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Mobile Dimensions Detection For Forensic Investigation 6

Mobile Dimensions Detection For Forensic Investigation

引用

6th ieee international conference on signal Processing, Computing and Control, ISPCC 2021

作者： Sharma, Sarang Gupta, Sheifali Ahuja, Rakesh Gupta, Deepali Institute Of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9781665425520

This paper proposed a canny edge detection-based approach for measuring the dimension of an object of mobile type captured in an image from the given distance. The purpose of the scheme is to implement aspects of forensic investigation helpful for our society. It makes use of mobile dimension detection algorithms to narrow it down to a range of mobile phones in a grievous event. The scheme not only identifying the mistrusting individual but also assists the forensics investigation team. In addition to that, the technique support to track the suspected individual as well as also concerns for tapping the communication records of concern person. It has been tested on wide varieties of mobile images of different model from same or different companies. The superiority of the proposed scheme is that excellent results are obtained while comparing the real dimension of an object with the object in an image. © 2021 ieee.

关键词： signal detection

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Visual Graph Reasoning Network

Visual Graph Reasoning Network

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Dingbang Li Xin Lin Haibin Cai Wenzhou Chen East China Normal University Zhejiang University

Visual question answering (VQA) is a fundamental and challenging cross-modal task. This task requires the model to fully understand the image’s content and reason out the answer based on the question. Existing VQA models understand visual content mainly based on bottom-up or grid features. However, both types of vision features have some drawbacks. The discreteness and independence of bottom-up features pre-vent models from adequately performing relational reasoning. image segmentation by grid features leads to the fragmentation of meaningful visual regions, limiting the cross-modal alignment capability of the model. Therefore, we proposed a more flexible method called Visual Graph. It can connect different patches according to semantic similarity and spatial relevance to model the potential relationships and cluster the adjacent homologous patches. based on the Visual Graph, we designed a Visual Graph Reasoning Network for VQA. We evaluated our model on GQA and VQA-v2. The experimental results show that our models can achieve excellent performance between single models.

关键词： Visualization image segmentation Limiting Semantics Cognition Question answering (information retrieval) Acoustics

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SISO-LoRa Wireless Communication Aided By Reconfigurable Intelligent Surfaces

SISO-LoRa Wireless Communication Aided By Reconfigurable Int...

引用

international conference on Communications and Networking, ComNet

作者： Ali Kotti Zouhair Ben Jemaa Safya Belghith RISC Laboratory ENIT-Tunisia University Of Gafsa RISC Laboratory ENIT-Tunisia University Of Carthage RISC Laboratory ENIT-Tunisia University Of Tunis ElManar

ISBN: (数字)9798331520793

ISBN: (纸本)9798331520809

The recent emergence of reconfigurable intelligent surfaces (RIS) offers a revolutionary approach with the potential to dramatically enhance spectrum and energy efficiency in future wireless communication networks. This technique allows network operators to manage the scattering, reflection, and refraction of radio waves, thereby mitigating the adverse effects of natural wireless propagation. Long Range (LoRa) serves as the physical layer of the LoRa wide area network (LoRaWAN) protocol, renowned for its extensive use in internet of things (IoT) applications. It is prized for its exceptional long-range capabilities, global availability, and support for bidirectional communication, despite its low data rate. In previous works, RIS technology has been investigated within MIMO communication systems. This paper presents a novel system design that integrates RIS-assisted single input single output (SISO) scenario in LoRa wireless communication. By exploiting RIS technology, we aim to maximize the signal-to-noise ratio (SNR) at the receiver to enhance the system performance in terms of bit error rate (BER) through optimal phase shifts at the reflecting RIS elements. We evaluate the BER performance of the new communication system at the output of a coherent receiver.

关键词： Wireless communication AWGN channels Wide area networks Bit error rate LoRa Receivers Reconfigurable intelligent surfaces Physical layer internet of Things signal to noise ratio

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From Pixels to Voice: A Simple and Efficient End-to-End Spoken image Description Approach via Vision Codec Language Models

From Pixels to Voice: A Simple and Efficient End-to-End Spok...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Chung Tran Sakriani Sakti Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Neural audio codecs provide a powerful tool for compressing audio signals into discrete codec representations. This compact discrete representation has made it possible to successfully apply a natural language processing (NLP) model to various audio and speech processing tasks, including text-to-speech (e.g., VALL-E, VALL-E X) and multimodal audio-text generation (e.g., LauraGPT, VioLA). While these models excel at handling sequential data like text and speech, their potential for processing non-sequential data, such as images, remains unexplored. In this paper, we introduce PixVoxLM, a simple and efficient end-to-end framework that combines vision-language models with neural audio codecs to tackle the image-to-Speech (I2S) problem. Experiments on the Flickr8k dataset demonstrate that PixVoxLM delivers promising results compared to existing I2S methods. Furthermore, this research is the first to explore a new capability: visual-guided speech completion in I2S model, paving the way for new practical applications in everyday communication, such as speech prompt-based instruction.

关键词： Codecs Speech coding signal processing Natural language processing Data models Acoustics Text to speech Speech processing Synthetic aperture sonar

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AIEngiMentor: Scalable Deep Learning System for Hands-on Engineering Education

AIEngiMentor: Scalable Deep Learning System for Hands-on Eng...

引用

signal, Information and Data Processing (ICSIDP), ieee international conference on

作者： Winfred Lin Qiang Wang Integem Inc. Cupertino CA USA

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In this research, we proposed AIEngiMentor, a scalable system that assists students by automatically detecting and diagnosing errors in robotics and engineering assembly. It integrates deep learning models for multi-stage video segmentation and recognition, leveraging the Segment Anything Model 2 (SAM2) to handle both device- and component-level complexities. AIEngiMentor incorporates a Video-based Adaptive Integrity Algorithm (VAIA), which dynamically adjusts to step-by-step requirements from the Learning Management System (LMS). This architecture delivers real-time, precise feedback, even when processing low-quality or moving images with complex backgrounds. Unlike costly industry solutions, AIEngiMentor provides an affordable, practical alternative, ensuring the accuracy and safety of circuit and device connections by validating each step against LMS-defined criteria. Preliminary results show AIEngiMentor outperforms manual inspection in accuracy and enhances student learning by enabling focus on complex problem-solving tasks. The system's adaptability and precision also indicate potential for broader use in industrial automation and healthcare, where real-time error detection is critical.

关键词： Deep learning Learning management systems image segmentation Adaptation models Accuracy Automation Medical services Real-time systems Problem-solving Wire

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Stock Market Prediction Using Machine Learning based Techniques

Stock Market Prediction Using Machine Learning Based Techniq...

引用

Intelligent signal Processing and Effective Communication Technologies (INSPECT), ieee international conference on

作者： Aditya Dubey Ramnaresh Sharma Dhananjay Bisen Nookala Venu Varshali Jaiswal Bhagat Singh Raghuwanshi Khemchand Shakywar Centre for Internet of Things Madhav Institute of Technology & Science Gwalior India Centre for AI Madhav Institute of Technology & Science Gwalior India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India School of Engineering Avantika University Ujjain MP

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Many methods have been used to forecast stock market trends in the big data age, including real numbers, fuzzy time series data, fuzzy sets design, and conventional time series data. The control of semantic value data is made possible by the use of fuzzy time series data in stock market prediction, which makes accurate value predictions possible. These techniques are mostly used to forecast dynamic, non-linear datasets in a variety of industries, such as stock markets and cryptocurrencies. In order to predict stock prices, artificial intelligence (AI) methods such as neural networks (NN) have become increasingly important. Many methods have been developed to validate parametric and non-parametric theories for predicting trends and gains in the stock market using the feed-forward (FF) NN technique. A variety of soft computing techniques have been investigated in order to provide ways for decision-making based on profit and loss criteria without relying on artificial intelligence. This paper's main goal is to perform a comprehensive examination of the different prediction techniques used to estimate price and return in the stock market. The study includes a comparative review of several approaches as well as a variety of stock market forecasting strategies.

关键词： Computational modeling Time series analysis signal processing algorithms Predictive models Prediction algorithms Market research Vectors Stock markets Forecasting Investment

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I-Tuning: Tuning Frozen Language Models with image for Lightweight image Captioning

I-Tuning: Tuning Frozen Language Models with Image for Light...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Ziyang Luo Zhipeng Hu Yadong Xi Rongsheng Zhang Jing Ma Department of Computer Science Hong Kong Baptist University Hong Kong SAR China College of Computer Science and Technology Zhejiang University Hangzhou China Fuxi AI Lab NetEase Inc. Hangzhou China

image Captioning is a traditional vision-and-language task that aims to generate the language description of an image. Recent studies focus on scaling up the model size and the number of training data, which significantly increase the cost of model training. Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters. We design a novel I-Tuning cross-attention module to connect the non-trainable pre-trained language decoder GPT2 and vision encoder CLIP-ViT. Since most parameters are not required to be updated during training, our framework is lightweight and fast. Experimental results conducted on three image captioning benchmarks reveal that our frame-work achieves comparable or better performance than the large-scale baseline systems. But our models contain up to 10 times fewer trainable parameters and require much fewer data for training compared with state-of-the-art baselines.

关键词： Training Costs Training data signal processing Benchmark testing Data models Decoding

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A Power-Efficient VLSI Multiplier Design Employing Radix-2m Squarer Unit and Adder Compressor

A Power-Efficient VLSI Multiplier Design Employing Radix-2m ...

引用

ieee international conference on Electronics, Circuits and systems (ICECS)

作者： Leonardo Antonietti Morgana Da Rosa Rodrigo Lopes Eduardo Da Costa Rafael Soares Graduate Program on Computing - Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing - Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

Multipliers are pivotal in numerous applications, including digital signal processing, image processing, and video processing. Nonetheless, their operation substantially increases power consumption for these applications. Therefore, applying strategies that can reduce power in this arithmetic operator is paramount. This paper presents a novel approach to optimize arithmetic units, focusing on multiplier units, through the integration of the Radix- $2^{m}$ squarer unit (RSU) and 4–2 adder/subtractor compressor (AC). Compared to the state-of-the-art, the proposed multiplier unit, termed MRSU (multiplier unit RSU-based), offers significant area-and energy-savings. The synthesis results show remarkable improvements in the MRSU Dadda 4–2 architecture, which achieves 14.35× more energy-savings for 8 bits, 4.87× for 16 bits, and 2.22× for 32 bits compared to the multiplier unit automatically selected by the logic-synthesis tool. Furthermore, compared to the Booth multiplier with the Dadda compression tree, MRSU Dadda 4–2 exhibits 1.67× more energy-savings for 8 bits, 1.93× for 16 bits, and 1.95× for 32 bits.

关键词： image coding Power demand Focusing Digital signal processing Computer architecture Very large scale integration Energy efficiency Electronic circuits Adders Arithmetic

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RepMedGraf: Re-parameterization Medical Generated Radiation Field for Improved 3D image Reconstruction

RepMedGraf: Re-parameterization Medical Generated Radiation ...

引用

ieee conference on Advanced Video and signal based Surveillance (AVSS)

作者： Ruotong Sun Fayang Liao Jing He Ying Liu Wei Yi Qinrui Fan Shu Hu Xin Wang Jing Hu Chengdu University of Information Technology Chengdu China Purdue University IN USA University at Albany State University of New York NY USA

ISBN: (数字)9798350374285

ISBN: (纸本)9798350374292

In the field of medical imaging, 3D image reconstruction has emerged as a crucial technique for accurate disease diagnosis. Neural Radiance Field (NeRF) has shown promise in generating high-quality 3D models through deep learning, but its application in medical imaging remains limited. This paper presents RepMedGraf, an improved model addressing the limitations of NeRF. RepMedGraf utilizes a generator based on lightweight RepVGG Blocks instead of MedNeRF model. By doing so, the risk of overfitting is reduced, and training efficiency is improved. The proposed method is trained on publicly available chest and knee datasets. Comparative evaluations are conducted based on the generated radiation fields, demonstrating the effectiveness and quality of the 3D models produced by RepMedGraf. The results highlight the potential of RepMedGraf as a valuable tool in medical imaging for enhanced diagnostic accuracy and improved patient care.

关键词： Training Solid modeling Three-dimensional displays Accuracy Computed tomography Surveillance Neural radiance field

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Three-Dimensional Foot Contact Position on a Smart Fitness Trampoline with a Upward Looking Wide View Imaging System 12

Three-Dimensional Foot Contact Position on a Smart Fitness T...

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12th international conference on Ubiquitous and Future Networks (ICUFN)

作者： Park, Sekyung Park, Junkyu Ahn, Jongsik Cho, Boram Lee, Suwoong Kim, Minyoung Korea Inst Ind Technol Adv Mechatron Grp Cheonan South Korea Kyungpook Natl Univ Dept Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9781728164762

This study estimates the position and depth of the user's foot contacts of playing on a trampoline by acquiring the image of the footprint shadow and processing it to enable quantitative analysis of jumping (as a fitness exercise) or to link it to computer games. First, a hardware prototype of a jumping fitness monitoring system is proposed, consisting of a trampoline, an upward-looking wide-angle fish-eye camera module, and a signal processing embedded board. Second, image-based three-dimensional foot contact position estimation (I3D-FCE) algorithms are proposed, comprising the foot contact position estimation algorithm in the transverse plane and the foot contact depth estimation algorithm in the vertical direction. The foot contact positions were estimated using the binary image of the foot shadow obtained by extracting the binary masks of the trampoline and foot. The foot contact depth was estimated using the similarity ratio of the footprint shadow sizes. To verify the usefulness of the proposed system and the accuracy of the proposed contact position estimation algorithms, a series of experiments were conducted using a robotic manipulator. The experimental results show that the X and Y coordinates of the foot contact position and the Z coordinate of the foot contact depth were successfully estimated within an average error of 3.40 (mm), 3.26 (mm), and 4.49 (mm), respectively.

关键词： image Processing Embedded board Smart Fitness

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