Distant Objects Research Articles

Analysis of Hybrid Warfare Elements in the 2023 Asymmetric War between Hamas and Israel

The conflict between Hamas and Israel has created a complex asymmetric warfare environment, giving rise to elements of hybrid warfare involving both military and non-military tactics. Hybrid warfare is not a new idea. The focus is primarily on the ability to target distant objects and processes through unconventional military methods, especially for state and military functions. Hybrid warfare, known as an asymmetric approach, seeks to achieve major impact using simple methods, such as stopping an enemy's military operations or cutting off public political support. The operational environment of hybrid warfare consists of a series of interrelated areas of conflict, such as military, economic, social, information, diplomatic, trade, science, and technology. Since states involved in hybrid war do so by undertaking centralized actions in all areas of conflict subject to a single goal, it follows that the actions undertaken by states involved in hybrid war must also be centralized in all areas. Hybrid warfare requires both conventional and unconventional instruments of power and means of subversion. This research aims to analyze the elements of hybrid war in the context of an asymmetric war between Hamas and Israel in 2023.

Haptic Magnetism.

New interactions are often developed by mimicking the real world. Therefore, many researchers in haptics have focused on creating a realistic experience of contact between users and objects. However, dispensing with mimicry may allow us to develop novel haptic interactions. We present Haptic Magnetism, an interaction modality that delivers sensations of distant objects through tactile stimulation and enables interactions through pseudo-magnetic attraction and repulsion. To show the feasibility of Haptic Magnetism, we designed 12 pseudo-magnetic stimuli and assessed them in two studies. In the first study, we show that participants gain a sense of distant objects. In the second study, we evaluate a subset of stimuli to show that participants can interact with the objects based on experiences of pseudo-magnetic attraction and repulsion. Finally, we discuss how Haptic Magnetism supports guiding movements, nudging users, and revealing affordances.

Analysis of a capacitance tomography system for object detection and localization

Abstract To ensure safe human-robot cooperation, it is necessary not only to measure the force exerted during contact, but also to monitor an approaching action. This would allow avoiding any unintentional contact through evasive maneuvers and thus prevent possible injuries. In our contribution we analyze a localization system based on capacitance tomography where an approaching object constitutes an electrode of a multi-electrode capacitance. The system processes measured voltages made on the surface of a conductive layer. From this, the location of the capacitive coupling between the object and the layer can be reconstructed, and the position of the object can be estimated. The system’s performance is further analyzed using the Neyman–Pearson detector. To define the performance of the system, for object detection only, the influence of object distance, object area, and number of measurements used on the probability of detection is examined.

Design of a Drowsiness Prevention Helmet with Vibration and IoT-Based Theft Detection Alarms

Ensuring safety while riding a motorbike is an imperative task. Currently, safety products such as helmets have the capability to provide protection to users without the additional feature of issuing warnings. Consequently, a preemptive alert system is developed to offer timely notifications to drivers. The experimental setup involves the utilization of a Max30100 sensor that is linked to a microcontroller and integrated into a helmet. The objective of this final project is to offer a timely alert to the rider and utilize the Max30100 sensor for pulse detection in order to ascertain the normalcy of the rider's pulse. In instances where the rider encounters tiredness and fatigue, it is common for the pulse intensity to exhibit a reduction. The Blynk application presents the detection pulse findings on the smartphone screen, while the buzzer on the helmet will activate in response to vibrations and sounds once the pulse has diminished. Based on testing, the average pulse rate on quiet road conditions is 78.58 BPM. On busy road conditions, the average pulse rate is 73.25 BPM. While in traffic conditions, the average pulse rate is 73.5 BPM. The helmet theft detector uses a Sharp GP2Y0A21 sensor that can only detect object distances up to 10 cm.

Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation

The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This research has the main aim of introducing a superior object detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involve obstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution in presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives.

NeXtFusion: Attention-Based Camera-Radar Fusion Network for Improved Three-Dimensional Object Detection and Tracking

Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep Camera-Radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient Camera-Radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.

Development of GWIDO: An Augmented Reality-based Mobile Application for Historical Tourism

This research aimed to design and reconstruct a business model for an augmented reality (AR) camera mobile application for historical tourism at Keraton Kasepuhan Cirebon. The goal was to utilize AR technology to provide an immersive and informative experience for tourists. The research addressed several main problems, including navigation and historical information through object tracking, by implementing an online application with features such as Indonesian and English language instructions to better serve domestic and foreign tourists. The research also aimed to investigate the benefits of using AR technology for object tracking and navigation and to explore how these aspects could be related to creating a formula that supports each other in addressing the formulated problems. Through the development of the GWIDO application, a positive impact on the development of historical tourist attractions was observed. This can be seen from the usefulness of its features such as AR navigation, which can be used as a virtual guide. The data collected was used to design and reconstruct the business model, which was implemented and tested to collect additional data for analysis. The final results of the research showed that the AR camera mobile application was effective in providing an immersive and informative experience for tourists. The redesigned business model improved the utilization of AR technology in the tourism industry. Based on the test results, the average response time for object distance between 0.1 meters to 0.5 meters was between 1.45 to 2.07 seconds, and the average time for object distance from visitors was between 3.15 to 4.71 seconds with a confidence level of 95%. Meanwhile, testing for navigation features using augmented reality is very dependent on the internet signal used on the user's device. The level of accuracy of objects that have been placed at certain coordinates is determined by how well the internet network performs, allowing objects to appear precisely according to their coordinates.

Social Implementation and Measurement Accuracy Verification of Non-contact Biological Monitors

In this research, for the purpose of social implementation, we conducted a near-miss demonstration experiment using a car driving in the city and a drive-simulator. Next, we conducted a demonstration experiment to evaluate the reliability of biometric information measured on patients in a medical facility's recuperation ward and residents of a special nursing home. NBC-1100 emits radar waves from a distance of up to 3 meters from an object and uses the reflected waves to measure biological information such as pulse waves and breathing waves. The multi-element pyroelectric effect was used to measure body temperature by detecting infrared radiation emitted from distant objects. This device is unique in that it can measure biological information without being restrained while wearing clothes. In the demonstration test, simultaneous measurements were conducted on nine healthy men aged 45 to 65 using a pharmaceutically approved product (μBP-mp) and a prototype non-contact biological monitor (NBC-1100 manufactured by K&S Co., Ltd.). The demonstration experiments at medical institutions and nursing care facilities were conducted with the consent of residents and their families and were conducted on 30 men and women between the ages of 70 and 94 who were undergoing treatment or in need of care. The tests were conducted on residents with chronic diseases such as dilated gastrostomies, symptomatic epilepsy, hypertension, Alzheimer's disease, and progressive supranuclear palsy. The evaluation method was simultaneous measurement using a master meter (μBP-mp) and a test meter (NBC-1100).

BEV-MAE: Bird’s Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios

Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on large-scale labeled data, whose collection can be expensive and time-consuming. Self-supervised pre-training is an effective and desirable way to alleviate this dependence on extensive annotated data. In this work, we present BEV-MAE, an efficient masked autoencoder pre-training framework for LiDAR-based 3D object detection in autonomous driving. Specifically, we propose a bird's eye view (BEV) guided masking strategy to guide the 3D encoder learning feature representation in a BEV perspective and avoid complex decoder design during pre-training. Furthermore, we introduce a learnable point token to maintain a consistent receptive field size of the 3D encoder with fine-tuning for masked point cloud inputs. Based on the property of outdoor point clouds in autonomous driving scenarios, i.e., the point clouds of distant objects are more sparse, we propose point density prediction to enable the 3D encoder to learn location information, which is essential for object detection. Experimental results show that BEV-MAE surpasses prior state-of-the-art self-supervised methods and achieves a favorably pre-training efficiency. Furthermore, based on TransFusion-L, BEV-MAE achieves new state-of-the-art LiDAR-based 3D object detection results, with 73.6 NDS and 69.6 mAP on the nuScenes benchmark. The source code will be released at https://github.com/VDIGPKU/BEV-MAE.

Invisible Backdoor Attack against 3D Point Cloud Classifier in Graph Spectral Domain

3D point cloud has been wildly used in security crucial domains, such as self-driving and 3D face recognition. Backdoor attack is a serious threat that usually destroy Deep Neural Networks (DNN) in the training stage. Though a few 3D backdoor attacks are designed to achieve guaranteed attack efficiency, their deformation will alarm human inspection. To obtain invisible backdoored point cloud, this paper proposes a novel 3D backdoor attack, named IBAPC, which generates backdoor trigger in the graph spectral domain. The effectiveness is grounded by the advantage of graph spectral signal that it can induce both global structure and local points to be responsible for the caused deformation in spatial domain. In detail, a new backdoor implanting function is proposed whose aim is to transform point cloud to graph spectral signal for conducting backdoor trigger. Then, we design a backdoor training procedure which updates the parameter of backdoor implanting function and victim 3D DNN alternately. Finally, the backdoored 3D DNN and its associated backdoor implanting function is obtained by finishing the backdoor training procedure. Experiment results suggest that IBAPC achieves SOTA attack stealthiness from three aspects including objective distance measurement, subjective human evaluation, graph spectral signal residual. At the same time, it obtains competitive attack efficiency. The code is available at https://github.com/f-lk/IBAPC.

Rancang Bangun Sistem Automatic Keamanan Jarak Pada Truk Trailer Menggunakan Sensor Jarak Berbasis Internet Of Things

One of the causes of accidents that occur on the road is a condition where there is an error in the human factor which lacks focus in observing the circumstances around the vehicle being driven. For drivers of large vehicles such as trucks, they have areas where sight cannot reach or what is commonly called a blind spot, which is due to the large dimensions so that the viewing angle is limited. This situation results in the risk of large vehicles hitting other drivers or other objects that are in the blind spot area. This study aims to design a tool to assist drivers in providing information and warnings if there is an object in the blind spot area. This tool uses an ultrasonic sensor as an object detector, as well as an information system that is sent via wifi and displayed on the Blynk application to determine the distance and location of objects. Testing is carried out by applying it directly to the vehicle to determine the performance of the tool on the vehicle

JDECMC: Improving JDE based multi-object tracking with Camera Motion Compensation

Jointly Detection and Embedding (JDE) based approaches estimate bounding boxes and embedding features of objects with a single network in multi-object tracking. Due to the crowded scenarios or non-rigid camera motion, the bounding box location in the image can shift dramatically, which affects the performance of the trackers. In addition, JDE-based approaches fuse the target appearance information and location information by applying the same rule, which could fail to associate when the target is lost or occluded. To address these issues, we propose a simple yet effective architecture based on JDE, called JDECMC. Our approach incorporates the embedding and location distance of the object, which combines embedding cosine distance and location distance of objects, as well as Camera Motion Compensation (CMC) to predict the correct location of the bounding box. This is especially useful in scenarios where dynamic camera motion can cause the bounding box to shift significantly. By enhancing the accuracy of the tracking system, JDECMC achieves a MOTA of 74.2%, 72.0%, and 90.5% on the MOT17, MOT20 and DanceTrack benchmark datasets, respectively, while also scoring an IDF1 of 72.5%, 72.4%, and 79.8% on those benchmarks. The experimental results demonstrate our effectiveness and advantages over the other one-shot trackers. The code is available at https://github.com/Melikamuliyih/JDECMC.

Effects of the perceived temporal distance of events on mental time travel and on its underlying brain circuits.

Mental Time Travel (MTT) allows us to remember past events and imagine future ones. According to previous literature, the Temporal Distance of events affects MTT: our ability to order events worsens for close, compared to far, events. However, those studies established distances a-priori, albeit the way we perceive events' temporal distance may subjectively differ from their objective distance. Thus, in the current study, we aimed to investigate the effects of Perceived Temporal Distance (PTD) on the MTT ability and the brain areas mediating this process. Thirty-three healthy volunteers took part in an fMRI MTT task. Participants were asked to project themselves into the past, present, or future, and to judge a series of events as relative-past or relative-future, in relation to the adopted time location. Outside the scanner, participants provided PTD estimates for each stimulus of the MTT task. Participants' performance and functional activity were analyzed as a function of these estimations. At the behavioural level, PTD predicts the modulation of the performance for relative-past and relative-future. Bilateral angular gyrus, retrosplenial cortex, temporo-parietal region and medial, middle and superior frontal gyri mediate the PTD effect. In addition to these areas, the closer the relative-future events are perceived, the higher the involvement of left parahippocampal and lingual gyri and right cerebellum. Thus, perceived proximity of events activates frontal and posterior parietal areas, which therefore might mediate the processing of PTD in the cognitive spatial representation of time. Future proximity also activates cerebellum and medial temporal areas, known to be involved in imaginative and constructive cognitive functions.

Justification of the approach to searching for and exploring productive objects: a case study of the Krasnoleninsk and Angaro-Lena oil and gas regions

The article provides a rationale for the approach to the search and exploration of local productive zones based on the analysis of geographically distant objects of different geological structures: the Tyumen and Chora formations. The need for a joint study of the problem of localizing oil and gas deposits has been identified by comparing the patterns of location of local productive objects in remote territories that differ in their geological structure. The relevance of the study is determined by the significant degree of depletion of reserves of hydrocarbon deposits, characterized by the simplest development conditions and the need to find an approach to the study of deposits, within which the establishment and description of the patterns of deposit structure cause difficulties. The primary information for the study was drilling data, geophysical well survey results, description of core material, petrophysical survey data, information on testing and well performance dynamics, 2D seismic interpretation materials, as well as published articles. Methods of analysis and comparison were adopted as a basis in order to highlight common features of the distribution of hydrocarbon deposits in the studied areas. In the course of the study, the prerequisites were formed for the development of a comprehensive methodology for studying the structure of hydrocarbon deposits of Tyumen and Chora formations from the perspective of an approach to the exploration of complex objects, implying a joint study of the possibilities of processes described separately by both organic and inorganic hypotheses of oil and gas generation. The need to introduce an approach to geological modeling that takes into account the fractal structure of geological systems is formulated.

Green Alga-Inspired Underwater Vision Based on Light-Driven Active Ion Transport across Janus Dual-Field Heterostructures.

Natural organisms have evolved various biological ion channels to make timely responses toward different physical and/or chemical stimuli, giving guidance to construct artificial counterparts and expand the corresponding applications. They have also shown promising potential to overcome disadvantages of traditional electronic devices (e.g., energy-consuming operation and adverse humidity interference). Herein, we constructed a green alga-inspired nanofluidic system based on a Janus dual-field heterogeneous membrane (i.e., J-HM), which can function underwater as an artificial visual platform for light perception through enhanced active ion transport. The J-HM was obtained through sequentially assembled MXene and Cu-HHTP (i.e., a metal-organic framework based on the reaction between 2,3,6,7,10,11-hexahydroxytriphenylene hydrate (HHTP) and Cu2+) building units. Due to the formed temperature gradient and intramembrane electric field caused by the localized thermal excitation and efficient charge separation of J-HM under illumination, thermo-osmotic and photo-driven forces are generated for preferential cation transport from Cu-HHTP to MXene. Furthermore, unidirectional active transport can be enhanced by self-diffusion under a concentration gradient. Then, the corresponding underwater light perceptions at various light illumination conditions are explored, showing nearly a linear correlation with the light intensity. Finally, it is demonstrated that the visual platform can achieve object shape, definition, and distance recognition using a defined pixelated matrix, giving impetus to develop ionic signal transmission based sensing systems.

Observing Dusty Star-Forming Galaxies at the Cosmic Noon through Gravitational Lensing: Perspectives from New-Generation Telescopes

Gravitational lensing, a compelling physical phenomenon, offers a unique avenue to investigate the morphology and physical properties of distant and faint celestial objects. This paper seeks to provide a comprehensive overview of the current state of observations concerning strongly lensed Dusty Star-Forming Galaxies. Emphasis is placed on the pivotal role played by cutting-edge facilities like the James Webb Space Telescope and the Square Kilometer Array Observatory. These advanced instruments operating at the two opposite ends of the electromagnetic spectrum, in conjunction with the amplifying effect of gravitational lensing, promise significant steps in our understanding of these sources. The synergy between these observatories is poised to unlock crucial insights into the evolutionary path of high-redshift, dust-obscured systems and unravel the intricate interplay between Active Galactic Nuclei and their host galaxies.

Prevention and control of myopia based on meridians and zang-fu organs theory for unblocking meridians and harmonizing zang-fu organs

The prevention and control of myopia has always been a global challenge, as high myopia can lead to visual field defects, distorted vision, blindness, and other serious risks. Traditional Chinese Medicine (TCM) believes that myopia is the external manifestation of the overall physical condition in the eyes, which is related to the function of the eyes and the overall body functions. Based on the meridians and zang-fu organs theory, this article explores the associations of meridians and the zang-fu organs with the eyes, as well as their influence on myopia. Obstruction of meridians and disharmony of the zang-fu organs were found to be the fundamental pathological mechanisms that lead to the inability of the eyes to see distant objects. As one of the myopia prevention and control techniques, tuina shows significant therapeutic efficacy and green safety. On the one hand, it helps transport essence and brighten the eyes by unblocking the meridians around the eyes, neck, shoulder, and back to promote the flow of qi and blood. On the other hand, it can generate essence and brighten the eyes by harmonizing cold, heat, deficiency, and excess in the zang-fu organs to tonify qi and blood. Under a combination of localized and holistic action, this ultimately promotes the generation of essence and qi, unobstructed the flow of the visual pathway, and restoration of visual function, effectively preventing the occurrence and progression of myopia.

Optical lens optimization design and tolerance analysis for a depth camera

This paper describes a depth camera lens design composed of eight glass lenses, where the field of view is 100°, the focal length is 1.88 mm, and the F/# is 2. The lens design requires the lens to be fixed and the object distance to be fixed at −745.964 mm. The depth of focus ranges from −0.0047 mm to 0.0096 mm and can be obtained by selecting a spatial frequency of 60 lp/mm for the Through-focus MTF optimization design. The object distance ranges from negative infinity (-∞) to −250 mm (for the depth of field), the MTF is greater than 0.73 at 60 lp/mm, the lateral color difference is less than 1.58 μm, the optical distortion is less than 1%, and the relative illumination is greater than 64%. When conducting tolerance analysis, we select a spatial frequency requirement of tolerance MTF>0.3. At an object distance of −745.964 mm, the lens resolution can reach 80 lp/mm. The object distance is -∞, and the lens resolution is above 59 lp/mm. The object distance is −250 mm, and the lens resolution can reach over 43 lp/mm. In this work, the depth camera lens is designed to have an object distance shooting range of −6 m to −0.25 m. When the object distance is −6 m, the object depth resolution is 61 mm; while at the object distance of −0.25 m, the object depth resolution is 0.1045 mm.

A review study of space perception and navigation of people with low vision: is simulated low vision a reliable methodology?

The inclusion of visually impaired participants in research protocols concerning their perception of space and navigation is essential for the reliability of the results, given the strategies developed by the people concerned in everyday life. However, the diversity of visual impairments, the scarcity of participants and possible safety issues due to obstacles in the physical space induce limitations and prompt researchers to look into alternative methodologies. Simulated low vision is seen as an option. This method involves sighted participants wearing goggles with customized filters or watching processed images in virtual environments. The objective of this study is to investigate the reliability of simulated low vision as a research method to describe the space perception and navigation of people with visual impairment. We conducted a literature review and identified 36 quantitative studies on low vision spatial performance involving multiple user groups. Simulated low vision proved effective in small-scale spatial ability evaluation, such as object detection and distance estimation, but remained challenging regarding large-scale capacity, such as navigation with mobility requirement. Advances in virtual environments suggest that they are a good alternative to goggles and screen displays because of their precision in mimicking ocular problems in simulation settings. Finally, the use of head-mounted-display (HMD) by people with real low vision could open up the possibility of greater testing in safer and controlled conditions, but requires confirmation of the validity of the protocols.

METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF THE FUNCTIONING OF OPTICAL-ELECTRONIC SYSTEMS WHEN MONITORING REMOTE OBJECTS

The work proposes an approach to improving the scientific and methodological apparatus for processing information about observed distant objects. The potential possibility of increasing the accuracy of measuring the coordinates of remote objects and obtaining spectrophotometric information through a refined mathematical model of the process of functioning of optical-electronic systems, based on the possibility of restoring the original signal from the observed object by solving the Fredholm integral equation of the 1st kind, is shown. Based on a mathematical model that takes into account the structure of the optical circuit, the influence of noise and external observation conditions, the methodology for assessing the effectiveness of optical-electronic systems when observing remote objects has been improved.