcell Ochoa-Luna, José Department of Computer Science Universidad Católica San Pablo Arequipa Peru

ISBN: (数字)9783031354458

ISBN: (纸本)9783031354441

Every day new Artificial Intelligence models are created, and existing ones are modified or combined to extend their range of applications and tasks they can solve. This paper presents a novel approach that combines Natural Language processing and generative networks to generate images from sketches and descriptions in natural language. We present a pipeline that was followed to recondition the generative network. It includes the processed text that will give the context to the sketch used for the generation. Finally, the model and the generated images are evaluated and compared using benchmark data sets, and promising results are reported. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Natural Language processing Techniques for Hate Speech Evaluation for Brazilian Portuguese 23rd

Natural Language Processing Techniques for Hate Speech Eval...

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23rd international conference on Computational Science and Its applications, ICCSA 2023

作者： Rosa, Cássia C. S. Martinez, Fábio V. Ishii, Renato Faculdade de Computação Universidade Federal de Mato Grosso do Sul Campo Grande Brazil

ISBN: (纸本)9783031371134

The numerous harmful publications generated from large amounts of data expelled daily on social media make it necessary to adopt automated technologies for online content moderation. Sentence classification and sentiment analysis are Natural Language processing (NLP) techniques used to detect hate speech on social media platforms such as Facebook and Instagram. However, some difficulties reduce the effectiveness of these tools in the Portuguese language. Previous research has shown how NLP models have high accuracy when trained with datasets centered on mastering the Brazilian Portuguese language. In this work, we propose the creation of a large-scale linguistic corpus for Brazilian Portuguese composed of publications collected from the social network Twitter. The experiments were performed by tuning a pretrained transformer model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Retinal image Pre-processing Using Equalization and thResholding (RIPPER) 23

Retinal Image Pre-Processing Using Equalization and thReshol...

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Proceedings of the 2023 10th international conference on Bioinformatics Research and applications

作者： Nnamdi Nwanze Chukwuemeka C. Nwanze Abumere Akinwale Larry Baitch Computer Science & Engineering North Central College USA School of Optometry Mass. Coll. of Pharm & Health Sciences USA Department of Ophthalmology Veterans Healthcare System USA

ISBN: (纸本)9798400708152

The eye is the only location on the human body that enables non-invasive evaluation of blood vessels. Clinicians can correlate the presence of systemic diseases, such as hypertension and diabetes, with the appearance of the ocular blood vessels. As such, there has been significant interest in accurate retinal vessel segmentation and classification. Traditionally, segmentation and classification methods first pre-process an image, then apply a vessel identifying technique. The results of this process is then compared to a gold-standard–typically an image that has been manually segmented by a human domain expert. In this paper, we propose RIPPER (Retinal image Pre-processing using Equalization And thResholding) a highly effective retinal image pre-processing technique for use in both manual and automated vessel segmentation systems. We apply RIPPER to two publicly available retinal image databases, DRIVE and HRF, which contain gold standard segmentations for each image. Our results show that using RIPPER can increase the accuracy of the gold standard images by up to 20% thereby allowing for increased performance in retinal vessel segmentation and classification systems.

关键词： convolutional neural networks datasets deep learning fundus images image pre-processing image processing machine learning retinal blood vessel segmentation retinal blood vessels classification retinal vessels vessel segmentation

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A Diverse Environment Coal Gangue image Segmentation Model Combining Improved U-Net and Semi-supervised Automatic Annotation 8th

A Diverse Environment Coal Gangue Image Segmentation Model C...

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8th international conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Liu, Xiuhua Zhu, Wenbo Zhu, Zhengjun Luo, Lufeng Zhang, Yunzhi Lu, Qinghua School of Mechatronic Engineering and Automation Foshan University Foshan528000 China China Coal Technology Engineering Group Tangshan Research Institute Tangshan063000 China

ISBN: (纸本)9789819980208

The problem of uneven illumination in the coal gangue image and the existence of fine coal gangue reduces the accuracy of the coal gangue location, which also makes it difficult to the annotation of the coal gangue images. In this paper, we propose an improved U-Net gangue segmentation algorithm to achieve gangue sorting and automatic image annotation under diversified environments. Specifically, to solve the problem that some fine coal and gangue fragments are difficult to be recognized, the InceptionV1 module is introduced instead of some of these convolutional blocks. Secondly, to solve the visual blur caused by light changes in industrial sites, the CPAM attention module is added to the fusion part of U-Net. Finally, to alleviate the difficulty of coal gangue image annotation, a semi-supervised human-in-the-loop automatic annotation framework is constructed, and a statistical method based on classification probability is proposed to automatically screen the questionable labels. The experimental results show that the proposed improved segmentation model exhibits good performance in the coal gangue sorting task with MIoU values of 80.23% and MPA values of 86.16%, which have higher accuracy compared to the original model. In addition, the statistical method proposed in this study can effectively screen out the incorrect and correct labeled data, thus enabling automatic annotation and screening. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image annotation

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Facial image Pre-processing for Enhanced Ethnicity Classification

Facial Image Pre-processing for Enhanced Ethnicity Classific...

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Intelligent Technologies (CONIT), international conference on

作者： Keshav Goel Hitesh Dhiman Himanshu Yadav Shailender Kumar Department of Computer Science and Engineering Delhi Technological University New Delhi India

Facial recognition and ethnicity detection is an important task in various applications such as security, marketing, and social sciences. However, the accuracy of Face recognition and ethnicity detection algorithms can be impacted by various factors such as lighting conditions, noise and irrelevant features in image background, variation in facial expressions, face size, pose and orientation across images. In this study, we present a novel face image preprocessing model in this work that aims to enhance the performance of deep learning models like ResNet, DenseNet and other state of the art Deep Learning models on facial recognition tasks, specifically ethnicity detection by addressing these issues. Our novel method for preprocessing facial images incorporates several techniques to enhance the quality of the input images. Firstly, we removed the background using DeepLab-v3. Secondly, we employed facial landmarks detection and facial pose and orientation correction using facial alignment based on the angle between the eyes. Thirdly, to facilitate race classification, we performed facial cropping to isolate the facial region and other critical features essential for the task. Finally, we ensured consistent lighting conditions across all images using the Multi-scale retinex algorithm. These techniques together enabled the deep learning model to better analyze and classify facial data. To assess the efficacy of our model, we carried out a series of experiments on the UTK Face dataset and compared the outcomes with those obtained when our preprocessing model was not *** experimental outcomes show that our suggested pre-processing model improves the f1-score of ethnicity detection classifiers by up to 6%. The proposed facial image preprocessing model is efficient, fast, and easy to implement, enabling real-time applications, making it appropriate. Our research highlights the importance of preprocessing techniques in enhancing ethnicity detection performance and p

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BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal processing

BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, ...

引用

Omni-layer Intelligent Systems (COINS), international conference on

作者： Sebastian Frey Marco Guermandi Simone Benatti Victor Kartsch Andrea Cossettini Luca Benini Integrated Systems Laboratory ETH Zürich Zurich Switzerland DEI University of Bologna Bologna Italy Greenwaves Technologies Grenoble France DIEF University of Modena and Reggio Emilia Reggio Emilia Italy

Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of- Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and machine Learning (ML) near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multi-precision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAP's form factor is 16x21x14 mm 3 and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an accelerometer; and a shield including an analog front-end (AFE) for ExG acquisition. Finally, the system also includes a flexibly placeable photoplethysmogram (PPG) PCB with a size of 9x7x3 mm 3 and a rechargeable battery ( $\phi$ 12x5 mm 2 ), We demonstrate BioGAP on a Steady State Visually Evoked Potential (SSVEP)-based Brain-computer Interface (BCI) application. We achieve 3.6 $\mu J/sample$ in streaming and 2.2 $\mu J/sample$ in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW allowing for an operation time of 15 h.

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Data Locality Aware Computation Offloading in Near Memory processing Architecture for Big Data applications

Data Locality Aware Computation Offloading in Near Memory Pr...

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international conference on High Performance Computing

作者： Satanu Maity Mayank Goel Manojit Ghose Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

The data-intensive applications of today's big data era often produce a large memory footprint. As a result, a significant volume of data needs to travel from memory to the CPU under the traditional Von-Neumann computing paradigm. Near-memory processing (NMP) or processing-in-memory (PIM) is a potential alternate computation framework where a computation unit is placed near the memory (or inside the memory) and a portion of an application is executed on it (termed computation offloading) aiming to reduce the amount of data movement and its consequences. Although a few computation offloading strategies have been proposed in recent times, the existing approaches do not consider the data locality offered by the last level cache and the overall execution time of the application while designing their policies. In this paper, we propose a data locality-aware computation offloading strategy for a hybrid computing system comprising the host processor and NMP-enabled 3D memory. After the application code is instrumented using the LLVM compiler framework, the strategy offloads a portion of an application to NMP if its estimated overall execution time is less. An extensive simulation performed on a set of standard simulators for a bunch of large graph-based application benchmarks reports the effectiveness of the proposed strategy by achieving a maximum speedup of 40% and 11.8% as compared to the host-only configuration and the state-of-art policy, respectively. The proposed strategy also reduces the off-chip data transfer and energy consumption by a significant margin as compared to the host-only configuration (avg 27%) and the state-of-art policy (avg 28%). Further, the proposed policy reduces the LLC miss rate by 57% as compared to the state-of-art policy.

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A Cost-Effective Approach For Real-Time Anemia Diagnosis Using An Automated image processing Tool Interfaced Paper Sensor

A Cost-Effective Approach For Real-Time Anemia Diagnosis Usi...

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ieee India conference (INDICON)

作者： Smriti Sinha Akashlina Basu Shirin Dasgupta Soumen Das School of Medical Science and Technology Indian Institute of Technology Kharagpur India Dr. B.C.Roy Multispeciality Medical Research Centre Indian Institute of Technology Kharagpur India

A large proportion of the world’s population from low and middle-income countries, particularly women of reproductive age and young children, are vulnerable to anemia. Anemia reduces work productivity and increases mortality in chronic ailments such as kidney, cardiovascular, acquired immunodeficiency diseases, etc. Therefore, mass screening of anemic population and early treatment are crucial for public health programs. While various POC device has been reported earlier for rapid, affordable sensing of anemia, these methods have several limitations that hinder their broad implementation in practical applications. Here, we report an on-card approach for anemia detection by measuring the blood-spreading area on a cellulose paper platform. The image analysis was coupled with a Python-based image processing tool for the automated measurement of blood Hb. This device was standardized using a minimal blood volume of 10μl and demonstrates a sensitivity of -5.17 ± 0.11 (mm 2 /(g/dl)) and can efficiently measure Hb as low as 1.07g/dl. On comparison with the gold standard method, strong correlation(r=0.99) and good accuracy (mean bias= 0.15g/dl with a narrow limit of agreements, i.e., -0.67 to 0.98 g/dl) was achieved. This device operates reagent-free and thus is not susceptible to interference caused by storing and transportation. Automated analysis precluded the chance of error due to manual handling, thus reducing the risk of misdiagnosis. The developed paper sensor offers a simple, affordable, automated method for real-time anemia detection and potentially facilitates anemia screening in poor settings.

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Feature Extraction from image Sensing (Remote) : image Segmentation

Feature Extraction from Image Sensing (Remote) : Image Segme...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Kaushal Chauhan Harshit Tomar Kushagra Kamal Pallavi Goel Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

In this research paper, an overview of computer methods for segmenting continuous-tone images into meaningful parts and characterizing these parts with “features” is presented. image segmentation is an essential pre-processing step in computer vision and image processing softwares. This paper discusses various techniques and algorithms used for image segmentation, including both supervised and unsupervised methods. The performance of these techniques is evaluated on different test images, and future research directions are proposed. Feature extraction from image sensing is a critical step in multimedia processing. This paper explores the process of feature extraction from remotely sensed images. With the increasing use of remote sensing technology, it is crucial to have effective and accurate methods for extracting features from remotely sensed data, which are utilized in various applications such as land-use mapping, environmental proctoring, and in natural-disaster management. This paper discusses several techniques for feature extraction, including pixel-based, object-based, and machine learning-based approaches. Additionally, it highlights the difficulties associated with feature extraction from remote sensing data, such as atmospheric effects, radiometric and geometric distortions, and the presence of noise. The paper concludes by discussing future directions in remote sensing feature extraction, including the integration of deep learning methods and the development of more robust methods has the ability of handling large-scale datasets. With the help of deep learning methods and algorithms, especially CNN’s, has tremendously improved the accuracy of image scene classification. In this study, a CNN model which is already trained was utilized to fetch-out deep features from images at various network layers, including the average pooling layer.

关键词： Deep learning image segmentation Scene classification Noise Neural networks Feature extraction Radiometry

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Single Photon Detectors for Quantum Computing

Single Photon Detectors for Quantum Computing

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Proceedings of the ieee Dallas Circuits and Systems Workshop (DCAS)

作者： Mst Shamim Ara Shawkat Sajid Hasan Nicole McFarlane Department of Electrical and Computer Engineering Florida International University Florida USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee USA

Optical quantum entanglement has emerged as a promising tool to solve many previously intractable problems in science and finds applications in signal processing, communication, and encryption. Quantum systems based on photons require optical detectors which are able to detect single photons, have a high photon to electron conversion gain, linear, low noise, and have a wide detection range. In order to be competitive with traditional computing systems in terms of size, weight, and power and to facilitate wider adoption the detectors should be low cost, scalable, and facilitate integration with the backend electronics. For these applications, single photon avalanche diodes (SPADs) are becoming more popular. They can be implemented in scalable formats using commercial silicon foundries where the cost per unit is low. However, these silicon SPADs suffer from higher noise and lower photon detection efficiency than other semiconducting materials. This paper we review an improved version of SPAD single photon detectors, perimeter gated SPAD (PGSPAD), which show improved noise performance and are suitable for quantum computing.

关键词： Photomultipliers Quantum computing Costs Detectors Logic gates Optical detectors Silicon

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