Appl. Syst. Innov., Volume 2, Issue 1 (March 2019) – 11 articles

This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set. Full article

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios. Full article

The main purpose of this paper is to construct an autopilot system for unmanned railcars based on computer vision technology in a fixed luminous environment. Four graphic predefined signs of different colors and shapes serve as motion commands of acceleration, deceleration, reverse and stop for the motion control system of railcars based on image recognition. The predefined signs’ strong classifiers were trained based on Haar-like feature training and AdaBoosting from Open Source Computer Vision Library (OpenCV). Comprehensive system integrations such as hardware, device drives, protocols, an application program in Python and man machine interface have been properly done. The objectives of this research include: (1) Verifying the feasibility of graphic predefined signs serving as commands of a motion control system of railcars with computer vision through experiments; (2) Providing reliable solutions for motion control of unmanned railcars, based on image recognition at affordable cost. The experiment results successfully verify the proposed methodology and integrated system. In the main program, every predefined sign must be detected at least three times in consecutive images within 0.2 s before the system confirms the detection. This digital filter like feature can filter out false detections and make the correct rate of detections close to 100%. After detecting a predefined sign, it was observed that the system could generate new motion commands to drive the railcars within 0.3 s. Therefore, both real time performance and the precision of the system are good. Since the sensing and control devices of the proposed system consist of computer, camera and predefined signs only, both the implementation and maintenance costs are very low. In addition, the proposed system is immune to electromagnetic interference, so it is ideal to merge into popular radio Communication Based Train Control (CBTC) systems in railways to improve the safety of operations. Full article

The possibility to test a radio frequency transceiver through the use of appropriate channel emulators allows evaluating their performance under various operating conditions. Many systems are able to operate with a relatively limited instantaneous bandwidth by applying known statistical models. Sometimes, it is necessary to evaluate the performance of a RF transceiver installed on high or very high speed platforms over predictable trajectories using optionally DEM (Digital Elevation Model) data of the terrain to estimate the number of stimulated paths and their contributions during the flight. When the instantaneous bandwidth of the signal becomes high (over several hundreds of MHz), one of the most important phenomena to consider is the Doppler spread induced by the channel and traditional narrowband models become useless. This paper presents some results when a time expansion is adopted to emulate the transceiver dynamic and the consequent Doppler spread with the aim of controlling the spectral purity of the emulated propagation channel. Full article

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%. Full article

The shift to Cloud computing has brought with it its specific security challenges concerning the loss of control, trust and multi-tenancy especially in Infrastructure-as-a-Service (IaaS) Cloud model. This article focuses on the design and development of an intrusion detection system (IDS) that can handle security challenges in IaaS Cloud model using an open source IDS. We have implemented a proof-of-concept prototype on the most deployed hypervisor—VMware ESXi—and performed various real-world cyber-attacks, such as port scanning and denial of service (DoS) attacks to validate the practicality and effectiveness of our proposed IDS architecture. Based on our experimental results we found that our Security Onion-based IDS can provide the required protection in a reasonable and effective manner. Full article

Human detection and tracking is an important task in artificial intelligent robotic systems, which usually require a robust target detector to work in a variety of circumstances. In complex environments where a camera is installed on a wheeled mobile robot, this task becomes much more difficult. In this paper, we present a real-time remote-control system for human detection, tracking, security, and verification in such challenging environments. Such a system is useful for security monitoring, data collection, and experimental purpose. In the proposed system, a Kinect RGB-D camera from Microsoft is used as a visual sensing device for the design of human detection and tracking. We also implemented a remote-control system on a four-wheel mobile platform with a Robot Operating System (ROS). By combining these two designs, a wireless controlled mobile platform having the feature of real-time human monitoring can be realized to handle this task efficiently. Experimental results validate the performance of the proposed system for wirelessly controlling the mobile robot to track humans in a real-world environment. Full article

There has been a rapid increase in the number of Internet of Things (IoT) devices in the last few years, providing a wide range of services such as camera feeds, light controls, and door locks for remote access. Access to IoT devices, whether within the same environment or remotely via the Internet, requires proper security mechanisms in order to avoid disclosing any secure information or access privileges. Authentication, on which other security classes are built, is the most important part of IoT security. Without ensuring that the authorized party is who it claims to be, other security factors would be useless. Additionally, with the increased mobility of IoT devices, traditional authentication mechanisms, such as a username and password, are less effective. Numerous security challenges in the IoT domain have resulted in the proposal of many different approaches to authentication. Many of these methods require either carrying an authentication token, such as a smartcard, or restricting access to a particular physical location. Considering that most IoT devices contain a wide array of sensors, a large amount of contextual information can be provided. Thus, real-time security mechanisms can protect user access by, for example, utilizing contextual information to validate requests. A variety of contextual information can be retrieved to strengthen the authentication process, both at the time of access request and throughout the entire access session, without requiring user interaction, which avoids the risk of being discovered by attackers of these features. In this paper, we introduce a continuous authentication framework that integrates contextual information for user authentication in smart homes. The implementation and evaluation show that the framework can protect smart devices against unauthorized access from both anonymous and known users, either, locally or remotely, in a flexible manner and without requiring additional user intervention. Full article

The transverse arch of the foot receives and transfers loads during gait. We aim to identify the difference in its structure between normal feet and hallux valgus (HV) feet and the effects of loading. Two groups, Without-HV and With-HV (HV ≥ 20°), were assessed using a weight-bearing plantar ultrasound imaging device to view the structure of the transverse arch. Measurements were recorded in sitting, quiet standing, and 90% weight-shift (90% W.S.) loading positions on the tested foot. Images were then processed using ImageJ software to analyze the transverse arch length (TAL), the length between the metatarsal heads (MTHs), transverse arch height (TAH), and the height of each MTH. TAL significantly increased in all positions in the With-HV group compared to that in the Without-HV group. It also increased in both groups under loading. TAH was not significantly higher in the With-HV group than in the Without-HV group in sitting and standing positions, except in the 90% W.S position, where both groups showed similar results. TAH decreased in both groups under loading. In summary, the structure of the transverse arch changes in HV feet and under loading conditions. This finding will help understand the structural differences between normal and HV feet and help resolve shoe fit problems in individuals with HV deformity. Full article

The method of earned value management is traditionally applied to the progress assessment of actual software projects in many IT companies. Also, Open Source Software (OSS) are used under the various situations because of cost reduction, standardization, and quick delivery. In particular, many OSS are developed and managed by using fault big data recorded on the bug tracking systems. Several research papers in terms of OSS reliability assessment have been published in the past. The fault is caused by the poor handling of effort control. Therefore, we can make a stable and safety operation for OSS system if the OSS project is appropriately managed by using the software effort. The OSS effort estimation model by using a conventional stochastic differential equation model is discussed in this paper. Then, we propose the optimal maintenance problem based on the earned value requirement. In particular, we develop the OSS project stability support tool. Furthermore, several performance illustrations of the developed software tool are shown by using the effort data under actual OSS project. Full article