Social media is a platform in which tons and tons of text are generated each and every day. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, the text data which is used for the classification is tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are the way of expression of the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for the purpose of the classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) which is also one of the machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. For the research purpose the dataset has been made publically available.

In recent years, the screen-to-body ratio of mobile handsets has been increasing. Today, the screen nearly fills up the entire front side. Conventionally, the screen is mainly seen as a metallic object that adversely affects antenna performance. In this paper, the large screen is used for the first time to facilitate an additional uncorrelated MIMO port in a tri-port design, for several LTE bands below 1 GHz. To this end, the design procedure explicitly considers the screen together with the terminal chassis, which can be simply modelled as two metal plates. In particular, characteristic mode analysis of the double-plate model enables a sufficient number of resonant modes to be created, tuned and selectively excited to yield three uncorrelated MIMO ports in the low band. Simulation and measurement results are found to be in good agreement. The measured bandwidths of the three ports are 23%, 17% and 21%, respectively. Within the operating band, the measured isolation is above 13 dB, envelope correlation coefficient below 0.16 and average total efficiency above 72%.

During the last two decades, profound technological changes have taken place around us, supported by disruptive advances, both on software and hardware sides. An amalgamation of information, communication and AI is taking place, as well as the cross-fertilization of a wide range of concepts, referred to as the digital transformation. As a result of convergence phenomenon, the boundaries between different disciplines are eroding, necessitating a thorough discussion on what the engineering education should be like in the future. In this paper, after presenting a brief history of engineering education, the recent paradigm changes are discussed, which essentially stress that skills must prevail over degrees to deal with challenges posed by the megatrends of the fourth industrial revolution.

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.

Bank safety deposit box is one of the secure storage for the clients to store their valuables such as important property or business paper, gold or even money but is in some case scenario traditional safe deposit are not safe enough, there have been many robberies from this safe box because of the code lock can be hacked and there no alarming system in this kind of safe boxes. In this paper I proposed a smart secure safe box with facial reorganization unlock feature using raspberry pi and uno Arduino which can used to secure your documents or valuables in a bank or at your home. In this I have used raspberry pi and pi camera to perform the face unlock and to send security alert through email. Secondly, I have used Arduino and hall sensor to trigger an alarm when the door is open unauthorized or kept open.

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.

Developing Chatbot Wrapper for Online Shopping: A Case Study of Using Generic Mobile Messaging System. A conversational UI chatbot using Telegram API was implemented.

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.

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.

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.

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.

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.

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.

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.

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.

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.