Design and Development of an Internet of Things (IoT)-Based Anti-Theft System in Museum Cultural Relics Using RFID

Art colonnades and museums all over the world are the first option for individuals to visit for the enhancement of the cultural life of people. To ensure their safety, museums have established numerous cultural security measures. Traditional strategies do not obstruct their pace entirely. They only use a computer in the museum to check individuals at the entrance and exit. Therefore, the authors proposed a gallery anti-stealing device created on the internet-of-things (IoT) technology that ensures security through passive readers/writers of RFID. Radio frequency identification (RFID) remains a system that practices isolated data storing and recovery and offers object tracking with a unique identity code. The system then sends sound and light warning information, while the photographic camera structure is triggered to capture a picture at the same time. The recognition of the accuracy in the hardware component of the device can be additionally enhanced by the use of this technology to increase the safety of museum equipment.

Iot Based Smart Switch Board Technology For Smart Home

To overcome the limitations of the traditional switch board, a new system equipped with latest technologies has been developed as Smart Switch Board and which is the smarter version compared to traditional Switch Boards. An IOT based switching device has been developed and which to be controlled via Blynk application. This smart device is very useful for handicapped persons. The smart switch meter has been used to control the smaller areas if it is connected through the router. It makes the system more secured as it is working through router’s IP address. Physical contact is not required to operate; it ensures reliability and safety from fire accidents. The Hardware component are quite cheap and easy to replace.

Reliability of computer structures of integrated modular avionics for hardware configurations

The problem of designing advanced computing systems in the class of structures of integrated modular avionics is considered. The unified topology of the internal network of the computer on the basis of Space Wire exchange channels and variants of its execution for various onboard applications is offered. Equivalent reliability schemes of each of the specific structures are introduced and the probabilities of trouble-free operation of each structure are analyzed. Families of graphic dependencies are given. The analysis of the existing principles and algorithms for testing multiprocessor multimodal onboard digital computer systems is given; the new testing algorithm for the multiprocessor systems which follows the software design standards for products of integrated modular avionics is offered. The structure of the unified automated workplace for checking the functional modules of integrated modular avionics is considered. Specific requirements inherent in the workplaces for testing integrated avionics are identified: an increased level of control of the hardware component of products; the ability to simulate the failure state of individual components of avionics to check the mode of reconfiguration of the computer system; modular construction of software with the division of verification tests into components performed at the level of each CPM and the computer as a whole in single-task and multitasking modes; openness of architecture of a workplace, which provides an ability to change the level of control complexity of a product and control of one class of complexity; intra-project unification of both hardware and software of the workstation of the inspection.

Development of Intelligent System for Alert Notification in Indoor Planting

The past decade has seen significant advancement in the field of agriculture industry. Various smart appliances such as cellular phones, moisture sensors, humidity sensor and smart irrigation are set to realize the concept of a new smart farming with the help of latest technology. In Malaysia, farmers experience crop damage and decrease in plant quantity and quality because they unable to monitor the crop all day. The development of a monitoring system that can helps farmer grow crops is enticing demand for busy individuals with physical limitations. Global System for Mobile Communication (GSM) technology, which has emerged in the late 1970s, is an ideal solution for this problem. In this paper, a development of intelligent system for alert notification in indoor planting is presented. This paper describes an application of GSM technology for monitoring light system in indoor planting with the use of hardware component like Arduino board, GSM SIM900A, LDR and LED strip. The major role of this system is to enable farmers to get notified when the light system for their plants is down through GSM SIM900A. Each time the light system is light on and light off, the farmers will receive an SMS to notify them. System functional testing was carried to evaluate the performance of implementing GSM SIM900A whether the prototype is free from error or there are a few errors occurs. The results shown that, the system is well functioning for alert notification in indoor planting monitoring. In conclusion, the development of intelligent systems for alert notification in indoor planting was developed using Arduino and GSM SIM900A to able farmers notified about their indoor planting when to be monitored.

Autonomous long-range drone detection system for critical infrastructure safety

The development of unmanned aerial vehicles has been identified as a potential source of a weapon for causing operational disruptions against critical infrastructures. To mitigate and neutralise the threat posed by the misuse of drones against malicious and terrorist activity, this paper presents a holistic design of a long-range autonomous drone detection platform. The novelty of the proposed system lies in the confluence between the design of hardware and software components to effective and efficient localisation of the intruder objects. The research presented in the paper proposes the design and validation of a situation awareness component which is interfaced with the hardware component for controlling the focal length of the camera. The continuous stream of media data obtained from the region of vulnerability is processed using the object detection that is built on region based fully connected neural network. The novelty of the proposed system relies on the processing of multi-threaded dual-media input streams that are evaluated to mitigate the latency of the system. Upon the successful detection of malicious drones, the system logs the occurrence of intruders that consists of both event description and the associated media evidence for the deployment of the mitigation strategy. The analytics platform that controls the signalling of the low-cost sensing equipment contains the NVIDIA GeForce GTX 1080 for detecting drones. The experimental testbeds developed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, drone flight path, tradeoff between the angle of coverage against the distance of coverage. The validation of the proposed system has resulted in yielding a range of intruder drone detection by 250m with an accuracy of 95.5%.

Hardware Failure Prediction on Imbalanced Times Series Data

Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil’s condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 6:1662–1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30% and accuracy of 99.83%. Thus, MRI head/neck coils can be classified normal or broken by training a LSTMFCN on image features, successfully. Augmenting the data using GP-generated samples can improve the performance even further.

Tempus-A Facial Recognition Technology in Attendance Monitoring

Attendance monitoring has strategic importance for every organization. It has shifted from utilizing paper-based attendance monitoring to biometrics, radio-frequency identification, Bluetooth and smart technologies, Internet of Things (IoT), cloud computing, or face recognition technology. Tempus is an automated attendance monitoring system that uses face recognition technology for input, real-time IoT capabilities for processing, and portability of mobile platforms for output. It has hardware and software components. The core of the hardware component is Raspberry Pi 3, which serves as a communication medium between the camera sensor and the information system. Tempus uses Haar Cascade for facial detection and Linear Binary Pattern Histogram (LBPH) for facial recognition. The software component is further divided into two: 1) the information system for administrators, an attendance monitoring which allows encoding of data, creating new user accounts, managing schedules, recording attendance, and generating reports; and 2) mobile platform for end-users, the teachers, that is provided for communication and notification purposes only.

The Application of Mamdani Method for Predicting The Best Portable Computer Based on Hardware and Price

A portable computer is a technology tool widely used among students and students. With a very helpful role as in typing needs, presentations, and math calculations. A variety of carry-on computers that many certainly make one difficult to determine a decent and good portable computer to use. In general, in the process of selecting a portable computer, there is no recognized standard to determine the recommended portable computer level. The purpose in this research is to produce predictive values that will be a reference in supporting decisions in determining a portable computer that complies with hardware component criteria and pricing. This study implemented FIS Mamdani models with the analysis stage of the formation of fuzzy sets, application of implications function, rule composition and defuzification. The result of this research is an output of predictive value based on hardware component inputs and prices that will assist the user in supporting decisions in determining the best carry-on computer and according to what they want.Keywords: Predictions, Fuzzy Inference System, Mamdani methods, portable computers, students.

New approach to practical leakage-resilient public-key cryptography

We present a new approach to construct several leakage-resilient cryptographic primitives, including leakage-resilient public-key encryption (PKE) schemes, authenticated key exchange (AKE) protocols and low-latency key exchange (LLKE) protocols. To this end, we introduce a new primitive called leakage-resilient non-interactive key exchange (LR-NIKE) protocol. We introduce an appropriate security model for LR-NIKE protocols in the bounded memory leakage (BML) settings. We then show a secure construction of the LR-NIKE protocol in the BML setting that achieves an optimal leakage rate, i.e., 1 – o(1). Our construction of LR-NIKE requires a minimal use of a leak-free hardware component. We argue that the use of such a leak-free hardware component seems to be unavoidable in any construction of an LR-NIKE protocol, even in the BML setting. Finally, we show how to construct the aforementioned leakage-resilient primitives from such an LR-NIKE protocol as summarized below. All these primitives also achieve the same (optimal) leakage rate as the underlying LR-NIKE protocol. We show how to construct a leakage-resilient (LR) IND-CCA-2-secure PKE scheme in the BML model generically from a bounded LR-NIKE (BLR-NIKE) protocol. Our construction of LR-IND-CCA-2 secure PKE differs significantly from the state-of-the-art constructions of these primitives, which mainly use hash proof techniques to achieve leakage resilience. Moreover, our transformation preserves the leakage-rate of the underlying BLR-NIKE protocol. We introduce a new leakage model for AKE protocols, in the BML setting, and present a leakage-resilient AKE protocol construction from the LR-NIKE protocol. We introduce the first-ever leakage model for LLKE protocols in the BML setting and the first construction of such a leakage-resilient LLKE from the LR-NIKE protocol.