Data Transmission through Li-Fi in Underwater

The emerging optical wireless communication techniques have offered high data rates in Gbps and visible light promises low attenuation of signal strength which provides high data density. The proposed method deals with the transmission of data underwater through visible light communication. The proposed method designs data transmission model where it transmits text, audio, image through water. The hardware used in this model are Arduino Nano and the transmitter part in the model is the laser light, whereas the receiver part is made of laser receiver. The transmitter follows On Off Keying (OOK) modulation technique where the blinking of laser on determine1’s and off as 0’s in this way the data is transmitted via line of sight to the receiver underwater. Li-Fi implementation can be executed to achieve rapid information move. In future, the capacity can be increased as per the requirement to transmit high quality image audio using higher rage lasers and photodiodes

Visualization and Geo-Mapping of Philippine Fire Incidents

A fire incident is one of the most catastrophic calamity bringing injuries, loss of properties, and casualties. In the Philippines, a rapid increase in fire incidents was recorded from 2013 to 2016. In this paper, we explore the importance of data visualization and analysis in extracting useful information that might help in planning and decision-making. This study used the 2012–2016 Philippine Nationwide Fire Incident Statistics data provided by the Bureau of Fire Protection through Open Data Philippines. Data cleaning and reprocessing were conducted to develop a dynamic system known as FireStatPH using Flask. Different data visualization techniques such as choropleth map were also used in the study to represent each dataset. FireStatPH can easily identify the cities and municipalities with high fire incidents, injuries, deaths, and damages. It also provides fast statistics based on fire incident data. The system contributes to the planning and decision-making process in eschewing fire incidents in the Philippines

Face RGB-D Data Acquisition System Architecture for 3D Face Identification Technology

The three-dimensional approach in face identification technology had gained prominent significance as the state-of-the-art breakthrough due to its ability to address the currently developing issues of identification technology (illumination, deformation and pose variance). Consequently, this trend is also followed by rapid development of the three-dimensional face identification architectures in which some of them, namely Microsoft Kinect and Intel RealSense, have become somewhat today's standard because of its popularity. However, these architectures may not be the most accessible to all due to its limited customisation nature being a commercial product. This research aims to propose an architecture as an alternative to the pre-existing ones which allows user to fully customise the RGB-D data by involving open source components, and serving as a less power demanding architecture. The architecture integrates Microsoft LifeCam and Structure Sensor as the input components and other open source libraries which are OpenCV and Point Cloud Library (PCL). The result shows that the proposed architecture can successfully perform the intended tasks such as extracting face RGB-D data and selecting out region of interest in the face area

Performance Analysis of Noise Subspace-based Narrowband Direction-of-Arrival (DOA) Estimation Algorithms on CPU and GPU

High-performance computing of array signal processing problems is a critical task as real-time system performance is required for many applications. Noise subspace-based Direction-of-Arrival (DOA) estimation algorithms are popular in the literature since they provide higher angular resolution and higher robustness. In this study, we investigate various optimization strategies for high-performance DOA estimation on GPU and comparatively analyze alternative implementations (MATLAB, C/C++ and CUDA). Experiments show that up to 3.1x speedup can be achieved on GPU compared to the baseline multi-threaded CPU implementation. The source code is publicly available at the following link: https://github.com/erayhamza/NssDOACud

Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Technology has turned into a significant differentiator in the money and traditional recordkeeping systems for the financial industry. To depict two customers as potential investors, it is mandatory to give the complex innovation that they anticipate and urge to purchase. In any case, it is difficult to keep on top of and be a specialist in each of the new advancements that are accessible. By reappropriating IT administrations, monetary administrations firms can acquire prompt admittance to the most recent ability and direction. Financial systems, along with machine learning (ML) algorithms, are vital for critical concerns like secure financial transactions and automated trading. These are the key to the provision of financial decisions for investors and stakeholders for the firms which are working with the trade credit (TC) approach, in Small and Medium Industries (SMEs). Huge and very sensitive data is processed in a limited time. The trade credit is a reason for more financial gains. The impact of TC with predictive machine learning algorithms is the reason why intelligent and safe revenue generation is the main target of the proposed study. That is, the combination of financial data and technology (FinTech) domains is a potential reason for sales growth and ultimately more profit.publishedVersio

KINERJA TEKNIK PREDISTORSI PADA SISTEM KOMUNIKASI KOOPERATIF DUAL-HOP KANAL TAK SIMETRIS

Pada makalah ini akan dilakukan evaluasi penerapan teknik predistorsi baik di sumber dan di relay pada sistem transmisi kooperatif dengan protokol amplify and forward (AF). Model predistorster dan penguat daya tinggi (HPA) di sumber adalah kaskade Wiener-Hammerstein, sedang model predistorter dan HPA di relay adalah inverse Rapp dan model Rapp. Penerapan teknik predistorsi tersebut diterapkan pada kondisi kanal tak simteris, model Ricean fading dan Rayleigh fading. Evaluasi terhadap kinerja teknik predistorsi ditunjukkan dengan nilai bit error rate (BER) di tujuan. Dari hasil simulasi dapat ditunjukkan bahwa penerapan teknik predistorsi di sumber dan di relay menghasilkan kinerja paling baik. Dan penerapan pada kanal  tak simteris hasil simulasi menunjukkan bahwa kondisi kanal jalur relay-tujuan (R-D) dan sumber-tujuan (S-D) memiliki pengaruh besar terhadap sinyal yang diterima di tujuan, memberikan kinerja paling baik bila model kanal-kanal tersebut bersifat Ricean fading

Modulation recognition of low-SNR UAV radar signals based on bispectral slices and GA-BP neural network

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201