Intelligent Devices for IoT Applications

Internet of Things (IoT) devices refer to a vast network of physical devices that are connected to the internet and can communicate with each other through sensors and software. These devices range from simple household appliances, like smart thermostats and security cameras, to more complex industrial equipment, such as sensors used in manufacturing and logistics. Specially, IoT enabled wireless gas sensing systems which can withstand harsh environments without compromising the performance are getting popular day by day, which necessitates adequate developments in this field. By being the essential components of a wireless gas sensing system, both the sensor and the elements for communication should be agile and resilient when it comes to tackle unfavorable scenario. Moreover, gas sensors are prone to drift, which can lead to inaccurate readings and decreased reliability over time. Again, recent advancements in antenna design, such as fractal antennas and metamaterial structures, have shown promises in improving the bandwidth and gain parameters of the antennas built on top of high temperature tackling substrates. This piece of research targets three fundamental sections: demonstration of recent advances in data driven techniques for gas sensing system optimization, designing of antennas for different applications, and device design as well as fabrication. The Dimatix DMP-2831 inkjet printer has been optimized to operate with six different inks and two different substrates including PET and 3 mol yttria-stabilized zirconia (3YSZ) based ceramic substrate. Later, the feature oriented gas sensor data analysis to investigate correlations among stability, selectivity and long term drift is illustrated, which should significant relations among those parameters that can be considered while designing different intelligent data driven models to compensate drift. Moreover, a subspace transfer based approach is proposed to classify drifted gas sensor response to detect particular gas with higher accuracy. The model achieved an average accuracy greater than 87% while using only 40% of the total dataset to be trained. In the field of antenna technology, a co-planar waveguide (CPW) fed super wideband antenna is proposed which can cover C, X, Ku, K, Ka, Q, V, and W bands according to the simulated performance with high gain and radiation efficiency. Again, a high temperature tolerant antenna based on 3YSZ substrate is proposed which achieved good alignment between the simulated and fabricated device performance

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

Background: Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. Results: We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. Conclusion: We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications

tracker independent drift detection and correction using segmented objects and features

Object tracking has been an active research topic in the field of video processing. However, automated object tracking, under uncontrolled environments, is still difficult to achieve and encounters various challenges that cause the tracker to drift away from the target object. %Object tracking methods with fixed models, that are predefined prior to the tracking task, normally fail because of the inevitable appearance changes that can be either object or environment-related. To effectively handle object or environment tracking challenges, recent powerful tracking approaches are learning-based, meaning they learn object appearance changes while tracking online. The output of such trackers is, however, limited to a bounding box representation, the center of which is considered as the estimated object location. Such bounding box may not provide accurate foreground/background discrimination and may not handle highly non-rigid objects. Moreover, the bounding box may not surround the object completely, or it may not be centered around it, which affects the accuracy of the overall tracking process. Our main objective in this work is to reduce drifts of state-of-the-art tracking algorithms (trackers) using object segmentation so to produce more accurate bounding box. To enhance the quality of state-of-the-art trackers, this work investigates two main venues: first tracker-independent drift detection and correction using object features and second, selection of best performing parameters of Graph Cut object segmentation and of support vector machines using artificial immune system. In addition, this work proposes a framework for the evaluation and ranking of different trackers using easily interpretable performance measures, in a way to account for the presence of outliers. For tracker-independent drift detection, we use saliency features or objectness using saliency, the ratio of the salient region corresponding to the target object with respect to the estimated bounding box is used to indicate the occurrence of tracking drift with no prior information about the target model. With objectness measures, we use both relative area and score of the detected candidate boxes according to the objectness measure to indicate the occurrenece of the tracking drift. For drift correction, we investigate the application of object segmentation on the estimated bounding box to re-locate it around the target object. Due to its ability to lead to a global near optimal solution, we use the Graph Cut object segmentation method. We modify the Graph Cut model to incorporate an automatic seed selection module based on interest points, in addition to a template mask, to automatically initialize the segmentation across frames. However, the integration of segmentation in the tracking loop has its computational burden. In addition, the segmentation quality might be affected by tracking challenges, such as motion blur and occlusion. Accordingly, object segmentation is applied only when a drift is detected. Simulation results show that the proposed approach improves the tracking quality of five recent trackers. Researchers often use long and tedious trial and error approaches for determining the best performing parameter configuration of a video-processing algorithm, particularly with the diverse nature of video sequences. However, such configuration does not guarantee the best performance. A little research attention has been given to study the algorithm's sensitivity to its parameters. Artificial immune system is an emergent biologically motivated computing paradigm that has the ability to reach optimal or near-optimal solutions through mutation and cloning. This work proposes the use of artificial immune system for the selection of best performing parameters of two video processing algorithms: support vector machines for object tracking and Graph Cut based object segmentation. An increasing number of trackers are being developed and when introducing a new tracker, it is important to facilitate its evaluation and ranking in relation to others, using easy to interpret performance measures. Recent studies have shown that some measures are correlated and cannot reflect the different aspects of tracking performance when used individually. In addition, they do not incorporate robust statistics to account for the presence of outliers that might lead to insignificant results. This work proposes a framework for effective scoring and ranking of different trackers by using less correlated quality metrics, coupled with a robust estimator against dispersion. In addition, a unified performance index is proposed to facilitate the evaluation process

Maritime Object Detection, Tracking, and Classification Using Lidar and Vision-Based Sensor Fusion

Autonomous Surface Vehicles have the capability of replacing dull, dirty, and dangerous jobs in the maritime field. However, few successful ASV systems exist today, as there is a need for greater sensing capabilities. Furthermore, a successful ASV system requires object detection and recognition capabilities to enable autonomous navigation and situational awareness. This thesis demonstrates an application of LiDAR sensors in maritime environments for object detection, classification, and camera sensor fusion. This is accomplished through the integration of a high-fidelity GPS/INS system, 3D LiDAR sensors, and a pair of cameras. After rotating LiDAR returns into a global reference frame, they are reduced to a 3D occupancy grid. Objects are then extracted and classified with a Support Vector Machine (SVM) classifier. The LiDAR returns, when converted from a global frame to a camera frame, then allow the cameras to process a region of their imaging frame to assist in the classification of objects using color-based features. The SVM implementation results in an overall accuracy 98.7% for 6 classes. The transformation into pixel coordinates is shown here to be successful, with an angular error of 2 degrees, attributed to measurement error propagated through rotations

Comparison of the Ice Watch Database and Sea Ice Classification from Sentinel-1 Imagery

In this thesis,we investigate the potential use of in-situ sea ice observations from the Ice Watch database as ground truth data for an automated classification algorithm of sea ice types from Sentinel-1 SAR data. The Ice Watch database and the Sentinel-1 data archive are searched for in-situ observations and satellite data acquisitions in Extra Wide swath mode overlapping in both space and time. Time differences of up to a maximum of 12 hours are accepted and included in this investigation. The Sentinel-1 data is downloaded in Ground- Range Detected format at medium resolution and thermal noise correction, radiometric calibration and additional multilooking with a 3-by-3 window is applied. Different ice types in the images are then classified with the Gaussian IA classifier developed at UiT. The resulting image with ice type labels is geolocated and aligned with the in-situ observation from the Ice Watch database. A grid of 25-by-25 pixels around the location of the Ice Watch observation is extracted. For data points with a large time difference between in-situ observation and satellite data acquisition, a sea ice drift algorithm is applied to estimate and correct for possible influence of ice drift between the two acquisition times. Correlation and linear regression is investigated between a total number of 123 observation and the classified area around the observation. In addition, per class accuracy for the trained ice types in the classifier is investigated. A medium to strong positive correlation is found between types of ice and a weakly negative to no correlation was found for sea ice concentration. “Second-/Multiyear ice” separation achieves the highest score with 93.8 % per class accuracy. The second highest scoring class is “Deformed First-Year Ice”, for which 48.1 % per class accuracy is achieved. The thinner ice performs poorly due to the low number of representative of observations from these classes. Based on the findings there is a relationship between the reported observations from the Ice Watch database and the classified Sentinel-1 images. The ability to separate the older and deformed ice types from younger level ice is present

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data