Expensive Hardware Research Articles

Passive Stylus Tracking: A Systematic Literature Review

Passive stylus systems offer a simple and cost-effective solution for digital input, compatible with a wide range of surfaces and devices. This study reviews the domain of passive stylus tracking on passive surfaces, a topic previously underexplored in existing literature. We answer four key research questions: what type of systems exist in this domain, what methods do they use for tracking styli, how accurate are they, and what are their limitations? A systematic literature review resulted in 24 papers describing passive stylus systems. Their methods primarily fall into four categories: monocular cameras with image processing, multiple camera systems with image processing, machine learning systems using high-speed cameras or motion capture hardware, and radio frequency signal-based systems with signal processing. We found the system with the highest accuracy used a single monocular camera. In many systems, markers such as retroreflective spheres, tape, or fiducial markers were used to enhance the feature matching. We have also found stagnation and in some cases, regression in the precision and reliability of these systems over time. The limitations in these systems include the lack of varied stylus form factor support, the restriction to specific camera positions and angles, and the requirement of expensive hardware. Given these findings, we discuss the important characteristics and features of passive stylus systems and propose ways forward in this field.

No Ear Left Behind: Wireless Earbuds for Low-Cost Hearing Screening

Hearing loss is particularly harmful for language acquisition and neuro-development if it is left undetected in early childhood. Newborn hearing screening technologies using otoacoustic emissions (OAE) rely on detecting soft sounds generated by a healthy cochlea. High-income countries like the United States frequently implement hearing screening programs for every child at birth. However, such universal hearing screening is significantly less common in low- and middle-income countries, partly due to the conventional wisdom that the test requires sensitive and expensive acoustic hardware that costs thousands of dollars. In our MobiSys paper[1], we present the first wireless earbud design for low-cost hearing screening. Our hardware-software system reliably performs hearing screening using low-cost acoustic hardware, while being in the form-factor of a wireless earbud (Figure. 1a,b). The earbud hardware is designed to work across a large age range from newborns to adults. By developing low-cost and open-source wearable technology, our work may help address global health inequities in hearing screening by democratizing these medical devices.

Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks

In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline.

A novel technique to resolve directional ambiguity for Particle Streak Velocimetry

In this study, a novel streak direction-resolving algorithm is introduced to determine the direction of the flow field for a single image frame with multiple streaks. The streak direction was resolved by varying its intensity from 100% to 50% of the total intensity of the streak image. This technique was applied to two main types of flows parallel and Hill’s vortex flows, parallel flows were divided into further two types constant velocity parallel flow and accelerating parallel flow. The purpose of using different types of flows was to test the robustness of the algorithm with easy and complex flows. The performance of the algorithm was checked by the angular deviation between the true and least-square fitted velocity vectors. The number of correct synthetic streak directions was measured with the success rate in percentage. A high success rate means low angular deviation and a high number of velocity vectors with correct direction was obtained. In this research, four different types of image formats were considered DP (double precision), 16-bit (without noise), 16-bit (1.0% noise), 16-bit (5.0% noise), and 8-bit (without noise) and the best results were obtained for DP and 16-bit image formats. The results of the parallel flows indicated a 100% streak direction success rate, Hill’s vortex was a type of complex flow therefore, the algorithm hard to resolve some streak directions due to very low velocities (less than 0.1 px (pixel)/interval) and very high-velocity gradients (greater than 52 px/interval). The observation shows that Hill’s vortex synthetic streak images were resolved with a success rate of 92.76% which means that the majority of the synthetic streaks were resolved. Experimental analysis was also done by using the PDMS microchannel setup. Intensity variation streaks were recorded with long camera exposure time and for maintaining the intensity variation, as the LED switched on maximum illumination was started, and continuous decrement in illumination was set by switching off at 50% of the total illumination intensity. The best results were achieved with a 94% success rate. Therefore, the proposed novel approach can be used with less expensive hardware for image processing with a single image frame and is useful for multiple applications.

Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot's position and environmental information, and changes the robot's path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.

Stand-off concealed firearm detection using motion tracking and convolutional neural networks

<p>The standoff detection of concealed firearms is crutial in managing public security in public spaces. Currently employed standoff concealed weapon detection techniques employ electromagnetic wave imaging which has been found to be extremely slow and may require expensive hardware and may not be applicable in public open spaces. Inorder to maintain safety in open spaces, artificial intelligence enabled video surveillance systems have been widely adopted. This poses an opportunity to explore video surveillance cameras as concealed weapon detectors. A review of existing video surveillance based automated weapon detection approaches discovered that the focus was on the detection of unconcealed firearms leaving a gap in the detection of concealed firearms. This study addresses the aforementioned gap by providing a standoff concealed firearm detection approach on video based on skeletal-based human motion tracking and convolutional neural networks. The motion of armed and unarmed persons was tracked using a depth camera and further classified using convolutional neural networks model. The developed model reported 100% accuracy, precision and recall scores. These results outperformed results obtained from traditional machine learning models therefore highlighting the superior capability of the proposed approach for concealed firearm detection on video to complement the efforts of human video surveillance operators.</p>

Computer vision in the food industry: Accurate, real-time, and automatic food recognition with pretrained MobileNetV2

In contemporary society, the application of artificial intelligence for automatic food recognition offers substantial potential for nutrition tracking, reducing food waste, and enhancing productivity in food production and consumption scenarios. Cutting-edge technologies such as Computer Vision and Deep Learning are highly beneficial, enabling machines to learn automatically, thereby facilitating automatic visual recognition. Despite some research in this field, the challenge of achieving accurate automatic food recognition quickly remains a significant research gap. Some models have been developed and implemented, but maintaining high performance swiftly, with low computational cost and low access to expensive hardware accelerators, is still an area that needs further exploration and improvement. This study employs the pre-trained MobileNetV2 model, which is cost-efficient and fast, for automatic food recognition on the public Food11 dataset, comprising 16643 images. It also utilizes various tools and techniques such as dataset understanding, transfer learning, data augmentation, regularization, dynamic learning rate, hyperparameter tuning, and consideration of images in different sizes to cut time and enhance the model’s performance and robustness. These techniques aid in choosing appropriate metrics, achieving better performance, avoiding overfitting and accuracy fluctuations, speeding up the model, and increasing the generalization of findings, making the study and its results applicable to real-world applications. Despite MobileNetV2′s simpler structure and fewer trainable parameters compared to some deep-dense models in the deep learning area, it quickly achieved approximately 92.97 % accuracy. This underscores the model’s potential for implementation in real-world applications, which is the main intention of this study.

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

Efficient neural implicit representation for 3D human reconstruction

High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving metaverse. Even though they work well in small spaces, conventional methods for reconstructing 3D human motion frequently require the use of expensive hardware and have high processing costs. This study presents HumanAvatar, an innovative approach that efficiently reconstructs precise human avatars from monocular video sources. At the core of our methodology, we integrate the pre-trained HuMoR, a model celebrated for its proficiency in human motion estimation. This is adeptly fused with the cutting-edge neural radiance field technology, Instant-NGP, and the state-of-the-art articulated model, Fast-SNARF, to enhance the reconstruction fidelity and speed. By combining these two technologies, a system is created that can render quickly and effectively while also providing estimation of human pose parameters that are unmatched in accuracy. We have enhanced our system with an advanced posture-sensitive space reduction technique, which optimally balances rendering quality with computational efficiency. In our detailed experimental analysis using both artificial and real-world monocular videos, we establish the advanced performance of our approach. HumanAvatar consistently equals or surpasses contemporary leading-edge reconstruction techniques in quality. Furthermore, it achieves these complex reconstructions in minutes, a fraction of the time typically required by existing methods. Our models achieve a training speed that is 110× faster than that of State-of-The-Art (SoTA) NeRF-based models. Our technique performs noticeably better than SoTA dynamic human NeRF methods if given an identical runtime limit. HumanAvatar can provide effective visuals after only 30 s of training. Please visit https://github.com/HZXu-526/Human-Avatar for further demo results and code.

Integrating Artificial Intelligence Into the Visualization and Modeling of Three-Dimensional Anatomy in Pediatric Surgical Patients

BackgroundPediatric surgeons often treat patients with complex anatomical considerations due to congenital anomalies or distortion of normal structures by solid organ tumors. There are multiple applications for three-dimensional visualization of these structures based on cross-sectional imaging. Recently, advances in artificial intelligence (AI) applications and graphics hardware have made rapid 3D modelling of individual structures within the body accessible to surgeons without sophisticated and expensive hardware. In this report, we provide an overview of these applications and their uses in preoperative planning for pediatric surgeons. MethodsDeidentified DICOM files containing cross-sectional imaging of preoperative pediatric surgery patients were loaded from an institutional PACS database onto a secure PC with dedicated graphics and AI hardware (NVIDIA Geforce RTX 4070 laptop GPU). Visualization was obtained using an open-source imaging platform (3D Slicer). AI extensions to the platform were utilized to delineate the anatomy of interest. ResultsSegmentations of skeletal and visceral structures within a scan were obtained using the TotalSegmentator extension with an average processing time under 5 min. Additional AI modules were utilized for providing detailed mapping of the airways (AirwaySegmentation), lungs (Chest Imaging Platform), liver (SlicerLiver), or vasculature (SlicerVMTK). Other extensions were used for delineation of tumors within the hepatic parenchyma (MONAI Auto3DSeg) and hepatic vessels (RVesselX). ConclusionAI algorithms for image interpretation and processors dedicated to AI functions have significantly decreased the technical and financial requirements for obtaining detailed three-dimensional images of patient anatomy. Models obtained using AI algorithms have potential applications in preoperative planning, surgical simulation, patient education, and training. Level of EvidenceV, Case Series, Description of Technique.

An Allocation and Optimization Procedure for ARPANET Network Reliability Using Particle Swarm Optimization

In this paper we study the expansion of the telecommunications industry and the complexity of communications infrastructures. For ARPA networks such as ARPANET, the provision of integrated broadband service is essential. The new technology used in ARPANETs and changing traffic patterns have attracted much research attention to network topological design. Most topological design researchers have proposed techniques based on expensive exchange-based hardware. We presented a realistic ARPA network model in this study. ARPA network architecture includes network optimization. We proposed to apply particle swarm optimization (PSO) to enhance the ARPA network, and we also estimated the appropriate distribution of each system component using an exponential cost function. The results of the study show that the optimizer (PSO) had good results.

Key-Region-Based UAV Visual Navigation

Abstract. Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping and localization has served as a reliable localization method for many applications, but the push to run more applications on less expensive hardware becomes extremely limiting. In this paper, we present a novel approach for visual geolocalization and navigation that improves landmark detection reliability while reducing reference map complexity. Similar to prior techniques, we use the process of point based matching schemes to solve for the image-to-map transform. The critical difference is that we use object detection to identify key-regions instead of keypoints. During an initial flight key-regions are mapped into an identity dictionary with their geolocations and few-shot learning encoded descriptors. Then on subsequent flights, key-regions are detected and matched using the identity dictionary for re-identification. Using the identified vehicles as key-regions, the results show that the proposed key-region based localization produces GPS like localization while maintaining a higher resilience to image noise compared to keypoint-based techniques.

Continuous 3D printing of metal structures using ultrafast mask video projection initiated vat photopolymerization

Additive manufacturing (AM) has recently emerged as the go-to technology for producing high-quality metal components to address cutting-edge research and industrial challenges. However, existing metal AM technologies operate on a layer-by-layer basis, giving rise to undesirable "staircasing effects" that result in the anisotropic behavior. Additionally, conventional metal printing methods are constrained by printing times, expensive hardware, and limited resolution control. In response to these limitations, this work is dedicated to the development of a cost-effective, ultra-fast layerless metal AM process for crafting intricate 3D metal structures. Low viscous resin was developed to enable continuous printing of metal precursors via mask video projection-based vat photopolymerization, which enable tunable physical properties and superior surface quality. Based on experimental findings, high-density meso- and microscale metallic parts can be printed at speeds orders of magnitude faster than commercial metal AM technologies. Furthermore, printed metallic components display a uniform morphology and microstructure with consistent grain size distribution. A comparative analysis of the surface quality between layer-based and layer-less printed metal structures were conducted. This work has the potential to revolutionize not only the field of AM but also various industries reliant on high-quality metal components, such as aerospace, automotive, and medical devices.

Data-Driven ICS Network Simulation for Synthetic Data Generation

Data-Driven ICS Network Simulation for Synthetic Data Generation

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them using a predetermined deep learning algorithm. Data collection in this research used scraping techniques to retrieve data from the unstructured wikipedia website and then processed it into structured data. The method used in this research is a standard cross-industry process for data mining by performing phases of data collection, data processing, proposed algorithms, testing and evaluation. The algorithm applied is deepwalk, kmeans, girvan newman. By doing this research, it is expected to provide knowledge about the deep learning approach for data representation of the wikipedia pages knowledge graph and can help users find similar wikipedia pages and enrich literacy on knowledge graph analysis.

Transmit antenna selection in M-MIMO system using metaheuristic aided model

Massive MIMO (M-MIMO) devices are the key tool to meet the performance stards established for 5G-wireless communication. However, more Radio Frequency (RF) chains needed in base station (BS) with a huge count of transmitting antennas, involve expensive hardware and computing complexities. In order to decrease the RF chains needed in BS, this work intended to use the optimal transmit antenna selection (TAS) strategy. This strategy is gaining a lot of interest since the optimization algorithm aids in the ability to enhance the system performance considerably the efficiency and secrecy rate. This work proposes a novel Coati Adopted Pelican Optimization (CA-PO) for choosing the optimal TA by considering efficiency as well as secrecy rate. In addition, the CA-PO algorithm makes the decision on which antenna to be elected. At last, the supremacy of CA-PO-based TAS is proven from the analysis regarding secrecy rate and EE analysis. Accordingly, the proposed CA-PO method for MF for set up 2 has attained a higher EE of 0.976; whereas, the DMOA, COA, MRFO, POA and BEA techniques have got a relatively lower EE of 0.968.

A GNU radio-based design for GPS software simulation source

GPS (Global Positioning System) plays a crucial role in modern navigation and positioning applications. Traditional research and development of GPS signals require expensive hardware equipment, limiting the flexibility and scalability of the system. In this paper, an open-source software called GNU Radio is utilized to construct a GPS signal simulation system, leveraging its rich modules and signal processing libraries. The GPS baseband signals are generated using signal processing modules provided by GNU Radio. Appropriate noise and interference are added to simulate the signal characteristics in real-world environments. Both single-channel and multi-channel signal models are presented. By comparing the generated GPS signals with those from hardware-based simulators, the results demonstrate that the GPS software simulator based on GNU Radio can produce high-quality GPS signals with excellent flexibility and adjustability.

Advancing Video Synchronization with Fractional Frame Analysis: Introducing a Novel Dataset and Model

Multiple views play a vital role in 3D pose estimation tasks. Ideally, multi-view 3D pose estimation tasks should directly utilize naturally collected videos for pose estimation. However, due to the constraints of video synchronization, existing methods often use expensive hardware devices to synchronize the initiation of cameras, which restricts most 3D pose collection scenarios to indoor settings. Some recent works learn deep neural networks to align desynchronized datasets derived from synchronized cameras and can only produce frame-level accuracy. For fractional frame video synchronization, this work proposes an Inter-Frame and Intra-Frame Desynchronized Dataset (IFID), which labels fractional time intervals between two video clips. IFID is the first dataset that annotates inter-frame and intra-frame intervals, with a total of 382,500 video clips annotated, making it the largest dataset to date. We also develop a novel model based on the Transformer architecture, named InSynFormer, for synchronizing inter-frame and intra-frame. Extensive experimental evaluations demonstrate its promising performance. The dataset and source code of the model are available at https://github.com/yuxuan-cser/InSynFormer.

A multi-granularity ensemble algorithm for medical image classification based on broad learning system

Medical image classification is an essential task in the fields of computer-aided diagnosis and medical image analysis. In recent years, researchers have made extensive work on medical image classification by computer vision techniques. However, most of the current work is based on deep learning methods, which still suffer from expensive hardware resources, long time consuming and a lot of parameters to be optimized. In this paper, a multi-granularity ensemble algorithm for medical image classification based on broad learning system is proposed, which is an end-to-end lightweight model. On the one hand, the proposed method is designed to address the problem of weak image feature learning ability of broad learning system. The convolution module with fixed weights based on transfer learning is introduced as a feature extractor to extract fusion features of medical images. On the other hand, the multi-granularity ensemble framework is proposed, which learn the fusion features of medical images from fine-grained to coarse-grained respectively, and the prediction results at different granularity levels are integrated by ensemble learning. In this way, the bottom local features can be sufficiently considered, while the global features can also be taken into account. The experimental results show that on the MedMNIST dataset (containing 10 sub-datasets), the proposed method can shorten the training time by tens of times while having similar accuracy to deep convolutional neural networks. On the ChestXRay2017 dataset, the proposed method can achieve an accuracy of 92.5%, and the training time is also significantly better than other methods.

Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction.

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.