Product recognition in store shelves as a sub-graph isomorphism problem

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.Comment: Slightly extended version of the paper accepted at ICIAP 2017. More information @project_page --> http://vision.disi.unibo.it/index.php?option=com_content&view=article&id=111&catid=7

A Newcomer\u27s Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

The smart health paradigms employ Internet-connected wearables for telemonitoring, diagnosis for providing inexpensive healthcare solutions. Fog computing reduces latency and increases throughput by processing data near the body sensor network. In this paper, we proposed a secure service-orientated edge computing architecture that is validated on recently released public dataset. Results and discussions support the applicability of proposed architecture for smart health applications. We proposed SoA-Fog i.e. a three-tier secure framework for efficient management of health data using fog devices. It discuss the security aspects in client layer, fog layer and the cloud layer. We design the prototype by using win-win spiral model with use case and sequence diagram. Overlay analysis was performed using proposed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014. The mobile clients were taken as test case. We performed comparative analysis between proposed secure fog framework and state-of-the art cloud-based framework

TCloud: Cloud SDI model for tourism information infrastructure management

This chapter proposes and develops a cloud-computing-based SDI model named as TCloud for sharing, analysis, and processing of spatial data particularly in the Temple City of India, Bhubaneswar. The main purpose of TCloud is to integrate all the spatial information such as tourism sites which include various temples, mosques, churches, monuments, lakes, mountains, rivers, forests, etc. TCloud can help the decision maker or planner or common users to get enough information for their further research and studies. It has used open source GIS quantum GIS for the development of spatial database whereas QGIS plugin has been linked with quantum GIS for invoking cloud computing environment. It has also discussed the various spatial overlay analysis in TCloud environment

Multimodal 2D Brain Computer Interface

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features

Towards a Single Trial fNIRS-based Brain-Computer Interface for Communication

Communication based on brain-computer interface (BCI) systems is still a challenge. Although most popular classes of BCIs heavily rely on electroencephalography (EEG), recent studies have demonstrated the feasibility of using functional near-infrared spectroscopy (fNIRS) as a reliable control signal in BCI systems. However, due to the inherent latency in hemodynamic responses, these systems are considerably slow. To address this issue, this study proposes an innovative oddball-based visio-mental task and investigates the feasibility of developing an fNIRS speller. The proposed paradigm derives its principles from the conventional oddball paradigm, which has been modified to include a set of mental arithmetic operations in the flash condition. Using statistical parametric mapping (SPM) and Pearson correlation analysis, the optimum channels and hemodynamic features were selected respectively. Linear discriminant analysis (LDA) was used to evaluate the performance of the proposed fNIRS-speller. Using 2 optimum channels, our analysis demonstrated the highest average accuracy of 78.5 ± 5.7% within 2-4 seconds of the stimulation and an average accuracy of 77.0 ± 8.9% only within the first 2 seconds. Achieving satisfactory performance while using only 2 channels and a 2-second window highlights the feasibility of developing a convenient and real-time fNIRS-speller. Such a system may have potential translational applications, particularly in users with a lack of eye gaze control

Disruptions of cortico-kinematic interactions in Parkinson\u27s disease

The cortical role of the motor symptoms reflected by kinematic characteristics in Parkinson\u27s disease (PD) is poorly understood. In this study, we aim to explore how PD affects cortico-kinematic interactions. Electroencephalographic (EEG) and kinematic data were recorded from seven healthy participants and eight participants diagnosed with PD during a set of self-paced finger tapping tasks. Event-related desynchronization (ERD) was compared between groups in the α (8−14 Hz), low-ß (14−20 Hz), and high-ß (20−35 Hz) frequency bands to investigate between-group differences in the cortical activities associated with movement. Average kinematic peak amplitudes and latencies were extracted alongside Sample Entropy (SaEn), a measure of signal complexity, as variables for comparison between groups. These variables were further correlated with average EEG power in each frequency band to establish within-group interactions between cortical motor functions and kinematic motor output. High ß-band power correlated with mean kinematic peak latency and signal complexity in the healthy group, while no correlation was found in the PD group. Also, the healthy group demonstrated stronger ERD in the broad ß-band than the PD participants. Our results suggest that cortical ß-band power in healthy populations is graded to finger tapping latency and complexity of movement, but this relationship is impaired in PD. These insights could help further enhance our understanding of the role of cortical ß-band oscillations in healthy movement and the possible disruption of that relationship in PD. These outcomes can provide further directions for treatment and therapeutic applications and potentially establish cortical biomarkers of Parkinson\u27s disease

A Comparative Characterization of Smart Textile Pressure Sensors

This research study investigates the impact of various insulating textile materials on the performance of smart textile pressure sensors made of conductive threads and piezo resistive material. We designed four sets of identical textile-based pressure sensors each of them integrating a different insulating textile substrate material. Each of these sensors underwent a series of tests that linearly increased and decreased a uniform pressure perpendicular to the surface of the sensors. The controlled change of the integration layer altered the characteristics of the pressure sensors including both the sensitivity and pressure ranges. Our experiments highlighted that the manufacturing design technique of textile material has a significant impact on the sensor; with evidence of reproducibility values directly relating to fabric dimensional stability and elasticity

Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges

The cloud and fog computing paradigms are developing area for storing, processing, and analysis of geospatial big data. Latest trend is mist computing which boost fog and cloud concepts for computing process where edge devices are used to help increase throughput and reduce latency to support at client edge. The present research article discussed the mist computing emergence for geospatial analysis of data from various geospatial applications. It also created a framework based on mist computing, i.e., MistGIS for analytics in mining domain from geospatial big data. The developed MistGIS platform is used in Tourism Information Infrastructure Management and Faculty Information Retrial System. Tourism Information Infrastructure Management is to assimilate entire geospatial data in context to travel/tourism places constitute of various lakes, mountains, rivers, forests, temples, mosques, churches, monuments, etc. It can aid all the stakeholders or users to acquire sufficient data in subsequent research studies. In this study, it has taken the Temple City of India, Bhubaneswar as the case study. Whereas Faculty Information Retrial System facilitated many functionalities with respect to finding the detail information of faculties according to their research area, contact details, and email ids, etc in all 31 National Institutes of Technology (NITs) in India. The framework is built with the Raspberry Pi microprocessor. The MistGIS platform has been confirmed by prelude analysis which includes cluster and overlay. The outcome show that mist computing assist cloud and fog computing to provide the analysis of geospatial big data