InPhase: Phase-based Ranging and Localization

Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

MRI image segmentation using machine learning networks and level set approaches

<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>

dnabarcoder: an open-source software package for analyzing and predicting DNA sequence similarity cut-offs for fungal sequence identification

The accuracy and precision of fungal molecular identification and classification are challenging, particularly in environmental metabarcoding approaches as these often trade accuracy for efficiency given the large data volumes at hand. In most ecological studies, only a single similarity cut-off value is used for sequence identification. This is not sufficient since the most commonly used DNA markers are known to vary widely in terms of inter- and intra-specific variability. We address this problem by presenting a new tool, dnabarcoder, to analyze and predict different local similarity cut-offs for sequence identification for different clades of fungi. For each similarity cut-off in a clade, a confidence measure is computed to evaluate the resolving power of the genetic marker in that clade. Experimental results showed that when analyzing a recently released filamentous fungal ITS DNA barcode dataset of CBS strains from the Westerdijk Fungal Biodiversity Institute, the predicted local similarity cut-offs varied immensely between the clades of the dataset. In addition, most of them had a higher confidence measure than the global similarity cut-off predicted for the whole dataset. When classifying a large public fungal ITS dataset – the UNITE database - against the barcode dataset, the local similarity cut-offs assigned fewer sequences than the traditional cut-offs used in metabarcoding studies. However, the obtained accuracy and precision were significantly improved.

Portable X-ray fluorescence (pXRF) calibration for analysis of nutrient concentrations and trace element contaminants in fertilisers

With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97–0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0–100 mg kg-1 (R2 values were from 0.78–0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10–0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump.

An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

Accuracy and precision of non-invasive continuous haemoglobin concentration monitoring in diabetic patients

Background Accuracy and precision of non-invasive continuous haemoglobin concentration (SpHb) provided by Masimo device in diabetic patients is poorly studied. This retrospective analysis aimed to provide data on SpHb accuracy and precision in diabetic patients. Results The sample size population consisted of 14 patients, with 56 SpHb/Lab data pairs. Lab value showed a mean ± standard deviation (SD) of 13.2 ± 1.2 g/dL, whilst SpHb showed a mean ± SD of 11.8 ± 1.1 g/dL. Linear regression analysis between Lab/SpHb data pairs showed a r of 0.8960 (CI95% 0.8281-0.9379, p value < 0.0001). SpHb underestimated the real Hb values provided by Lab. Bland-Altman analysis showed that SpHb accuracy was −1.37 g/dL (CI95% −1.51 to −1.22 g/dL, p value < 0.0001), precision of 0.55 g/dL, lower LOA −2.45 g/dL (CI95% −2.71 to −2.20 g/dL) and upper LOA −0.28 g/dL (CI95% −0.53 to −0.02 g/dL). Conclusions For the first time, we provided data on SpHb accuracy and precision in the diabetic population. SpHb showed a high correlation coefficient when compared with Lab values, but the wide LOA limits its accuracy.

Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes

Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake in LTC is laborious and subjective, limiting clinical inference capabilities. Recent advances in automatic image-based food estimation have not yet been evaluated in LTC settings. Here, we describe a fully automatic imaging system for quantifying food intake. We propose a novel deep convolutional encoder-decoder food network with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate’s remaining food volume relative to reference portions in whole and modified texture foods. We trained and validated the network on the pre-labelled UNIMIB2016 food dataset and tested on our two novel LTC-inspired plate datasets (689 plate images, 36 unique foods). EDFN-D performed comparably to depth-refined graph cut on IOU (0.879 vs. 0.887), with intake errors well below typical 50% (mean percent intake error: $$-4.2$$ - 4.2 %). We identify how standard segmentation metrics are insufficient due to visual-volume discordance, and include volume disparity analysis to facilitate system trust. This system provides improved transparency, approximates human assessors with enhanced objectivity, accuracy, and precision while avoiding hefty semi-automatic method time requirements. This may help address short-comings currently limiting utility of automated early malnutrition detection in resource-constrained LTC and hospital settings.

METHOD VALIDATION OF SIMVASTATIN IN PCL-PEG-PCL TRIBLOCK COPOLYMER MICELLES USING UV-VIS SPECTROPHOTOMETRIC FOR SOLUBILITY ENHANCEMENT ASSAY

Objective: This study aims to increase the solubility of simvastatin (SIM), a hydrophobic drug, by incorporating it into PCL-PEG-PCL triblock copolymer micelles and validating the assay method used, namely Uv-Vis spectrophotometric. Methods: The shake flask method was used to determine the increase in solubility experienced by SIM after being incorporated into the micellar system. The values ​​of maximum wavelength (λmax), linearity, LOD, LOQ, accuracy, and precision were used as parameters measured to assess the validity of the assay method used. Results: The results showed that PCL-PEG-PCL triblock copolymer micelles could increase SIM solubility by 9.7 times (89.49±5.75 µg/ml) compared to SIM without modification (9.19±0.24 µg/ml). The validation results show the λmax value of 239 nm, a linear calibration curve with an R-value of 0.9994, LOD and LOQ of 0.33 µg/ml and 1.00 µg/ml, accurate measurement with recovery at concentrations of 80%, 100%, and 120% were 102.93±1.32%, 100.78±0.40%, and 104.58±0.79% and also had good precision ​​with RSD<2%. Conclusion: The PCL-PEG-PCL triblock copolymer micelles can increase SIM solubility and the Uv-Vis spectrophotometric method has been validated successfully for the quantitative analysis of SIM in PCL-PEG-PCL triblock copolymer micelles.

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Predictive maintenance (PdM) is indicated state of the machine to perform a schedule of maintenance based on historical data, integrity factors, statistical inference methods, and engineering approaches that are currently often applied to aircraft maintenance. The Predictive maintenance on aircraft to avoid the worse event (failure) and get information about the status of aircraft machines by applied on Machine Learning (ML) to get high accuracy and precision. The research aims to look for the method and technique of ML, which is the best applied on PdM for aircraft in accuracy indicators. The techniques of ML have been divided by classification and regression, which are compared on three ML methods: Random Forest (RF), Support Vector Machine (SVM), and simple LSTM. The result of the study for classification technique are LSTM 98,7%, SVM 95,6%, and RF 900,3%. On other hand, Regression technique for ML result on MAE and RMSE are LSTM 13,55 and 22,13, SVM 15,77 and 20,51, RF 15,06 and 19,98. Classify technique is better and faster than regression when calculating the PdM on an aircraft engine. The LSTM method of ML is the best applied to it because of the accuracy higher and time process faster than other methods in this study. Finally, the LSTM method is highly recommended while using with classify technique on ML to determine the PdM on an aircraft engine.