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Kristiansen Roberts posted an update a month ago
Minimally invasive surgery (MIS) is poised to be revolutionized by binocular endoscopy, a consequence of advances in stereo vision technology. However, lingering issues exist, manifesting as low reconstruction accuracy, a restricted surgical area, and inefficient computational processes. These difficulties necessitated the design of a framework dedicated to real-time, dense reconstruction within binocular endoscopy scenes. From the SGBM algorithm, an initial disparity map was obtained, which was then formulated as a disparity confidence map dataset to train StereoNet. The depth map derived from StereoNet was applied to determine the appropriate left image for each depth map, and this image was fed into the Oriented Fast and Brief-Simultaneous Localization and Mapping (ORB-SLAM) framework to execute real-time, dense reconstruction of the binocular endoscopy scene with an RGB-D camera. The proposed algorithm underwent testing in a stomach phantom and on the stomach of a live pig. The ground truth benchmark reveals a 1620 mm RMSE for the proposed algorithm, coupled with a substantial 834650 effective points in the resulting point cloud. This substantial advancement over binocular SLAM techniques ensures real-time performance while enabling dense reconstruction. Rigorous testing confirms the effectiveness of the proposed algorithm.
A TDLAS sensor, designed for online CO2 and H2O concentration monitoring, was implemented. A small, self-designed multi-pass cell, along with homemade laser drive circuitry and a data acquisition system, formed its core. The optical, electrical sections, and the gas circuit were all combined and housed within a portable carrying case possessing dimensions of 134 mm in height, 388 mm in length, and 290 mm in width. A laser current and temperature control function was realized in a TDLAS drive module (90 mm x 45 mm), possessing a temperature control precision of 14 millikelvins and a current control accuracy of 0.5 amperes, while integrating signal acquisition and demodulation. In terms of weight and power consumption, the TDLAS system registered 5 kg and 10 W, respectively. The targeting of CO2 absorption lines (2004 nm) and H2O absorption lines (1392 nm) was accomplished using distributed feedback lasers, respectively. Allan’s study indicated that carbon dioxide detection sensitivity reached 0.13 ppm and water vapour reached 37 ppm on average over 18 seconds and 35 seconds, respectively. After roughly 10 seconds, the system’s response was achieved. Atmospheric CO2 and H2O concentrations were measured over 240 hours to verify sensor performance. A comparison of experimental outcomes was conducted against data gathered from a commercial LI-7500 instrument, employing non-dispersive infrared technology. The developed gas analyzer’s measurements closely matched those of the commercial instrument, demonstrating comparable accuracy. As a result, the TDLAS sensor has great application potential in detecting atmospheric CO2 and H2O concentrations, and monitoring ecological soil fluxes.
In terms of resources, endangered languages are typically constrained, as they are non-renewable cultural assets, existing outside the realm of materiality. This language’s continued existence benefits from the effectiveness of automatic speech recognition (ASR). Yet, for languages with limited resources, native speakers are scarce, and the existence of labeled datasets is insufficient. volasertib inhibitor Due to limitations like substantial speaker dependence and overfitting, ASR performance demonstrates inaccuracies in speech recognition. The paper’s response to the shortcomings is an LSTM-Transformer-driven audiovisual speech recognition (AVSR) approach. The approach incorporates visual data, such as lip movements, to mitigate the reliance of acoustic models on individual speakers and the volume of training data. By merging auditory and visual data, the novel method improves the representation of speaker characteristics, thereby enabling speaker adaptation, a task challenging in a single-medium environment. Experiments regarding speaker variation are part of this approach, and it further investigates how speaker-dependent audiovisual fusion is. Experimental findings indicate that average CER for AVSR is 169% less than that of traditional models (when optimized), and 118% less than lip reading’s average CER. Significant improvement is apparent in the accuracy of recognizing phonemes, especially those found at the end of words. Recognition of initial sounds improves for affricates and fricatives, where the lip movements are evident, but decreases for stops, where the lip movements are not as obvious. AVSR’s ability to generalize to different speakers is demonstrably better than single-modality systems, potentially yielding a CER decrease of up to 172%. Consequently, AVSR’s application to AI-driven language preservation plays a critical role in protecting endangered languages.
Locating tiny flaws in metallic structures via array eddy current testing (ECT) probes is a persistent and complex problem, requiring probes with exceptionally high resolution and sensitivity. The spatial resolution of an ECT array probe is intrinsically bound by the size of the induction coils; there is a limit. Although using smaller coils could lead to better spatial resolution, the trade-off is a reduction in sensor sensitivity. For the purpose of obtaining finer spatial resolution without diminishing sensitivity, this research introduces a resolution-enhanced ECT array probe equipped with four coil rows on a flexible printed circuit board (FPCB). Consecutive coils within a row are spaced 2 mm apart, and each row is shifted horizontally by 0.5 mm compared to the previous one. Interpolated and aligned data from the four rows creates a line, boosting the probe’s image resolution to 0.5 mm. The probe’s operation is configured using differential settings, with two coils operating concurrently at each moment. By carefully controlling the current flow in both coils, either a matching or an opposite direction of flow can be attained, which results in distinct eddy current distributions and unique sets of two output images. A discrete wavelet transform-based image fusion method, along with a patch-image model, is used to effectively suppress noise and highlight the indications of defects. Experimental observations show that small defects, having dimensions of 1 mm in length, 0.1 mm in width, and 0.3 mm in depth, in a 304 stainless steel sample, are identifiable through the fused image, thus confirming the probe’s superb sensitivity in detecting extremely small flaws.
Brain-computer interfaces (BCIs), a common tool in control applications, are often utilized by people with significant physical impairments. Several researchers have worked on the design of operational wheelchairs controlled by brain signals. For controlling devices, a steady-state visually evoked potential (SSVEP) electroencephalogram (EEG)-based brain-computer interface (BCI) was designed and developed. This research incorporated a QR code visual stimulus pattern into a previously established system. The proposed visual stimulation pattern, incorporating four flickering frequencies, produced four commands. Subsequently, a relative power spectrum density (PSD) approach was applied in order to extract the SSVEP feature set and was benchmarked against an absolute PSD method. Our experiments were devised to validate the efficiency of the proposed system’s operation. Real-time processing using the proposed SSVEP method and algorithm demonstrated an average classification accuracy of approximately 92% based on the results. The proposed BCI control method, in a simulated wheelchair environment governed by independent-based control, demanded approximately five times more time than keyboard control for real-time responsiveness. SSVEP-based wheelchair control, facilitated by a QR code pattern, is proposed. Nevertheless, extended periods of sustained control contribute to visual strain. We aim to validate and improve the proposed wheelchair control mechanism for those with severe physical incapacities.
The ability of the lower body to generate force plays a key role in sports performance metrics. The Vmaxpro inertial measurement unit’s body placement and its impact on the reliability of test-retest and inter-device measurements during vertical jump assessments are the focal points of this investigation. Highly trained female athletes, numbering eleven, performed 220 countermovement jumps (CMJ). Two Vmaxpro units, strategically placed, one between the L4 and L5 vertebrae (hip method) and another on top of the tibial malleolus (ankle method), were used to gather data concurrently. Intra-session reliability for ankle measurements showed superior consistency compared to hip measurements, exhibiting higher ICC (0.96) and CCC (0.93) values, a smaller SEM (10 cm), and a lower CV (464%) than the hip measurements (ICC 0.91, CCC 0.92, SEM 34 cm, CV 513%). Subsequently, the ankle displayed superior sensitivity (SWC = 0.28) relative to the hip method (SWC = 0.40). The measurement’s error (SEM) exhibited a greater magnitude than the substantial change (SWC), highlighting the inability to identify substantial improvements. A moderate agreement existed between the assessment methodologies, as measured by rs = 0.84, ICC = 0.77, CCC = 0.25, and a standard error of measurement (SEM) of 1.47 cm. Methods exhibited measurable differences, quantified as -85cm, statistically significant (p < 0.005), and showing an effect size of 22. In closing, the device’s placement affects the accuracy of the CMJ measurement, particularly at the ankle joint, leading to an underestimation. Despite the acceptable level of consistency in the instrument, the reliability analysis produced results revealing a substantial amount of both random and systematic error. Therefore, the Vmaxpro instrument lacks the necessary accuracy for a dependable CMJ measurement.
The finite element (FE) modeling of bridges, in updating, depends on measured modal parameters; less frequently, it is based on measured structural responses under a known load.