A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

Electromagnetic data collected using electroencephalography (EEG) and magnetoencephalography (MEG) are of central importance for psychophysiological research. The scope of concepts, methods, and instruments used by EEG/MEG researchers has dramatically increased and is expected to further increase in the future. Building on existing guideline publications, the goal of the present paper is to contribute to the effective documentation and communication of such advances by providing updated guidelines for conducting and reporting EEG/MEG studies. The guidelines also include a checklist of key information recommended for inclusion in research reports on EEG/MEG measures.

https://csea.phhp.ufl.edu/Publications/Keil_CommitteeReport_2014.pdf

Committee report: Publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography

In this paper, we develop a vision-based system that employs a combined RGB and depth descriptor to classify hand gestures. The method is studied for a human-machine interface application in the car. Two interconnected modules are employed: one that detects a hand in the region of interaction and performs user classification, and another that performs gesture recognition. The feasibility of the system is demonstrated using a challenging RGBD hand gesture data set collected under settings of common illumination variation and occlusion.

https://cvrr.ucsd.edu/publications/2014/OhnBar_IEEETITS2014.pdf

Hand Gesture Recognition in Real-Time for Automotive Interfaces: A Multimodal Vision-based Approach and Evaluations

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review.

This works gives an account of evaluation of white blood cell differential counts via computer aided diagnosis (CAD) system and hematology rules. Leukocytes, also called white blood cells (WBCs) play main role of the immune system. Leukocyte is responsible for phagocytosis and immunity and therefore in defense against infection involving the fatal diseases incidence and mortality related issues. Admittedly, microscopic examination of blood samples is a time consuming, expensive and error-prone task. A manual diagnosis would search for specific Leukocytes and number abnormalities in the blood slides while complete blood count (CBC) examination is performed. Complications may arise from the large number of varying samples including different types of Leukocytes, related sub-types and concentration in blood, which makes the analysis prone to human error. This process can be automated by computerized techniques which are more reliable and economical. In essence, we seek to determine a fast, accurate mechanism for classification and gather information about distribution of white blood evidences which may help to diagnose the degree of any abnormalities during CBC test. In this work, we consider the problem of pre-processing and supervised classification of white blood cells into their four primary types including Neutrophils, Eosinophils, Lymphocytes, and Monocytes using a consecutive proposed deep learning framework. For first step, this research proposes three consecutive pre-processing calculations namely are color distortion; bounding box distortion (crop) and image flipping mirroring. In second phase, white blood cell recognition performed with hierarchy topological feature extraction using Inception and ResNet architectures. Finally, the results obtained from the preliminary analysis of cell classification with (11200) training samples and 1244 white blood cells evaluation data set are presented in confusion matrices and interpreted using accuracy rate, and false positive with the classification framework being validated with experiments conducted on poor quality blood images sized 320 × 240 pixels. The deferential outcomes in the challenging cell detection task, as shown in result section, indicate that there is a significant achievement in using Inception and ResNet architecture with proposed settings. Our framework detects on average 100% of the four main white blood cell types using ResNet V1 50 while also alternative promising result with 99.84% and 99.46% accuracy rate obtained with ResNet V1 152 and ResNet 101, respectively with 3000 epochs and fine-tuning all layers. Further statistical confusion matrix tests revealed that this work achieved 1, 0.9979, 0.9989 sensitivity values when area under the curve (AUC) scores above 1, 0.9992, 0.9833 on three proposed techniques. In addition, current work shows negligible and small false negative 0, 2, 1 and substantial false positive with 0, 0, 5 values in Leukocytes detection.

Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception

In this study, we investigated experimentally the influence of anisotropic compartments on source reconstruction using phantom measurements with an artificial current dipole as signal source. The anisotropic compartment was built from single skeins with a nine times higher conductivity than the surrounding solution. We placed the dipole at the lower borders of the anisotropic bulk and measured the magnetic field and the electric potential for each dipole location. The current dipole was rotated in relation to the anisotropic skeins and potentials and fields were recorded for different angles between anisotropic skeins and current dipole. We found that the estimated dipole orientations are strongly influenced by the anisotropy, while the reconstructed position of the dipole is not significantly influenced. We conclude that for bioelectric and biomagnetic source reconstruction, it is necessary to take into account anisotropic structures in the volume conductor if dipole orientations are of interest.

Effects of anisotropy in a physical torso phantom on source reconstructions from a current dipole

Due to increase of vehicle usage all around the world, the importance of safety driving in traffic is increasing. All ofÂ the countries around the world are taking actions to increase the safety driving habitats and decrease the number oftraffic accidents. One of the applied precautions is to put necessary automatic auditing mechanisms into service forÂ controlling the drivers as they drive since reckless drivers may not obey many traffic rules. In this study, image andvideo processing based methods are applied to identify the dangerously lane changing vehicles/drivers in the traffic.Â The proposed method focuses on to detect three different violations in traffic: the vehicles frequently changingtraffic lanes, the vehicles changing lanes when it is forbidden, and the vehicles overtaking the other vehicles usingÂ the right lanes instead of left one. The proposed method is based on the image and video processing techniques. Itfirst detects the vehicles in video sequences, then tracks the vehicles in the following frames and determines the laneÂ changes of the vehicles. In the vehicle detection phase an image subtraction method is used. In the vehicle trackingphase, Kalman filtering tracking algorithm is used. After determining the lane changes of the vehicles/drivers, a ruleÂ based decision system is used to find out the vehicles/drivers improperly changing lanes and those vehicles aremarked on the video. The proposed method is tested on the videos captured from real traffic environments andÂ promising results are obtained.

Determination and identification of dangerously lane changing vehicles in traffic by image processing techniques

In this study, a framework that generates natural language descriptions of images within a controlled environment is proposed. Previous work on neural networks mostly focused on choosing the right labels and/or increasing the number of related labels to depict an image. However, creating a textual description of an image is a completely different phenomenon, structurally, syntactically, and semantically. The proposed semantic image annotation framework presents a novel combination of deep learning models and aligned annotation results derived from the instances of the ontology classes to generate sentential descriptions of images. Our hybrid approach benefits from the unique combination of deep learning and semantic web technologies. We detect objects from unlabeled sports images using a deep learning model based on a residual network and a feature pyramid network, with the focal loss technique to obtain predictions with high probability. The proposed framework not only produces probabilistically labeled images, but also the contextual results obtained from a knowledge base exploiting the relationship between the objects. The framework’s object detection and prediction performances are tested with two datasets where the first one includes individual instances of images containing everyday scenes of common objects and the second custom dataset contains sports images collected from the web. Moreover, a sample image set is created to obtain annotation result data by applying all framework layers. Experimental results show that the framework is effective in this controlled environment and can be used with other applications via web services within the supported sports domain.

/pdf/a-hybrid-approach-for-semantic-image-annotation-2wadosgrrw.pdf

A Hybrid Approach for Semantic Image Annotation

the aim of this paper is to recognize the Arabic handwriting letters by using histogram of oriented gradients (HOG). We collected 2240 letters by 8 people, each person wrote 28 alphabet letter 10 times. First of all we resize All 2240 hand writing letter of Arabic Alphabet as images(pre-processing) after that extract these images by using one of feature extraction methods which is histogram of oriented gradients (HOG) .For classification, the K-Nearest Neighbor (KNN) is used. The results are shown by using 1120 images in the one case and 2240 images in the second case and evaluate these results with the confusion matrix. Other cases we used leave one out (LOO), 2-fold classification and leave one out cross validation. The best fully performance of HOG was with leave one out technique because of the ability of HOG algorithm to capture the shape of letter in the image according to its edges (gradients).

Applying the Histogram of Oriented Gradients to Recognize Arabic Letters

This paper presents an Internet of Things (IoT) framework which is specially designed for assisting the research and rescue operations targeted to collapsed buildings aftermath of an earthquake. In general, an IoT network is used to collect and process data from different sources called things. According to the collected data, an IoT system can actuate different mechanisms to react the environment. In the problem at hand, we exploit the IoT capabilities to collect the data about the victims before the building collapses and when it falls down the collected data is processed to generate useful reports which will direct the search and rescue efforts. The proposed framework is tested by a pilot implementation with some simplifications. The initial results and experiences are promising. During the pilot implementation, we observed some issues which are addressed in the proposed IoT framework properly.

Gokhan Sengul

Papers

Effects of anisotropy in a physical torso phantom on source reconstructions from a current dipole

Determination and identification of dangerously lane changing vehicles in traffic by image processing techniques

A Hybrid Approach for Semantic Image Annotation

Applying the Histogram of Oriented Gradients to Recognize Arabic Letters

An IoT application for locating victims aftermath of an earthquake