Indoor 3D Localization Scheme Based on BLE Signal Fingerprinting and 1D Convolutional Neural Network

Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location time series have been used to take advantage of spectral differences among land cover classes over time and to minimize the impact of missing observations. Deep convolutional neural networks (CNNs) have demonstrated potential for land cover classification of single date images. However, over large areas and using time series their application is complicated because they are sensitive to missing observations and they may misclassify small and spatially fragmented surface features due to their spatial patch-based implementation. This study demonstrates, for the first time, a one-dimensional (1D) CNN single pixel time series land classification approach that uses temporal percentile metrics and that does not have these issues. This is demonstrated for all the Conterminous United States (CONUS) considering two different 1D CNN structures with 5 and 8 layers, respectively. CONUS 30 m land cover classifications were derived using all the available Landsat-5 and -7 imagery over a seven-month growing season in 2011 with 3.3 million 30 m land cover class labelled samples extracted from the contemporaneous CONUS National Land Cover Database (NLCD) 16 class land cover product. The 1D CNNs and, a conventional random forest model, were trained using 10%, 50% and 90% samples, and the classification accuracies were evaluated with an independent 10% proportion. Temporal metrics were classified using 5, 7 and 9 percentiles for each of five Landsat reflective wavelength bands and their eight band ratios. The CONUS and detailed 150 × 150 km classification results demonstrate that the approach is effective at scale and locally. The 1D CNN classification land cover class boundaries were preserved for small axis dimension features, such as roads and rivers, with no stripes or anomalous spatial patterns. The 8-layer 1D CNN provided the highest overall classification accuracies and both the 5-layer and 8-layer 1D CNN architectures provided higher accuracies than the random forest by 1.9% - 2.8% which as all the accuracies were > 83% is a meaningful increase. The CONUS overall classification accuracies increased marginally with the number of percentiles (86.21%, 86.40%, and 86.43% for 5, 7 and 9 percentiles, respectively) using the 8-layer 1D-CNN. Class specific producer and user accuracies were quantified, with lower accuracies for the developed land, crop and pasture/hay classes, but no systematic pattern among classes with respect to the number of temporal percentiles used. Application of the trained model to a different year of CONUS Landsat ARD showed moderately decreased accuracy (80.79% for 7 percentiles) that we illustrate is likely due to different intra-annual surface variations between years. These encouraging results are discussed with recommended research for deep learning using temporal metric percentiles.

Recently, three dimensional (3D) convolutional neural networks (CNNs) have emerged as dominant methods to capture spatiotemporal representations in videos, by adding to pre-existing 2D CNNs a third, temporal dimension. Such 3D CNNs, however, are anti-causal (i.e., they exploit information from both the past and the future frames to produce feature representations, thus preventing their use in online settings), constrain the temporal reasoning horizon to the size of the temporal convolution kernel, and are not temporal resolution-preserving for video sequence-to-sequence modelling, as, for instance, in action detection. To address these serious limitations, here we present a new 3D CNN architecture for the causal/online processing of videos. Namely, we propose a novel Recurrent Convolutional Network (RCN), which relies on recurrence to capture the temporal context across frames at each network level. Our network decomposes 3D convolutions into (1) a 2D spatial convolution component, and (2) an additional hidden state 1 × 1 convolution, applied across time. The hidden state at any time t is assumed to depend on the hidden state at t - 1 and on the current output of the spatial convolution component. As a result, the proposed network: (i) produces causal outputs, (ii) provides flexible temporal reasoning, (iii) preserves temporal resolution. Our experiments on the largescale large Kinetics and MultiThumos datasets show that the proposed method performs comparably to anti-causal 3D CNNs, while being causal and using fewer parameters.

When using convolutional neural networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice [two-dimensional (2D)] or whole volumes [three-dimensional (3D)]. One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost. In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs, and to triplanar orthogonal 2D CNNs. The standard pseudo-3D method regards the neighboring slices as multiple input image channels. We additionally design and evaluate a novel, simple approach where the input stack is a volumetric input that is repeatably convolved in 3D to obtain a 2D feature map. This 2D map is in turn fed into a standard 2D network. We conducted experiments using two different CNN backbone architectures and on eight diverse data sets covering different anatomical regions, imaging modalities, and segmentation tasks. We found that while both pseudo-3D methods can process a large number of slices at once and still be computationally much more efficient than fully 3D CNNs, a significant improvement over a regular 2D CNN was only observed with two of the eight data sets. triplanar networks had the poorest performance of all the evaluated models. An analysis of the structural properties of the segmentation masks revealed no relations to the segmentation performance with respect to the number of input slices. A post hoc rank sum test which combined all metrics and data sets yielded that only our newly proposed pseudo-3D method with an input size of 13 slices outperformed almost all methods. In the general case, multislice inputs appear not to improve segmentation results over using 2D or 3D CNNs. For the particular case of 13 input slices, the proposed novel pseudo-3D method does appear to have a slight advantage across all data sets compared to all other methods evaluated in this work.

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For 4D time-series tomograms, this is usually handled by segmenting the constituent tomograms independently through time with 3D convolutional neural networks. Inter-volume information is therefore not utilized, potentially leading to temporal incoherence. In this paper, we attempt to resolve this by proposing two hidden Markov model variants that refine 4D segmentation labels made by 3D convolutional neural networks working on each time point. Our models utilize not only inter-volume information, but also the prediction confidence generated by the 3D segmentation convolutional neural networks themselves. To the best of our knowledge, this is the first attempt to refine 4D segmentations made by 3D convolutional neural networks using hidden Markov models. During experiments we test our models, qualitatively, quantitatively and behaviourally, using prespecified segmentations. We demonstrate in the domain of time series tomograms which are typically undersampled to allow more frequent capture; a particularly challenging problem. Finally, our dataset and code is publicly available.

Deep learning models have gained prominence in AI research due to their exceptional feature extraction capabilities. In fingerprint-based indoor localization, 1D and 2D convolutional neural networks (CNNs) are frequently utilized for their superior performance and minimal hardware requirements. However, these models primarily extract spatial features and often exhibit degraded real-time performance due to signal inconsistencies caused by shadowing, fading, and multipath effects. To address this, incorporating temporal variations through 3D CNNs can enhance outcomes, albeit with increased computational costs. This study proposes a knowledge distillation (KD) approach using 3D CNNs for indoor localization, to bridge the computational and performance gaps. The proposed KD framework employs a complex teacher model (TM) to train two simpler student models A (SM-A) and B (SM-B). We employ both response-based and feature-based KD methods to optimize the student models’ weights. The proposed techniques are evaluated using the UJILIB dataset collected from two floors of the University of Jaume I library. To enhance model performance, data augmentation is applied, yielding substantial improvements in test accuracy of 10.56% for TM, 16.19% for SM-A, and 16.93% for SM-B. The application of KD results in accuracy improvements of 5.45% for SM-A and 3.35% for SM-B, consequently reducing their average positioning errors (APEs) from 1.070 m and 1.030 m to 0.840 m and 0.944 m, respectively. Compared to KD-based SM-A, TM shows only 3.30% higher positioning accuracy, but with a 65.14% increase in computational complexity. Notably, the KD-enhanced SM-A also achieves a significantly lower APE of 0.84 m compared to the 2D CNN of 1.72 m.

The past several years have seen both an explosion in the use of Convolutional Neural Networks (CNNs) and accelerators to make CNN inference practical. In the architecture community, the lion share of effort has targeted CNN inference for image recognition. The closely related problem of video recognition has received far less attention as an accelerator target. This is surprising, as video recognition is more computationally intensive than image recognition, and video traffic is predicted to be the majority of internet traffic in the coming years. This paper fills the gap between algorithmic and hardware advances for video recognition by providing a design space exploration and flexible architecture for accelerating 3D Convolutional Neural Networks (3D CNNs)—the core kernel in modern video understanding. When compared to (2D) CNNs used for image recognition, efficiently accelerating 3D CNNs poses a significant engineering challenge due to their large (and variable over time) memory footprint and higher dimensionality. To address these challenges, we design a novel accelerator called Morph, that can adaptively support different spatial and temporal tiling strategies depending on the needs of each layer of each target 3D CNN. We codesign a software infrastructure alongside the Morph hardware to find good-fit parameters to control the hardware. Evaluated on state-of-the-art 3D CNNs, Morph achieves up to 2.7× (1.9× average) reduction in energy consumption and improves performance/watt up to 4.4× (3× average) compared to a baseline 3D CNN accelerator, with an area overhead of 2%. Morph further achieves a 11.6× average energy reduction on 3D CNNs when compared to Eyeriss, a popular 2D CNN accelerator, while reducing efficiency compared to Eyeriss on a 2D CNN by 71%.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users' devices and improves the shared CRNP model by aggregating users' updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users' privacy leakage.

LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization

The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first proposed to solve image classification problems at first. With advancements in science and technology, as well as the growth of the internet, tasks involving video analysis have received increased attention in recent years. As a result, it was suggested that a 3-dimensions convolutional neural network (3D CNN) be applied to tackle 3D image processing or video analysis. 3D CNN is primarily used in the field of human activity recognition, video classification, medical imaging detection, and so on. This paper offers a succinct introduction to the general architecture and representative models of 3D CNN, contrasts the variations between 2D CNN and 3D CNN, explores the widely used models derived from 3D CNN, and displays the application outcomes of 3D CNN.

Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. Database Correlation Method (DCM) is a positioning technology that based on a database of a premeasured location dependent variable such as Received Signal Strength (RSS). DCM has shown superior in terms of accuracy. Absolute RSS values received from a base station change with time, but the relative RSS (RRSS) values which refer to the relations of the RSS values between different base stations are more stable. This study proposes and implements a robust RRSS GSM-based technique for both positioning and traffic estimation. The study was tested and analyzed in Egypt roads using realistic data and Android smart phones. The performance evaluation showed good results. Mean positioning accuracy was about 29m in urban areas and velocity estimation was about 1 km/h in rural areas.

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

BackgroundAccurate segmentation of pelvic bones is an initial step to achieve accurate detection and localisation of pelvic bone metastases. This study presents a deep learning-based approach for automated segmentation of normal pelvic bony structures in multiparametric magnetic resonance imaging (mpMRI) using a 3D convolutional neural network (CNN).MethodsThis retrospective study included 264 pelvic mpMRI data obtained between 2018 and 2019. The manual annotations of pelvic bony structures (which included lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis) on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images were used to create reference standards. A 3D U-Net CNN was employed for automatic pelvic bone segmentation. Additionally, 60 mpMRI data from 2020 were included and used to evaluate the model externally.ResultsThe CNN achieved a high Dice similarity coefficient (DSC) average in both testing (0.80 [DWI images] and 0.85 [ADC images]) and external (0.79 [DWI images] and 0.84 [ADC images]) validation sets. Pelvic bone volumes measured with manual and CNN-predicted segmentations were highly correlated (R2 value of 0.84–0.97) and in close agreement (mean bias of 2.6–4.5 cm3). A SCORE system was designed to qualitatively evaluate the model for which both testing and external validation sets achieved high scores in terms of both qualitative evaluation and concordance between two readers (ICC = 0.904; 95% confidence interval: 0.871–0.929).ConclusionsA deep learning-based method can achieve automated pelvic bone segmentation on DWI and ADC images with suitable quantitative and qualitative performance.

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems