CAN Tx Frame Implementation using Verilog

This article discusses the concept of CAN protocol and its implementation in verilog language. Initially the CAN protocol description is given in brief with the block diagram, later its design, implementation in verilog code is presented. The CAN transmission (Tx) data Frame is realized using verilog code, this is achieved by defining individual sub-blocks verilog codes and combining these to get the CAN transmission of data frame. In the year 1986, CAN data link layer protocol was introduced in SAE conference. In 1993, CAN protocol and high speed physical layer were internationally accredited as ISO 11898. As on today it has 11898-1 to 4 standard documents. The CAN 1.0, 2.0 versions were initially had fixed data rate for the entire frame. In 2012, CAN-FD (Flexible data rate) protocol was introduced. This will allow data phase a second higher bit rate, along with this restriction of 8 bytes is extended up to 64 bytes.In this paper CAN Tx data frame is realized using Xilinx 14.7 version using verilog language.

Joint Channel Tracking and Data Detection for Massive MIMO Uplink Over Time-Varying Channel

In this letter, we propose a novel joint channel estimation and data detection scheme for massive multiple-input multiple-output (MIMO) uplink, which operates in a time-varying fading channel. More specifically, at the receiver of the proposed scheme, a data frame is divided into multiple blocks, and in each block, a demodulated data block is used for updating channel state information (CSI) in an iterative manner. Furthermore, the initial CSI in the block of interest is given by the estimated CSI in the previous block, hence allowing accurate tracking of CSI in a time-varying channel without imposing additional pilot insertion inside the data frame. Since the length of the divided blocks affects both the achievable channel tracking and data detection performances, it is optimized so as to maximize the discrete-input continuous-output memoryless channel's (DCMC) capacity derived in this letter. It is demonstrated that the DCMC capacity of the proposed scheme is capable of nearly achieving those of the perfect CSI counterpart without imposing any substantial pilot overhead.

Joint Channel Tracking and Data Detection for Massive MIMO Uplink Over Time-Varying Channel

In this letter, we propose a novel joint channel estimation and data detection scheme for massive multiple-input multiple-output (MIMO) uplink, which operates in a time-varying fading channel. More specifically, at the receiver of the proposed scheme, a data frame is divided into multiple blocks, and in each block, a demodulated data block is used for updating channel state information (CSI) in an iterative manner. Furthermore, the initial CSI in the block of interest is given by the estimated CSI in the previous block, hence allowing accurate tracking of CSI in a time-varying channel without imposing additional pilot insertion inside the data frame. Since the length of the divided blocks affects both the achievable channel tracking and data detection performances, it is optimized so as to maximize the discrete-input continuous-output memoryless channel's (DCMC) capacity derived in this letter. It is demonstrated that the DCMC capacity of the proposed scheme is capable of nearly achieving those of the perfect CSI counterpart without imposing any substantial pilot overhead.

Development of a Model and Algorithm for Data Aggregation and Classification for a Personalized Nutrition Recommendation System

The article demonstrates the design and implementation of a data aggregation algorithm for a future recommendation system in the field of personalized nutrition. It was based on theoretical materials on machine learning methods in natural language processing, as well as tutorials on building classification models using the Keras library. A distinctive feature of the classifier implemented within the framework of this project is the fact that it simultaneously accepts images and text data as input to obtain more accurate and balanced predictions. The implementation of the designed data aggregation algorithm for the recommendation system in the field of personalized nutrition is considered in detail. A review was made of the tools and approaches chosen at various stages of aggregation. The metrics for evaluating the predictions of the implemented model for the classification of geographic labels, as well as the analysis of the average sentiment of user reviews are determined and the results are visualized. Predicted geo tags and revealed comment sentiments were added to the main data frame as additional features.

DG-LoRa: Deterministic Group Acknowledgment Transmissions in LoRa Networks for Industrial IoT Applications

In this paper, we propose a novel MAC protocol, called DG-LoRa, for improving scalability in low power wide area networks. DG-LoRa is backward compatible with legacy LoRaWAN and adds new features, such as group acknowledgment transmissions in the time-synchronized frame structure that supports determinism on channel access. In DG-LoRa, the number of responses to data frames that are transmitted from end devices is maximized by allocating the spreading factor and timeslot in the frame structure. We evaluate the performance of DG-LoRa using the Monte-Carlo simulation and then compare it with the performance of legacy LoRaWAN in terms of data drop rate and the number of retransmissions. Our numerical results show that DG-LoRa supports approximately five times more connections to the LoRa network satisfying a 5% data drop rate. Also, it is observed that DG-LoRa enables low overhead by reducing the number of data frame retransmissions.

KAJIAN PEMANFAATAN DATA GOOGLE MAPS DALAM OFFICIAL STATISTICS

Publikasi statistik usaha penyediaan makan minum yang diterbitkan oleh BPS tidak bisa memfasilitasi pebisnis dalam mengidentifikasikan daerah yang berpotensi memiliki kemampuan untuk dikembangkan usaha pada sektor penyediaan makan dan minum. Selain itu, adanya keterbatasan waktu, biaya, dan tenaga dalam pengumpulan data oleh Subdirektorat Pariwisata BPS pada survei VREST sehingga, menyebabkan statistik penyediaan makan minum tidak bisa di terbitkan sesuai metodologi yaitu setiap tahun. Penelitian ini memanfaatkan metode web scraping untuk mendapatkan data usaha penyedia makan minum dari situs web google maps. Jumlah data yang terkumpul sebanyak 34.526 usaha penyedia makan minum di Pulau Jawa dan Bali. Hasil nilai pencocokan data hasil web scraping dengan data frame BPS menunjukkan persentase kemiripan (match) sebesar 68,22%. Provinsi Bali adalah daerah yang memiliki potensi untuk mengembangkan usaha penyediaan makanan minuman terkhusus pada Kota/Kabupaten Jembrana, Buleleng, Tabanan, Karangasem, dan Klungkung. Sedangkan, provinsi Jawa Tengah adalah daerah yang memiliki potensi untuk mengembangkan usaha akomodasi terkhusus pada Kota/Kabupaten Cilacap, Blora, Grobogan, Batang, dan Kendal.

An interactive visualization of Google Books Ngrams with R and Shiny: Exploring a(n) historical increase in onset strength in a(n) huge database

International audience Using the re-emergence of the /h/ onset from Early Modern to Present-Day English as a case study, we illustrate the making and the functions of a purpose-built web application named (an:a) lyzer for the interactive visualization of the raw n-gram data provided by Google Books Ngrams (GBN). The database has been compiled from the full text of over 4.5 million books in English, totalling over 468 billion words and covering roughly five centuries. We focus on bigrams consisting of words beginning with graphic <h> preceded by the indefinite article allomorphs a and an, which serve as a diagnostic of the consonantal strength of the initial /h/. The sheer size of this database affords us the possibility to attain a maximal diachronic resolution, to distinguish highly specific groups of <h>-initial lexical items, and even to trace the diffusion of the observed changes across individual lexical units. The functions programmed into the app enable us to explore the data interactively by filtering, selecting and viewing them according to various parameters that were manually annotated into the data frame. We also discuss limitations of the database, of the app and of the explorative data analysis. The app is publicly accessible online at https://osf.io/ht8se/.

Analysis of Throughput and Delay for an Underwater Multi-DATA Train Protocol with Multi-RTS Reception and Block ACK

We propose an underwater multi-DATA train protocol with multi-RTS reception and block ACK (BACK) for underwater acoustic sensor networks. Due to long underwater acoustic propagation delay, some RTS frames may not overlap at a sink node, even if the RTS frames were sent to the sink node simultaneously by different sensor nodes. We consider that our underwater sink node can recover these nonoverlapping RTS frames. Since our RTS frame contains ID of the RTS sending node and a timestamp, the sink node calculates the propagation delay between the RTS sending node and the sink node, then broadcasts a CTS frame. Since our CTS frame contains when each RTS sending node can transmit a DATA frame to the sink node, multiple DATA frames transmitted by different sensor nodes can be formed as a train at the sink node. We also propose an underwater BACK protocol which is analogous to our proposed underwater multi-DATA train protocol. We analyze normalized throughput and mean access delay of our proposed protocols and the conventional protocols. The analytical and simulation results show that our analysis is accurate and our proposed protocols outperform the conventional protocols. Our proposed protocol can shorten the delay and increase the throughput via the multi-DATA train, multi-RTS reception, and BACK.