Enhanced Mechanisms for Navigation and Tracking Services in Smart Phones

Combining Global Positioning System (GPS) and Short Message Service (SMS), this paper develops a realisticsystem, called Mobile Navigation and Tracking System (MNTS), to provide navigation and target tracking services.MNTS is an Android based mobile application which integrated many enhanced mechanisms for navigation andtarget tracking services. MNTS not only provides users with the GPS navigation capability, but also supports QuickResponse (QR) code decoding, nearby scenic spot searching, friend positioning and target tracking. In targettracking, MNTS utilizing SMS mainly adopts two proposed novel approaches: location prediction and dynamicthreshold to reduce the number of short message transmissions while maintaining location accuracy within anacceptable range. Location prediction utilizes the current target’s location, moving speed, bearing to predict its nextlocation. When the distance between the predicted location and the actual location exceeds a threshold, the targetsends a short message to the tracker to update the actual location. Based on the movement speed of the target,the threshold is dynamically adjusted to balance the location accuracy and the number of short messages.Furthermore, as MNTS is free and open-source software, service providers or developers can easily extend theirown services based on this system

Anti-Theft Vehicle Tracking System Using GPS and Location Prediction

Currently the number of private vehicles is increasing day by day and hence the importance of tracking and theft prevention.  Recently the vehicle tracking systems are getting wide popularity and can be used in tracking in case of stolen vehicles. Real-time applications like Vehicle Tracking System is developed using Arduino board with a microcontroller. We have developed a vehicle tracking system with a Smartphone which is less expensive and reliable when compared to the existing system as there is no need for extra hardware. The objective is to develop an application for tracking vehicles, which will help the cab owners to track their car all the time and to predict the location of the vehicle in the case of a failing GPS (Global Positioning System). Time series prediction algorithm is used to predict the location of the vehicle if GPS is in off mode. The vehicle tracking system installed will update the GPS coordinates of the vehicle continued to the cloud, and this data can be used for predicting the location of the vehicle in case of emergency. This system can also be used to generate the bills after finishing the freight in the form of an SMS based on the distance traveled, which can be calculated from the latitude and longitude data. The GPS data can be mapped to the Google maps to track the location in real time. Compared with the existing system, this system is having the advantage of location prediction from the historic location data, and the cost is reduced by almost half. 

Persation: an IoT Based Personal Safety Prediction Model Aided Solution

The number of attacks on innocent victims in moving vehicles, and abduction of individuals in their vehicles has risen alarmingly in the past few years. One common scenario evident from the modus operandi of this kind of attack is the random motion of these vehicles, due to the driver's unpredictable behaviours. To save the victims in such kinds of assault, it is essential to offer help promptly. An effective strategy to save victims is to predict the future location of the vehicles so that the rescue mission can be actioned at the earliest possibility. We have done a comprehensive survey of the state-of-the-art personal safety solutions and location prediction technologies and proposes an Internet of Things (IoT) based personal safety model, encompassing a prediction framework to anticipate the future vehicle locations by exploiting complex analytics of current and past data variables including the speed, direction and geolocation of the vehicles. Experiments conducted based on real-world datasets demonstrate the feasibility of our proposed framework in accurately predicting future vehicle locations. In this paper, we have a risk assessment of our safety solution model based on the OCTAVE ALLEGRO model and the implementation of our prediction model

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

A hybrid 2D/4D-MRI methodology using simultaneous multi-slice imaging for radiotherapy guidance

Purpose: Respiratory motion management is important in abdominothoracic radiotherapy. Fast imaging of the tumor can facilitate multileaf collimator (MLC) tracking that allows for smaller treatment margins, while repeatedly imaging the full field-of-view is necessary for 4D dose accumulation. This study introduces a hybrid 2D/4D-MRI methodology that can be used for simultaneous MLC tracking and dose accumulation on a 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden). Methods: We developed a hybrid 2D/4D-MRI methodology that uses a simultaneous multislice (SMS) accelerated MRI sequence, which acquires two coronal slices simultaneously and repeatedly cycles through slice positions over the image volume. As a result, the fast 2D imaging can be used prospectively for MLC tracking and the SMS slices can be sorted retrospectively into respiratory-correlated 4D-MRIs for dose accumulation. Data were acquired in five healthy volunteers with an SMS-bTFE and SMS-TSE MRI sequence. For each sequence, a prebeam dataset and a beam-on dataset were acquired simulating the two phases of MR-linac treatments. Prebeam data were used to generate a 4D-based motion model and a reference mid-position volume, while beam-on data were used for real-time motion extraction and reconstruction of beam-on 4D-MRIs. In addition, an in-silico computational phantom was used for validation of the hybrid 2D/4D-MRI methodology. MLC tracking experiments were performed with the developed methodology, for which real-time SMS data reconstruction was enabled on the scanner. A 15-beam 8× 7.5 Gy intensity-modulated radiotherapy plan for lung stereotactic body radiotherapy with isotropic 3 mm GTV-to-PTV margins was created. Dosimetry experiments were performed using a 4D motion phantom. The latency between target motion and updating the radiation beam was determined and compensated. Local gamma analyses were performed to quantify dose differences compared to a static reference delivery, and dose area histograms (DAHs) were used to quantify the GTV and PTV coverage. Results: In-vivo data acquisition and MLC tracking experiments were successfully performed with the developed hybrid 2D/4D-MRI methodology. Real-time liver–lung interface motion estimation had a Pearson's correlation of 0.996 (in-vivo) and 0.998 (in-silico). A median (5th–95th percentile) error of 0.0 (−0.9 to 0.7) mm and 0.0 (−0.2 to 0.2) mm was found for real-time motion estimation for in-vivo and in-silico, respectively. Target motion prediction beyond the liver–lung interface had a median root mean square error of 1.6 mm (in-vivo) and 0.5 mm (in-silico). Beam-on 4D MRI reconstruction required a median amount of data equal to an acquisition time of 2:21–3:17 min, which was 20% less data compared to the prebeam-derived 4D-MRI. System latency was reduced from 501 ± 12 ms to −1 ± 3 ms (SMS-TSE) and from 398 ± 10 ms to −10 ± 4 ms (SMS-bTFE) by a linear regression prediction filter. The local gamma analysis agreed within (Formula presented.) to 3.3% (SMS-bTFE) and (Formula presented.) to 10% (SMS-TSE) with a reference MRI sequence. The DAHs revealed a relative D 98% GTV coverage between 97% and 100% (SMS-bTFE) and 100% and 101% (SMS-TSE) compared to the static reference. Conclusions: The presented 2D/4D-MRI methodology demonstrated the potential for accurately extracting real-time motion for MLC tracking in abdominothoracic radiotherapy, while simultaneously reconstructing contiguous respiratory-correlated 4D-MRIs for dose accumulation

A Systematic Literature Review on Machine Learning in Shared Mobility

Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

PADL: A Modeling and Deployment Language for Advanced Analytical Services

In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments.This work was partially supported by the SPRI–Basque Government through their ELKARTEK program (3KIA project, ref. KK-2020/00049). Aitor Almeida’s participation was supported by the FuturAAL-Ego project (RTI2018-101045-A-C22) granted by the Spanish Ministry of Science, Innovation and Universities. Javier Del Ser also acknowledges funding support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government

Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform

Floods, earthquakes, storm surges and other natural disasters severely affect the communication infrastructure and thus compromise the effectiveness of communications dependent rescue and warning services. In this paper, a user centric approach is proposed to establish communications in disaster affected and communication outage areas. The proposed scheme forms ad hoc clusters to facilitate emergency communications and connect end-users/ User Equipment (UE) to the core network. A novel cluster formation with single and multi-hop communication framework is proposed. The overall throughput in the formed clusters is maximized using convex optimization. In addition, an intelligent system is designed to label different clusters and their localities into affected and non-affected areas. As a proof of concept, the labeling is achieved on flooding dataset where region specific social media information is used in proposed machine learning techniques to classify the disaster-prone areas as flooded or unflooded. The suitable results of the proposed machine learning schemes suggest its use along with proposed clustering techniques to revive communications in disaster affected areas and to classify the impact of disaster for different locations in disaster-prone areas