Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual...
The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compr...
Although neural network quantization is an imperative technology for the computation and memory efficiency of embedded neural network accelerators, simple post-training quantization incurs unacceptable levels of accuracy degradation on some important...
Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focu...
As the theory of complex networks is further studied, the scale of nodes in the network is increasing, which makes it difficult to find useful patterns from only the analysis of nodes. Therefore, this paper proposes a complex network node layout meth...
Due to the large number of parameters and heavy computation, the real-time operation of deep learning in low-performance embedded board is still difficult. Network Pruning is one of effective methods to reduce the number of parameters without additio...
In recent years, the model compression technique is very effective for deep neural network compression. However, many existing model compression methods rely heavily on human experience to explore a compression strategy between network structure, spe...
Recently, the deep neural network (DNN) has become one of the most advanced and powerful methods used in classification tasks. However, the cost of DNN models is sometimes considerable due to the huge sets of parameters. Therefore, it is necessary to...
Neural networks have become a cornerstone of computer vision applications, with tasks ranging from image classification to object detection. However, challenges such as hyperparameter optimization (HPO) and model compression remain critical for impro...
The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typical...
In the past few years, deep learning has achieved remarkable advancements in the area of image compression. Remote sensing image compression networks focus on enhancing the similarity between the input and reconstructed images, effectively reducing t...
Deep neural network quantization is a widely used method in the deployment of mobile or edge devices to effectively reduce memory overhead and speed up inference. However, quantization inevitably leads to a reduction in the performance and equivalenc...
As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion chall...
Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often use...
Rolling bearings are often exposed to high speeds and pressures, leading to the symmetry in their rotating structure being disrupted, which can lead to serious failures. Intelligent rolling bearing fault diagnosis is a critical part of ensuring opera...
Network pruning has been widely used in model compression techniques, and offers a promising prospect for deploying models on devices with limited resources. Nevertheless, existing pruning methods merely consider the importance of feature maps and fi...
Despite the impressive performance of existing image compression algorithms, they struggle to balance perceptual quality and high image fidelity. To address this issue, we propose a novel invertible neural network-based remote sensing image compressi...
Objective. The wearable electrocardiogram (ECG) monitoring device is an effective tool for diagnosing intermittent heart diseases. However, the massive amount of ECG data increases power consumption during wireless transmission, thereby reducing the...
When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorithm to compress the d...
Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time....
It is hard to directly deploy deep learning models on today’s smartphones due to the substantial computational costs introduced by millions of parameters. To compress the model, we develop an
Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scal...
The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deplo...
The utilization of ordinary Portland cement (OPC) in conventional concretes is synonymous with high carbon emissions. To remedy this, an environmentally friendly concrete, alkaline-activated slag concrete (AASC), where OPC is completely replaced by g...
Just-Noticeable Difference (JND) in an image/video refers to the maximum difference that the human visual system cannot perceive, which has been widely applied in perception-guided image/video compression. In this work, we propose a Binary Decision-b...
The presented research aims to find a data-driven formula for the compressive stress–strain behaviour of closed-cell aluminium foams with respect to the apparent density of the material. This is a continuation and new development of an earlier...
Box compression strength (BCS) is a critical parameter for assessing the performance of shipping containers during transportation. Traditionally, BCS evaluation relies heavily on physical testing, which is both time-consuming and costly. These limita...
The success of the convolutional neural network (CNN) comes with a tremendous growth of diverse CNN structures, making it hard to deploy on limited-resource platforms. These over-sized models contain a large amount of filters in the convolutional lay...
In deep coal mine strata, characterized by high ground stress and extensive fracturing, predicting the strength of fractured rock masses is crucial for stability analysis of the surrounding rock in coal mine strata. In this study, rock samples were o...
Accurate and reliable estimation of the axial compression capacity can assist engineers toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are gaining popularity in diverse structural applications. This study pro...
The prestressed concrete-filled double skin steel tube (CFDST) lattice tower has emerged as a promising structural solution for large-capacity wind turbine systems due to its superior load-bearing capacity and economic efficiency. The steel–con...
The concept of action recognition in smart security heavily relies on deep learning and artificial intelligence to make predictions about actions of humans. To draw appropriate conclusions from these hypotheses, a large amount of information is requi...
Excessive exhaust backpressure (EBP) in modern diesel engines disrupts gas exchange, increases residual gas fraction (RGF), and reduces combustion efficiency. Traditional experimental approaches, including simulations and bench testing, are often tim...
Convolutional Neural Networks (CNNs) are broadly used in numerous applications such as computer vision and image classification. Although CNN models deliver state-of-the-art accuracy, they require heavy computational resources that are not always aff...
A wireless local area network (WLAN) is an important type of wireless network which connotes different wireless nodes in a local area network. Network traffic or data traffic in a WLAN is the amount of network packets moving across a wireless network...
The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR...
With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. Accord...
In this article a scheme for image transmission over Wireless Sensor Networks (WSN) with an adaptive compression factor is introduced. The proposed control architecture affects the quality of the transmitted images according to: (a) the traffic load...
With the development of Convolutional Neural Networks (CNNs), there is a growing requirement for their deployment on edge devices. At the same time, Compute-In-Memory (CIM) technology has gained significant attention in edge CNN applications due to i...
Sentence compression is a natural language-processing task that produces a short paraphrase of an input sentence by deleting words from the input sentence while ensuring grammatical correctness and preserving meaningful core information. This study i...
In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the lit...
Digital images play a particular role in a wide range of systems. Image processing, storing and transferring via networks require a lot of memory, time and traffic. Also, appropriate protection is required in the case of confidential data. Discrete a...
A common technique for compressing a neural network is to compute the k-rank
Video surveillance systems play an important role in underground mines. Providing clear surveillance images is the fundamental basis for safe mining and disaster alarming. It is of significance to investigate image compression methods since the under...
The article presents a novel application of the most up-to-date computational approach, i.e., artificial intelligence, to the problem of the compression of closed-cell aluminium. The objective of the research was to investigate whether the phenomenon...
With the goal of addressing the issue of image compression in wireless multimedia sensor networks with high recovered quality and low energy consumption, an image compression and transmission scheme based on non-negative matrix factorization (NMF) is...
In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large stora...
In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have...
Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolutio...
Convolutional neural networks (CNNs) offer significant advantages when used in various image classification tasks and computer vision applications. CNNs are increasingly deployed in environments from edge and Internet of Things (IoT) devices to high-...
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