To assess the reliability of dc energy measurement equipment on-board trains, a setup was developed to characterize current transducers under realistic operating conditions. The operating principle is based on a current ratio measurement technique. The reference sensor is a high-precision zero-flux current transducer in combination with a broadband high-precision current shunt. The influence of ac distortion on this reference sensor was found to be within a few parts in 106 using an initial version of the setup, in which ac distortion was applied through a separate winding. A revised version of the setup employs a programmable electronic load to apply dynamic currents up to 600 A. The setup was used to characterize a 100 μohm high-current shunt resistor. The effect of dissipative heating on the dc transresistance error was around 0.03 %, with a settling time of about half an hour. The short-term intrinsic current dependence was also around 0.03 %. The effect of ac distortion was within a few parts in 106. The intrinsic current dependence and the onset of the heating effect were also observed when exposing the sensor to a dynamic current profile that was recorded during a trip between two successive underground train stations on Metro de Madrid. These results demonstrate that the setup described in this paper is very effective for characterizing dc current sensors for practical railway applications. Future work will concentrate on even more demanding current signals, such as chopped signals, and on other types of sensors and measurement systems.

In this paper, we design a mobile 2D platformer game named ‘ExerAdventure’ where progress inside the game world is dependent on physical activity in the real world. The game has two modes, namely adventure and story mode. This design can motivate people to lead an active and healthy lifestyle.

Civic technology is a fast-developing segment that holds huge potential for a new generation of startups. A recent survey report on civic technology noted that the sector saw $430 million in investment in just the last two years. It’s not just a new market ripe with opportunity it’s crucial to our democracy. Crowdsourcing has proven to be an effective supplementary mechanism for public engagement in city government in order to use mutual knowledge in online communities to address such issues as a means of engaging people in urban design. Government needs new alternatives – alternatives of modern, superior tools and services that are offered at reasonable rates. An effective and easy-to-use civic technology platform enables wide participation. Response to, and a ‘conversation’ with, the users is very crucial for engagement, as is a feeling of being part of a society. These findings can contribute to the future design of civic technology platforms. In this research, we are trying to introduce a crowdsourcing platform, which will be helpful to people who are facing problems in their everyday practice because of the government services. This platform will gather the information from the trending twitter tweets for last month or so and try to identify which challenges public is confronting. Twitter for crowdsourcing as it is a simple social platform for questions and for the people who see the tweet to get an instant answer. These problems will be analyzed based on their significance which then will be made open to public for its solutions. The findings demonstrate how crowdsourcing tends to boost community engagement, enhances citizens ‘ views of their town and thus tends us find ways to enhance the city’s competitiveness, which faces some serious problems. Using of topic modeling with Latent Dirichlet Allocation (LDA) algorithm helped get categorized civic technology topics which was then validated by simple classification algorithm. While working on this research, we encountered some issues regarding to the tools that were available which we have discussed in the ‘Counter arguments’ section.

This is a research project which uses computer vision techniques to find out the empty parking spaces in any given Parking lot. It uses existing functions from OpenCV library in Python programming language to extract the parking lines from an image.

This research is based on developing a drones which have significant importance in the current market with many new inventions. In this paper; we tried to build a drone that is controlled by using a virtual assistant called Amazon Alexa. This drone will be using only the receiver other than the transmitter and also built-in services of amazon help us to create voice recognition understandable by the system. We have succeeded 70% of the work and achieved good results in connecting Amazon Alexa and Arduino Yun based drone.

In this age of digital advancements, where technologies are changing in a fraction of time. From the abacus which made tutoring math easy millennia back, to word processor which changed the way research paper is being written and presented. After every era, with the advancements in the technology has not only given shaped the education but also transformed it. There was a time when the education world of black on white changed to projected presentations. However, now in this paper, we believe to go beyond the two-dimensional space and create a whole new educational world for children. Augmented Reality (AR) has successfully made classroom learning more interactive and engaging for students as well as for teachers to deliver their lectures. AR is the combination of the real-world with computer- generated world. It is one of the most emerging fields in computer science. The conventional approach for learning can be stressful and to a certain extent less effective for some students. So, we propose a system in which students use smart devices like tablets, mobile, etc. that act as an alternative to boring supportive textbooks. Also, we plan to develop an application consisting of two modules like interactive learning and fun examination, a hybrid of the traditional approach and innovative practical illustrations of complicated concepts leading education in another dimension.

Communication for control-centric industrial applications is characterized by the requirements of very high reliability, very low and deterministic latency and high scalability. This paper proposes a novel solution for providing reliable and deterministic communication, through Wi-Fi, in industrial environments. The proposed solution, termed as HAR2D-Fi, adopts hybrid channel access mechanisms for achieving deterministic communication. It also provides temporal redundancy for enhanced reliability. HAR2D-Fi implements different medium access control (MAC) designs that build on the standard physical (PHY) layer. Such designs can be classified into two categories: (a) MAC designs with pre-defined (physical) time-slotted schedule, and (b) MAC designs with virtual time-slotted schedule. Performance evaluation, based on analysis and system-level simulations, demonstrates the viability of HAR2D-Fi for control-centric industrial applications.

Emerging of Cognitive Radio (CR) technology has provided optimistic solution for the dearth of spectrum by improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference free transmission in a cognitive radio networks, an important part for unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate with each other to make the final sensing decision. This overcome the hidden terminal problem and also improve the overall reliability of the decisions made about the presence or absence of a licensed user. Each unlicensed user send the sensing results to the base station for final decision. However there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust aware model is proposed for detection of misbehaving nodes such that their sensing reports can be filter out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks.

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

Versatile configurable defected ground structures (CDGSs) for enhancing the performance of low profile antennas are introduced. It is shown that CDGS can significantly reduce mutual coupling (MC) between multiple antennas and suppress cross-polarization (XP) and enhance circular polarization (CP) excitation in single port low profile antennas for example. The key idea of CDGS is to construct defected ground structures (DGSs) from a grid of slots, which can be either opened or shorted with hardwires, so that they can be configured and optimized to enhance desired antenna performance characteristics. The importance and versatility of the CDGS approach is that it overcomes the issue of having to design bespoke DGS for each individual antenna design. Three design examples are provided to demonstrate the versatility of CDGSs for MC reduction, XP suppression and CP excitation. Experimental results demonstrate that MC can be reduced by up to 43 dB, XP can be suppressed by 15 dB and CP can be excited with 78 MHz (2.2%) 3-dB axial ratio (AR) bandwidth. The compactness and ease of fabrication also make the CDGS well suited to compact low profile internet of things (IoT) and wireless communication applications.

According to kids’ learning process, an auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. The network can achieve its intended functionality through testing by mnist dataset and convolution neural network. Round function is added between the abstract network and concrete network in order to get the representative generation of class. The generation ability can be increased by adding jump connection and negative feedback. At last, the characteristics of the network is discussed. The input can be changed to any form by encoder and then change it back by decoder through inverse function. The concrete network can be seen as the memory stored by the parameters. Lethe is that when new knowledge input, the training process makes the parameters change. At last, the application of the network is discussed. The network can be used for logic generation through deep reinforcement learning. The network can also be used for language translation, zip and unzip, encryption and decryption, compile and decompile, modulation and demodulation.

To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test, we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The Concrete network (generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, we can conclude that one neural network can do image recognition, speech recognition, nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. By proof, fully connected network can do what convolution neural network and recurrent neural network do, so fully connected network is the universal network. The phenomenon of synesthesia is the result of multi-input and multi-output. Connection in mind can realize through the universal network and sending the output into input. Connection in mind is the key of creativity, synesthesia is the assistant.

Arranging some magnets can intensify its magnetic field, as known as Halbach array. Magnets can be considered as a 3 dimensional array of magnetic dipoles. Optimizing these magnetic dipole directions, magnets can be more stronger. This article shows how to ditermine these directions, and how to magnetize ferromagnetic material with that directions.

We propose BSC, a Bitcoin Smart Contract implementation. It integrates the functionality of smart contracts into the Bitcoin system, giving developers the ability to build decentralized applications on Bitcoin. BSC will require a new hard fork, on which Bitcoin holders can use their existing funds directly. BSC combines the unlimited creative space of smart contracts and the vast network effect of Bitcoin, which will bring even more possibilities to the cryptocurrency world.

The ongoing decommissioning of conventional power plants decreases the installed reactive power reserves for voltage control in transmission grids. Hence, an efficient planning of compensation devices substituting this lack of reactive power is required. Grid operators must allocate these devices for steady-state voltage control and for dynamic voltage control ensuring voltage stability. A separate determination of this static and dynamic VAR demand, however, fails to exploit synergies and disregards that VAR compensation in steady-state reduces the reserves for dynamic compensation. This paper proposes a coupled determination of the system static and dynamic VAR demand. An optimisation method applying mixed-integer programming identifies an efficient allocation and portfolio consisting of different compensation technologies. It includes constraints for voltage limits during steady-state and contingencies as well as for long-term voltage stability. Results emphasise that the method identifies an efficient portfolio for various operation and fault scenarios, while providing the required voltage stability margin.

In this paper, we present results from a world-first radiant cooling pavilion, demonstrating a method of cooling people without cooling the air. Instead, surfaces are chilled and thermal radiation is used to keep people cool. A thermally-transparent membrane is used to prevent unwanted air cooling and condensation, a required precursor to deploying radiant cooling panels without humidity control in tropical environments. The results from this thermal comfort study demonstrate the ability to keep people comfortable with radiation in warm air, a paradigm shifting approach to thermal comfort that may help curb global cooling demand projections.

The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every “thing’‘ to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving traffic and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in “Vehicle-to-Vehicle’‘ (V2V) and “Vehicle-to-Infrastructure” (V2I) communication. To improve “Vehicle-to-Everything” (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform. Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.

[Paper accepted at ACNS 2020] In this paper, we propose a simple and effective attack on the recently introduced Smartphone Authentication with Built-in Camera Protocol, called ABC. The ABC protocol uses the photo-response non-uniformity (PRNU) as the main authentication factor in combination with anti-forgery detection systems. The ABC protocol interprets the PRNU as a fingerprint of the camera sensor built-in a smartphone device. The protocol works as follows: during the authentication process, the user is challenged with two QR codes (sent by the server) that need to be photographed with a pre-registered device. In each QR code, the server embeds a unique pattern noise (not visible to the naked eye), called probe signal, that is used to identify potential forgeries. The inserted probe signal is very similar to a genuine fingerprint. The photos of QR codes taken by the user are then sent to the server for verification. The server checks (i) if the photos contain the user’s camera fingerprint (used to authenticate the pre-registered device) and (ii) if the photos contain the embedded probe signal. If an adversary tries to remove (subtract) his own camera fingerprint and replace it with the victim’s camera fingerprint (computed from photos shared on social media), then he will implicitly remove the embedded probe signal and the attack will fail. The ABC protocol is able to detect these attacks with a false acceptance rate (FAR) of 0.5%. However, the ABC protocol wrongly assumes that the attacker can only determine his own camera fingerprint from the photos of the presented QR codes. The attack proposed in our work is able to get past the anti-forgery detection system with a FAR of 54.1%, simply by estimating the attacker’s camera fingerprint from a different set of photos (e.g. five photos) owned by the attacker. This set of photos can be trivially obtained before the attack, allowing the adversary to compute his camera fingerprint independently of the attack. The key to the success of our attack is that the independently computed adversary’s camera fingerprint does not contain the probe signal embedded in the QR codes. Therefore, when we subtract the adversary’s camera fingerprint and add the victim’s camera fingerprint, the embedded probe signal will remain in place. For this reason, the proposed attack can successfully pass through the anti-forgery detection system of the ABC protocol. In this paper, we also propose a potential fix based on analyzing signals from built-in motion sensors, which are not typically shared on social media.