Estimate the Memory Bounds Required by Shared Variables in Software Transactional Memory Programs

Estimating memory required by complex programs is a well-known research topic. In this work, we build a type system to statically estimate the memory bounds required by shared variables in software transactional memory (STM) programs. This work extends our previous works with additional language features such as explicitly declared shared variables, introduction of primitive types, and allowing loop body to contain any statement, not required to be well-typed as in our previous works. Also, the new type system has better compositionality compared to available type systems.

Ultra-High-Throughput Multi-Core AES Encryption Hardware Architecture

Security issues in high-speed data transfer between devices are always a big challenge. On the other hand, new data transfer standards such as IEEE P802.3bs 2017 stipulate the maximum data rate up to 400 Gbps. So, security encryptions need high throughput to meet data transfer rates and low latency to ensure the quality of services. In this paper, we propose a multi-core AES encryption hardware architecture to achieve ultra-high-throughput encryption. To reduce area cost and power consumption, these cores share the same KeyExpansion blocks. Fully parallel, outer round pipeline technique is also applied to the proposed architecture to achieve low latency encryption. The design has been modelled at RTL (Register-Transfer-Level) in VHDL and then synthesized with a CMOS 45nm technology using Synopsys Design Compiler. With 10-cores fully parallel and outer round pipeline, the implementation results show that our architecture achieves a throughput of 1 Tbps at the maximum operating frequency of 800 MHz. These results meet the speed requirements of future communication standards. In addition, our design also achieves a high power-efficiency of 2377 Gbps/W and area-efficiency of 833 Gbps/mm2, that is 2.6x and 4.5x higher than those of the other highest throughput of single-core AES, respectively.

Backstepping Control of Switched Reluctance Motor with Artificial Neural Network based Flux Estimator

The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.

The Improvement of the CurveCP Cryptography for enhancing the secure of Internet of Things

With the development of Information Technology, Internet of Things (IoT) has been applied widely in human civilization, but it leads the increase of the risk about information security. The traditional security solutions has revealed the lack of compatibility with IoT because of the difference about complex communication protocol, low energy, processing ability and limited memory. Therefore, the suitable IoT security solutions needs the balance between energy and costs as well as wide adaption for the various network protocols. Our research team, after had been under a long process of analyzing theoretical documents and operating simulated experiments, recognized that the Lightweight cryptography is the most optimal solution in new network platform generation. Our team also improved and implemented CurveCP which is one of these Lightweight cryptographies in the Wireless sensor Networks (WSN) so as to enhance data secure and information security of IoT System. This study describes briefly the improvement of CurveCP Lightweight cryptography by reducing length of cryptographic key as well as implement in IoT System. It also includes the simulation experiment, solutions evaluation, conclusion and future development.

Research and Optimize Geocoding with the User's Coordinates on VMap

In this article, we present an optimal method for searching the address on VMap based on the current location and the entire view of the users. The article will focus on understanding and researching factors to optimize user's search, enhancing the user’s experience on VMap. The article used data from OpenStreetMap, open source of Pelias geocoder to run test suites. Specifically, based on comparison results with the best rated digital map platform today, Google Maps, we calculated the effectiveness of the method. The proposed method helps to diversify search results with the user's current location, providing relevant search results in case the user's view does not have much influence on search optimization (such as user's view too large or too small). From there, as the optimal basis for searching of many other locations other than the user's current location and view, location bias priority will be enhanced. Besides, evaluation methods need to be performed on multiple users to evaluate effectiveness. This method was applied for VMap and showed optimistic results. Keywords QUERE, geocoding, user’s coordinates, digital maps, VMap, GIS References [1] The National Assembly of Vietnam, Law of Surveying and Cartography, 2018, https://thuvienphapluat.vn/van-ban/tai-nguyen-moi-truong/Luat-Do-dac-va-Ban-do-354638.aspx/, 2018 (accessed on: October 26th, 2020) (in Vietnamese). [2] Top U.S. Mapping Apps by Reach 2018, Statista, https://www.statista.com/statistics/865419/most-popular-us-mapping-apps-ranked-by-reach/, 2018 (accessed on: October 20th, 2020) (in Vietnamese). [3] Top U.S. Mapping Apps by Users 2018, Statista, https://www.statista.com/statistics/865413/most-popular-us-mapping-apps-ranked-by-audience/, 2018 (accessed on: October 26th, 2020). [4] Results of the Population and Housing Census at 0:00 on April 1, General Statistics Office of Vietnam, https://www.gso.gov.vn/du-lieu-va-so-lieu-thong-ke/2019/12/ket-qua-tong-dieu-tra-dan-so-va-nha-o-thoi-diem-0-gio-ngay-01-thang-4-nam-/, 2019 (accessed on: October 26th, 2020) (in Vietnamese). [5] Ministry of Information and Communication, Vmap: The Vietnamese Digital Map Has Officially come Into Operation (in Vietnamese). [6] Wu, X. Wang, A Geocoding Algorithm for Natural Language Address with Neighborhood Properties, in Proceedings of 2nd International Conference on Computer Science and Network Technology, ICCSNT 2012, 2012, pp. 807-810, https://doi.org/10.1109/ICCSNT.2012.6526053. [7] K. Matci, U. Avdan, Address Standardization Using the Natural Language Process for Improving Geocoding Results, Comput, Environ, Urban Syst., Vol. 70, 2018, pp. 1-8, https://doi.org/10.1016/j.compenvurbsys.2018.01.009. [8] Lambert, S. Francisco, G. Lerner, Refining Location Estimates and Reverse Geocoding Based on Auser Profile Inventors, Browsing History, May, 2010. [9] Elastic, Script score Query, Elasticsearch, Elastic, https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html/, 2018 (accessed on: October 26th, 2020) (in Vietnamese). [10] Stewart, D. Tall, The exponential function, in Complex Analysis, 2012, pp. 8294.

Automatic building of a large and complete dataset for image-based table structure recognition

Table is one of the most common ways to represent structured data in documents. Existing researches on image-based table structure recognition often rely on limited datasets with the largest amount of 3,789 human-labeled tables as ICDAR 19 Track B dataset. A recent TableBank dataset for table structures contains 145K tables, however, the tables are labeled in an HTML tag sequence format, which impedes the development of image-based recognition methods. In this paper, we propose several processing methods that automatically convert an HTML tag sequence annotation into bounding box annotation for table cells in one table image. By ensembling these methods, we could convert 42,028 tables with high correctness, which is 11 times larger than the largest existing dataset (ICDAR 19). We then demonstrate that using these bounding box annotations, a straightforward representation of objects in images, we can achieve much higher F1-scores of table structure recognition at many high IoU thresholds using only off-the-shelf deep learning models: F1-score of 0.66 compared to the state-of-the-art of 0.44 for ICDAR19 dataset. A further experiment on using explicit bounding box annotation for image-based table structure recognition results in higher accuracy (70.6%) than implicit text sequence annotation (only 33.8%). The experiments show the effectiveness of our largest-to-date dataset to open up opportunities to generalize on real-world applications. Our dataset and experimental models are publicly available at shorturl.at/hwHY3

New Feature Selection Method for Multi-channel EEG Epileptic Spike Detection System

Epilepsy is one of the most common and severe brain disorders. Electroencephalogram (EEG) is widely used in epilepsy diagnosis and treatment, with it the epileptic spikes can be observed. Tensor decomposition-based feature extraction has been proposed to facilitate automatic detection of EEG epileptic spikes. However, tensor decomposition may still result in a large number of features which are considered negligible in determining expected output performance. We proposed a new feature selection method that combines the Fisher score and p-value feature selection methods to rank the features by using the longest common sequences (LCS) to separate epileptic and non-epileptic spikes. The proposed method significantly outperformed several state-of-the-art feature selection methods.

Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

Design and Simulation of a DC Stabilization System for Solar Energy System

During the last few years, the demand for solar photovoltaic (PV) energy has grown remarkably since it provides electricity from an exhaustible and clean energy source. The generated power of solar panels depends on environment conditions, which changes continuously due to many factors, for example, the radiation, the characteristics of the load, etc. In order for the solar energy system operates at its most efficiency, it needs to work at its maximum power point (MPP). Previous literature has dealt with either investigating Maximum Power Point Tracking (MPPT) algorithms or tracking a steady output voltage from solar panels. However, when the load is changed, the new MPP need to be defined. In this paper, a novel adaptive MPPT system was proposed to investigate the MPP and keep tracking MPP at the same time. The proposed system was implemented in Proteus simulation. As the results, when the load is changing, the system obtained a steady and reliable desired output voltage. It is not only able to obtain a reliable steady DC output voltage but also keep the solar energy system work at its maximum efficiency.

A General Computational Framework for Prediction of Disease-associated Non-coding RNAs

Since last decade, we have been witnessing the raise of non-coding RNAs (ncRNAs) in biomedical research. Many ncRNAs have been identified and classified into different classes based on their length in number of base pairs (bp). In parallel, our understanding about functions of ncRNAs is gradually increased. However, only small set among tens of thousands of ncRNAs have been well studied about their functions and their roles in development of diseases. This raises a pressing need to develop computational methods to associate diseases and ncRNAs. Two most widely studied ncRNAs are microRNA (miRNA) and long non-coding RNA (lncRNA), since miRNAs are the regulators of most protein-coding genes and lncRNAs are the most ubiquitously found in mammalian. To date, many computational methods have been also proposed for prediction of disease-associated miRNAs and lncRNAs, and recently comprehensively reviewed. However, in the previous reviews, these computational methods were described separately, thus this limits our understanding about their underlying computational aspects. Therefore, in this study, we propose a general computational framework for prediction of disease-associated ncRNAs. The framework demonstrates a whole computational process from data preparation to computational models.