External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Small Changes in Inter-Pulse-Intervals Can Cause Synchronized Neuronal Firing During High-Frequency Stimulations in Rat Hippocampus

Deep brain stimulation (DBS) traditionally utilizes electrical pulse sequences with a constant frequency, i.e., constant inter-pulse-interval (IPI), to treat certain brain disorders in clinic. Stimulation sequences with varying frequency have been investigated recently to improve the efficacy of existing DBS therapy and to develop new treatments. However, the effects of such sequences are inconclusive. The present study tests the hypothesis that stimulations with varying IPI can generate neuronal activity markedly different from the activity induced by stimulations with constant IPI. And, the crucial factor causing the distinction is the relative differences in IPI lengths rather than the absolute lengths of IPI nor the average lengths of IPI. In rat experiments in vivo, responses of neuronal populations to applied stimulation sequences were collected during stimulations with both constant IPI (control) and random IPI. The stimulations were applied in the efferent fibers antidromically (in alveus) or in the afferent fibers orthodromically (in Schaffer collaterals) of pyramidal cells, the principal cells of hippocampal CA1 region. Amplitudes and areas of population spike (PS) waveforms were used to evaluate the neuronal responses induced by different stimulation paradigms. During the periods of both antidromic and orthodromic high-frequency stimulation (HFS), the HFS with random IPI induced synchronous neuronal firing with large PS even if the lengths of random IPI were limited to a small range of 5–10 ms, corresponding to a frequency range 100–200 Hz. The large PS events did not appear during control stimulations with a constant frequency at 100, 200, or 130 Hz (i.e., the mean frequency of HFS with random IPI uniformly distributed within 5–10 ms). Presumably, nonlinear dynamics in neuronal responses to random IPI might cause the generation of synchronous firing under the situation without any long pauses in HFS sequences. The results indicate that stimulations with random IPI can generate salient impulses to brain tissues and modulate the synchronization of neuronal activity, thereby providing potential stimulation paradigms for extending DBS therapy in treating more brain diseases, such as disorders of consciousness and vegetative states

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Scanning technologies to building information modelling: a review

Building information modelling (BIM) is evolving significantly in the architecture, engineering and construction industries. BIM involves various remote-sensing tools, procedures and standards that are useful for collating the semantic information required to produce 3D models. This is thanks to LiDAR technology, which has become one of the key elements in BIM, useful to capture a semantically rich geometric representation of 3D models in terms of 3D point clouds. This review paper explains the ‘Scan to BIM’ methodology in detail. The paper starts by summarising the 3D point clouds of LiDAR and photogrammetry. LiDAR systems based on different platforms, such as mobile, terrestrial, spaceborne and airborne, are outlined and compared. In addition, the importance of integrating multisource data is briefly discussed. Various methodologies involved in point-cloud processing such as sampling, registration and semantic segmentation are explained in detail. Furthermore, different open BIM standards are summarised and compared. Finally, current limitations and future directions are highlighted to provide useful solutions for efficient BIM models.European Union's Horizon 2020 research and innovation programme | Ref. 860370Ministerio de Ciencia e Innovación | Ref. PID2019-108816RB-I0

A novel low-cost multi-sensor solution for pavement distress segmentation and characterization at night

The pavement distress measurement is a crucial aspect in guaranteeing the safety of transportation infrastructure. In this regard, we introduce a novel and cost-effective multi-sensor approach for pavement segmentation during low-light night conditions. By utilizing the low-cost Azure Kinect multi-sensor system, we generated a multi-sensor dataset that encompasses aligned IR, RGB, and depth images. Then we carried out the data annotation process on the RGB images. A total of 11,343 manual annotations were meticulously made on 791 images, which were randomly selected from a collection of 96,891 frames. Subsequently, four different deep learning-based image segmentation models were analyzed both quantitatively and qualitatively. The results indicated that the segmentation performance on the IR dataset outperformed that of the RGB dataset. The model with the highest mIoU (mean Intersection over Union) of 0.7169 was ConvNext when trained on the IR dataset. Furthermore, we proposed the use of relative height for evaluating the severity of pavement distress. On the aligned depth map, the relative height was calculated using the depth data from the corresponding pavement distress area. Additionally, a quantitative comparison between manual annotations and the results obtained through deep learning revealed that the latter was more effective in identifying more severe forms of pavement distress. Through this study, we established the feasibility of collecting pavement distress data during nighttime using a low-cost multi-sensor system