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Technical Report_ Feasibility study of an autonomous exploration legged robot for building mapping-v1

Technical report summarizing the work of KIEM Spotter

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Utilizing Drone-Based Ground-Penetrating Radar for Crime Investigations in Localizing and Identifying Clandestine Graves

The decomposition of a body is influenced by burial conditions, making it crucial to understand the impact of different conditions for accurate grave detection. Geophysical techniques using drones have gained popularity in locating clandestine graves, offering non-invasive methods for detecting surface and subsurface irregularities. Ground-penetrating radar (GPR) is an effective technology for identifying potential grave locations without disturbance. This research aimed to prototype a drone system integrating GPR to assist in grave localization and to develop software for data management. Initial experiments compared GPR with other technologies, demonstrating its valuable applicability. It is suitable for various decomposition stages and soil types, although certain soil compositions have limitations. The research used the DJI M600 Pro drone and a drone-based GPR system enhanced by the real-time kinematic (RTK) global positioning system (GPS) for precision and autonomy. Tests with simulated graves and cadavers validated the system’s performance, evaluating optimal altitude, speed, and obstacle avoidance techniques. Furthermore, global and local planning algorithms ensured efficient and obstacle-free flight paths. The results highlighted the potential of the drone-based GPR system in locating clandestine graves while minimizing disturbance, contributing to the development of effective tools for forensic investigations and crime scene analysis.

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Scarcity leads to .... affection!

Where in the 90s everyone had a hi-fi stereo sound system to play their favourite records or CDs, now we hardly have any favourite music, let alone a decent installation to play it on. Although we can hear everything ever made through Spotify or iTunes, we usually listen through a speaker on the smartphone or tablet. Gone is both the pleasure in music and the pleasure of building a personal music collection and thus identity. The same goes for books (for example Kobo plus). The presentation of text - fonts, paragraphs, and images - in eBooks cannot be compared to the beautiful formatting of printed books. The easier we communicate textually, the less we read. SMS and WhatsApp are wonderful examples of "garbled" textual communication. Tis text focusses on why this may be the case.

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Resource-Efficient Image Buffer Architecture for Neighborhood Processors

Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.

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Resource-Efficient Image Buffer Architecture for Neighborhood Processors

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MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision.

Background: Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results: We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion: MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.

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Movement registration and analysis for fall risk assessment in the hospital: lessons from an observational pilot study

BackgroundA valuable opportunity for reducing the fall incidence in hospitals, is alerting nurses when a patient is about to fall. For such a fall prevention system, more knowledge is needed on what occurs right before a fall. This can be achieved with a stereo camera that automatically detects (and records) dangerous situations.MethodsInpatients with a high risk of falling are selected for inclusion. A fall-risk questionnaire is administered and falls are logged during their stay. A stereo-camera (3D BRAVO-EagleEye system) is mounted in the ceiling and monitors the bed with surroundings. A baseline recording is made to improve the algorithms behind the alert system. When a fall or dangerous situation is detected, monitoring data preceding the incident is stored. Data is analyzed to assess 1) the quality of the system and 2) the prevalence of dangerous situations. Interviews with senior nurses are included in the evaluation.ResultsData collection is ongoing (Currently n=18; falls=1), and currently consists of ±62 hour of baseline recordings and ±24 hour of event-based recordings. These recordings include false positives as well as actual high risk situations.ConclusionsDespite the initial enthusiasm of the participating departments, inclusion of participants is slow, and the number of falls lower than expected. Possible explanations for this have been discussed with the involved senior nurses. With the monitoring data we gained more insight into the occurrence of dangerous situations, but to be able to reliably predict falls, more data on actual fallsshould be recorded.

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Movement registration and analysis for fall risk assessment in the hospital: lessons from an observational pilot study

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RARE: people detection in crowded passages by range image reconstruction

In this paper, we address the problem of people detection and tracking in crowded scenes using range cameras. We propose a new method for people detection and localisation based on the combination of background modelling and template matching. The method uses an adaptive background model in the range domain to characterise the scene without people. Then a 3D template is placed in possible people locations by projecting it in the background to reconstruct a range image that is most similar to the observed range image. We tested the method on a challenging outdoor dataset and compared it to two methods that each shares one characteristic with the proposed method: a similar template-based method that works in 2D and a well-known baseline method that works in the range domain. Our method performs significantly better, does not deteriorate in crowded environments and runs in real time.

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A Simulator for Depth Estimation

Met toestemming overgenomen uit Microniek, 2020, nr. 5 A stereo-vision system that was developed for application in mobile robots turned outto lack depth resolution in the background of the pictures. A simulator was built togain understanding of the parameters that influence depth estimation in stereo vision.In this article we will explain how these properties influence depth resolution andprovide a link to the webtool that was made to interactively observe and evaluate theresulting depth resolution when the parameters are varied. This tool makes it possibleto find the correct hardware that provides the resolution required, or to determinethe resolution for specific hardware.

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People counting with stereo cameras

People counting is a challenging task with many applications. We propose a method with a fixed stereo camera that is based on projecting a template onto the depth image. The method was tested on a challenging outdoor dataset with good results and runs in real time.

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Technical Report_ Feasibility study of an autonomous exploration legged robot for building mapping-v1