Anthony A. Adole1, Dr. Chris Bearchell2 and Prof. Eran Edirisinghe1, 1Department of Computer Science, EPSRC Centre for Doctoral Training in Embedded Intelligence, Loughborough University, Leicestershire, UK, 2Surface Intelligence, Oxford, UK
In recent years detection and recognition of Off-line handwriting character has being a major task in the computer vision sector, researchers are looking on to developing deep learning models to avoid the traditional approaches which involves the tedious task of using the conventional methods for feature extraction and localization. However, state-of-the-art object detection modelsrely upon region proposal algorithms as a result, they settle for object locations principles, such network reduces thetime period of those detection network, exposing region proposal computation as a bottleneck. Faster-RCNN is a popular model used for recognition purpose in many recognition tasks, the goal of this paper is to serve as a guide for Multi-Classification on offline Handwriting Document using Pre-trained Faster-RCNN with inception resnet v2 feature Extractor. The result obtained from the experiments shows improved pre-trained models can be used insolving the research question concerning handwriting detection and recognition.
Offline Handwriting recognition and detection, faster-RCNN, inception resnet v2, Kanji handwriting, Japanese offline document, recognition and detection
Yueqi Han1,2, Bo Yang1,2, Yun Zhang1, Bojiang Yang1 and Yapeng Fu1,2, 1College of Meteorology and Oceanography, National University of Defense Technology, Nanjing, China, 2National Key Laboratory on Electromagnetic Environmental Effects and Electro-optical Engineering, PLA Army Engineering University, Nanjing, China
Data assimilation (DA) for the non-differentiable parameterized moist physical processes is a complicated and difficult problem, which may result in the discontinuity of the cost function (CF) and the emergence of multiple extreme values. To solve the problem, this paper proposes an inner/outer loop ensemble-variational algorithm (I/OLEnVar) to DA. It uses several continuous sequences of local linear quadratic functions with single extreme values to approximate the actual nonlinear CF so as to have extreme point sequences of these functions converge to the global minimum of the nonlinear CF. This algorithm requires no adjoint model and no modification of the original nonlinear numerical model, so it is convenient and easy to design in assimilating the observational data during the non-differentiable process. Numerical experimental results of DA for the non-differentiable problem in moist physical processes indicate that the I/OLEnVar algorithm is feasible and effective. It can increase the assimilation accuracy and thus obtain satisfactory results. This algorithm lays the foundation for the application of I/OLEnVar method to the precipitation observational data assimilation in the numerical weather prediction (NWP) model.
Ensemble-variational Data Assimilation, Non-differentiable, Inner/Outer Loop
Ting-Yu Lin, Chia-Min Lai and Chi-Wei Chen, Institute for Information Industry, Taipei, R.O.C
Due to the advent of the Internet of Things era, the number of related wireless devices is increasing, making the abundant and complex information networks formed by communication between devices. Therefore, security and trust between devices a huge challenge. In the traditional identification method, there are identifiers such as hash-based message authentication code, key, and so on, often used to mark a message that the receiving end can verify it. However, this kind of identifiers is easy to tamper. Therefore, recently researchers address the idea that using RF fingerprint, also called radio frequency fingerprint, for identification. Our paper demonstrates a method that extracts properties and identifies each device. We achieved a high identification rate, 99.9% accuracy in our experiments where the devices communicate with Wi-Fi protocol. The proposed method can be used as a stand-alone identification feature, or for two-factor authentication.
Internet-of-Things (IoT), Authentication, RF fingerprint, Machine Learning (ML), Device Identification
Hasara Maithree, Dilan Dinushka and Adeesha Wijayasiri, Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka
Many researches have been carried out for change detection using temporal SAR images. In this paper an algorithm for change detection using SAR videos has been proposed. There are various challenges related to SAR videos such as high level of speckle noise, rotation of SAR image frames of the video around a particular axis due to the circular movement of airborne vehicle, non-uniform back scattering of SAR pulses. Hence conventional change detection algorithms used for optical videos and SAR temporal images cannot be directly utilized for SAR videos. We propose an algorithm which is a combination of optical flow calculation using Lucas Kanade (LK) method and blob detection. The developed method follows a four steps approach: image filtering and enhancement, applying LK method, blob analysis and combining LK method with blob analysis. The performance of the developed approach was tested on SAR videos available on Sandia National Laborataries website and SAR videos generated by a SAR simulator.
Remote Sensing, SAR videos, Change Detection
MinhTri Tran, Anna Kuwana and Haruo Kobayashi, Division of Electronics and Informatics, Gunma University, Kiryu 376-8515, Japan
Proposed derivation and measurement of self-loop function for a low-pass Tow Thomas biquadratic filter are introduced. The self-loop function of this filter is derived and analyzed based on the widened superposition principle. The alternating current conservation technique is proposed to measure the selfloop function. Research results show that the selected passive components (resistors, capacitors) of the frequency compensation of Miller’s capacitors in the operational amplifier and the Tow Thomas filter can cause a damped oscillation noise when the stable conditions for the transfer functions of these networks are not satisfied.
Superposition, Self-loop Function, Stability Test, Tow-Thomas Biquadratic Filter, Voltage Injection
MinhTri Tran, Anna Kuwana, and Haruo Kobayashi, Division of Electronics and Informatics, Gunma University, Kiryu 376-8515, Japan
Proposed stability test for RLC low-pass filters is presented. The self-loop functions of these filters are derived and analyzed based on the widened superposition principle. The alternating current conservation technique is proposed to measure the self-loop function. An active inductor is replaced with a general impedance converter. Our research results show that the values of the selected passive components (resistors, capacitors, and inductors) in these filters can cause a damped oscillation noise when the stable conditions for the transfer functions of these networks are not satisfied.
Widened Superposition, RLC Low-Pass Filter, Stability Test, Self-loop Function, Voltage Injection
Ye-Shun Shen, Fang-Biau Ueng* and Hung-Sheng Wang, Department of Electrical Engineering,National Chung-Hsing University, Taichung, Taiwan
Single carrier-frequency division multiple access (SC-FDMA) has been adopted as the uplink transmission standard in fourth generation cellular network to enable the power efficiency transmission in mobile station. Since multiuser multiple input multiple output (MU-MIMO) is a promising technology to fully exploit the channel capacity in mobile radio network, this paper investigates the uplink transmission of MU-MIMO SC-FDMA system with orthogonal space frequency block codes (SFBC). It is preferable to minimize the length of the cyclic prefix (CP) to improve the transmission energy and spectrum efficiency. Several techniques for block transmission without CP have been investigated. CP removal at the transmitter is compensated by a CP reconstruction at the receiver where only the past interference components are considered. In this paper, the chained turbo equalization with chained turbo estimation is employed in the designed receiver. The chained turbo estimation employs short training sequence (TR) that can improve the spectrum efficiency without sacrificing the estimation accuracy. In this paper, we propose a novel spectrally efficient iterative joint channel estimation, multiuser detection and turbo equalization for MU-MIMO SC-FDMA system without CP and with short TR. Some simulation examples for uplink scenario are given to demonstrate the effectiveness of the proposed scheme.
MU-MIMO SC-FDMA, chained turbo equalization, chained turbo estimation
Tubonimi Jenewari and David Mulvaney, Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, UK
The aim of the research is to provide a framework for prototyping executable model for distributed embedded system, it includes both hardware and software, in this way the modelling and implementation process will allow seamless execution of distributed system at functional, hardware simulation and hardware realization levels. An example of the setup will be an ABS (anti-lock braking system) that comprises of system components; sensor, actuator, ECU’s which are interconnected through the vehicle communication bus. Software automobile vehicle model will provide the vehicle dynamics and the model of the ABS that will mimic the functionality of ABS. Multiple processors (ECU) will be interconnected in a distributed format to reduce the time of execution.
All this is captured in a Unified Modelling Language (UML) a standard for the idea of the design to be captured, expressive enough to be understood. The UML notation will be converted to Extensible Markup Language (XML), and a parser code written to extract all the necessary class information (to get the classes where the simulation code will come in) from the overwhelming lines of XML to be transferred to the ECU for execution of the simulation. The simulation is carried out using GDB.
Prototyping, distributed embedded system, executable model, UML
Sudeep Kumar, Deepak Kumar Vasthimal, and Musen Wen, eBay Inc., 2025 Hamilton Ave, San Jose, CA 95125, USA
Today, a plethora of distributed applications are managed on internally hosted cloud platforms. Such managed platforms are often multi tenant by nature and not specifically tied to a single use-case. Smaller footprint of infrastructure on a managed cloud platform has its own set of challenges especially when applications are required to be infrastructure aware for quicker deployments and response times. There are often times and challenges to quickly spawn ready to use instances or hosts on such infrastructure. As part of this paper we outline mechanisms to quickly spawn ready to use instances for application while also being infrastructure aware. In addition, paper proposes architecture that provides high availability to deployed distributed applications.
cloud computing, virtual machine, elastic, elastic search, consul, cache, java, kibana, mongoDB, high performance computing, architecture.
Mridula Korde, Department of Electronics and Communication Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Increasing Internet data traffic has driven the capacity demands for currently deployed 3G and 4G wireless technologies. Now, intensive research toward 5th generation wireless communication networks is progressing in many fronts. 5G technologies are expected to be in use around 2020. Moving toward 5G, network synchronization is expected to play a key role in the successful deployment of the new mobile communication networks. Synchronization is an essential prerequisite for all mobile networks to operate. It’s fundamental to data integrity, and without it data will suffer errors and networks can suffer outages. ‘Loss of synchronization’ problems can be difficult to diagnose and resolve quickly and add to operational costs. Poor synchronization affects customer satisfaction and is therefore revenue affecting too. This paper presents synchronization requirement and related aspects in upcoming 5G technologies.
5G, MIMO, Synchronization
Mason Chen, OHS, Stanford University, Palo Alto, USA
This paper will address Altitude Sickness risk when hiking on the high Mountains. It’s very risky if the people are not aware of their altitude sickness symptom such as Fatigue, Headache, Dizziness, Insomnia, Shortness of breath during exertion, Nausea, Decreased appetite. The consequence of altitude sickness could be dangerous on the inconvenient high mountains. Pulse Oximeter was used to monitor the Oxygen% and Heart Beat at different altitude levels from near-sea level in San Jose, Denver (5,000 Feet), Estes Park (8,000 Feet), Rocky Mountains Alpine Center (12,000 Feet). 2.5-mins Jumping Rope exercise was conducted to analyze the fatigue behavior associated with Altitude Sickness. Statistical analysis was conducted to verify several hypotheses to predict the Altitude Sickness Risk as well as the Exercise Fatigue Behavior. This paper has demonstrated how to assess their body strength and readiness before they may take a strenuous hiking on the high mountains.
JMP, Statistics, Altitude Sickness, Data Mining, AI
Emeka Ogbuju, Federal University Lokoja, Nigeria
Big data has been defined in terms of the V-dimensions, namely volume, variety and velocity to mention a few. It is within the context of this definition of big data that some database models have been faulted and departure from their usage contemplated by the database community. The drive towards a one-size-fits-all the dimensions of data as proposed by several researchers may end up as a mirage given that the application area determines the priority each dimension gets in a software development project. A transaction-laden application may demand more of the volume dimension of big data and a guarantee of the ACID properties of its transaction than a variety of data types. Given that it is not always the case that all the dimensions are required on every application, this paper is of the view that it may yield more results if database models are rated and used on the basis of their inherent strengths augmented by the extent to which they can be made adaptive to some or all the V-dimensions of data. Based on this submission, a volume-adaptive big data model of the relational database model is proposed. The model partitions a relation such that the sum of all partitions makes up the original relation. The query times of equivalent queries on the original and any of the partitions show that the query time of the partitions are well optimised relative to the query time of the original relation. The partitions are scalable across several servers and in this way, the model adapts to the volume dimension of data and at the same time, takes advantage of the ACID properties of the relational database model.
Big Data, V-dimensions of data, adaptive model of relational DBMS, application prototypes, NoSQL, ACID properties
Gustavo A. Lado and Enrique C. Segura, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires
This paper presents a new technique for efficient coding of highly dimensional vectors, overcoming the typical drawbacks of classical approaches, both of the type of local representations and those of distributed codifications. The main advantages and disadvantages of those classical approaches are revised and a novel, fully parameterizaed strategy is introduced to obtain representations of intermediate levels of locality and sparsity, according to the neccesities of the particular problem to deal with. The proposed method, called COLOSSUS (COding with LOgistic Softmax Sparse UnitS) is based on an algorithm that permits a smooth transition between both extreme behaviors -local, distributed- via a parameter that regulates the sparsity of the representation. The activation function is of the logistic type. We propose an appropiate cost function and derive a learning rule that happens to be similar to the Oja's Hebbian learning rule. Experiments are reported that show the efficiency of the proposed technique.
Neural Networks, Sparse Coding, Autoencoders
Haixin Wang1,2 and Jianxin Shen1, 1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China and 2School of Mechanical & Aerospace Engineering, Queen's University Belfast, Belfast, UK
One of the challenges for autonomous aircraft is safe and reliable navigation in urban or indoor environments. The path planning of aerial robot is a complicated task due to the factors such as the decreasing accuracy of global positioning system (GPS), the narrow space and the dynamic movement of obstacles. To navigate effectively in such an environment, one of the skills an agent needs is to develop the ability to avoid collisions. In this study, we investigated a possible approximation (called a partially observable Markov decision process) to improve the performance of autonomous UAVS in GPS-free environments by combing the newly developed A3C reinforcement learning approach. Developing and testing algorithms in the real world is expensive and time consuming for drones. In addition, taking advantage of current research and advances in machine intelligence requires the collection of extensive training and testing to determine changes in conditions and environment. This article leverages open source tools such as Microsoft’s state-of-the-art drone simulator Airsim, a machine learning framework that leverages TensorFlow, a tensor library of Google. The main method of the paper is Asynchronous Advantage Actor-Critic network.
Unmanned aerial vehicle, Asynchronous advantage actor-critic algorithm, Simulation, Path planning
Hung Lay2, Peiqi Gu1 and Yu Sun2, 1University High School, 2California State Polytechnic University, Pomona, CA 91768
Web Application, ReactJS
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