Aagaardadams7740

7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (-0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.A fully automatic, non-contact method for the assessment of the respiratory function is proposed using an RGB-D camera-based technology. The proposed algorithm relies on the depth channel of the camera to estimate the movements of the body's trunk during breathing. It solves in fixed-time complexity, O(1), as the acquisition relies on the mean depth value of the target regions only using the color channels to automatically locate them. This simplicity allows the extraction of real-time values of the respiration, as well as the synchronous assessment on multiple body parts. Two different experiments have been performed a first one conducted on 10 users in a single region and with a fixed breathing frequency, and a second one conducted on 20 users considering a simultaneous acquisition in two regions. The breath rate has then been computed and compared with a reference measurement. The results show a non-statistically significant bias of 0.11 breaths/min and 96% limits of agreement of -2.21/2.34 breaths/min regarding the breath-by-breath assessment. The overall real-time assessment shows a RMSE of 0.21 breaths/min. We have shown that this method is suitable for applications where respiration needs to be monitored in non-ambulatory and static environments.We propose to use ambient sound as a privacy-aware source of information for COVID-19-related social distance monitoring and contact tracing. The aim is to complement currently dominant Bluetooth Low Energy Received Signal Strength Indicator (BLE RSSI) approaches. These often struggle with the complexity of Radio Frequency (RF) signal attenuation, which is strongly influenced by specific surrounding characteristics. This in turn renders the relationship between signal strength and the distance between transmitter and receiver highly non-deterministic. We analyze spatio-temporal variations in what we call "ambient sound fingerprints". We leverage the fact that ambient sound received by a mobile device is a superposition of sounds from sources at many different locations in the environment. Such a superposition is determined by the relative position of those sources with respect to the receiver. We present a method for using the above general idea to classify proximity between pairs of users based on Kullback-Leibler distance between sound intensity histograms. The method is based on intensity analysis only, and does not require the collection of any privacy sensitive signals. Further, we show how this information can be fused with BLE RSSI features using adaptive weighted voting. We also take into account that sound is not available in all windows. Our approach is evaluated in elaborate experiments in real-world settings. The results show that both Bluetooth and sound can be used to differentiate users within and out of critical distance (1.5 m) with high accuracies of 77% and 80% respectively. Their fusion, however, improves this to 86%, making evident the merit of augmenting BLE RSSI with sound. We conclude by discussing strengths and limitations of our approach and highlighting directions for future work.Soil compaction management relies on costly annual deep tillage. Variable-depth tillage or site-specific tillage modifies the physical properties of the soil at the required zones for the growth of crops. In this study, a depth control system was designed for the subsoiler of the tillage at various depths. For this purpose, an algorithm was written to investigate the subsoiler location and soil compaction. A program was also developed to implement this algorithm using Kinco Builder Software to control the subsoiler depth, which was evaluated on the experimental platform. mTOR inhibitor In this study, four compression sensors were used at a distance of 10 cm up to a depth of 40 cm on the blade mounted at the front of the tractor. The data of these sensors were used as the input and compared with the pressure baseline limit (2.07 MPa), and with the priority to select the greater depth, the depth of subsoiler was determined. At all three modes of sensor activation (single, collective, and combined), this system was able to operate the hydraulic system of the tractor and place the subsoiler at the desired depth through the use of the position sensors.Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification "smarter". In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the "siphonic effect" produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.The aim of this study was to characterise all the goal scoring patterns during open play (elaborate attacks versus counterattacks) related to zone pitch division and the number of players involved in the 2018 FIFA World Cup in Russia. An Iterative Dichotomiser 3 (ID3) decision tree algorithm was used to classify all the goal scoring patterns (94 goals in 64 matches). The results did not show statistical differences between the type of scoring goal during the 2018 FIFA World Cup (p > 0.05; ES = Moderate). According to the result of the patterns of how the goals were achieved, an ID3 algorithm decision tree with seven classification decision nodes was calculated. Consequently, this study may aid national team coaches for the next World Cup to establish notational analyses and spatial-temporal relations to understand how scoring patterns during open play are related to zone pitch division and the number of players involved.We report simulations on the spectral behavior of non-layered gold-silicon and all-silicon frequency-selective metasurfaces in an asymmetric element configuration in the mid-infrared spectral window of 5-5.8 μm. The non-layered layout is experimentally feasible due to recent technological advances such as nano-imprint and nano-stencil lithography, and the spectral window was chosen due to the multitude of applications in sensing and imaging. The architecture exhibits significant resonance in the window of interest as well as extended tunability by means of variation of cell element sizes and relative coordinates. The results indicate that the proposed metasurface architecture is a viable candidate for mid-infrared absorbers, sensors and imaging systems.An indole-based fluorescent chemosensor IH-Sal was synthesized to detect Zn2+. IH-Sal displayed a marked fluorescence increment with Zn2+. The detection limit (0.41 μM) of IH-Sal for Zn2+ was greatly below that suggested by the World Health Organization. IH-Sal can quantify Zn2+ in real water samples. More significantly, IH-Sal could determine and depict the presence of Zn2+ in zebrafish. The detecting mechanism of IH-Sal toward Zn2+ was illustrated by fluorescence and UV-visible spectroscopy, DFT calculations, 1H NMR titration and ESI mass.A common technique for compressing a neural network is to compute the k-rank ℓ2 approximation Ak of the matrix A∈Rn×d via SVD that corresponds to a fully connected layer (or embedding layer). Here, d is the number of input neurons in the layer, n is the number in the next one, and Ak is stored in O((n+d)k) memory instead of O(nd). Then, a fine-tuning step is used to improve this initial compression. However, end users may not have the required computation resources, time, or budget to run this fine-tuning stage. Furthermore, the original training set may not be available. In this paper, we provide an algorithm for compressing neural networks using a similar initial compression time (to common techniques) but without the fine-tuning step. The main idea is replacing the k-rank ℓ2 approximation with ℓp, for p∈[1,2], which is known to be less sensitive to outliers but much harder to compute. Our main technical result is a practical and provable approximation algorithm to compute it for any p≥1, based on modern techniques in computational geometry. Extensive experimental results on the GLUE benchmark for compressing the networks BERT, DistilBERT, XLNet, and RoBERTa confirm this theoretical advantage.The present study compared the effect between walking exercise and a newly developed sensor-based gait retraining on the peaks of knee adduction moment (KAM), knee adduction angular impulse (KAAI), knee flexion moment (KFM) and symptoms and functions in patients with early medial knee osteoarthritis (OA). Eligible participants (n = 71) with early medial knee OA (Kellgren-Lawrence grade I or II) were randomized to either walking exercise or gait retraining group. Knee loading-related parameters including KAM, KAAI and KFM were measured before and after 6-week gait retraining. We also examined clinical outcomes including visual analog pain scale (VASP) and Knee Injury and Osteoarthritis Outcome Score (KOOS) at each time point. After gait retraining, KAM1 and VASP were significantly reduced (both Ps less then 0.001) and KOOS significantly improved (p = 0.004) in the gait retraining group, while these parameters remained similar in the walking exercise group (Ps ≥ 0.448). However, KAM2, KAAI and KFM did not change in both groups across time (Ps ≥ 0.