Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

The goal of 6G communication networks requires higher transmission speeds, tremendous data processing, and low-latency communication. Orthogonal frequency-division multiplexing (OFDM), which is widely utilized in 5G communication systems, may be a viable alternative for 6G. It significantly reduces inter symbol interference (ISI) in the frequency-selective fading environment. Channel estimation is critical in OFDM to optimize system performance. Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions. The simulation results show that the signal bit error (SER) is equivalent to and better than that of the minimum mean squared error (MMSE) and least square (LS) methods

GA-Based Optimization for Multivariable Level Control System: A Case Study of Multi-Tank System

This paper presents a systematic way to determine the trade-off optimized controller tunings using computation optimization technique for both servo and regulatory controls of the Multi-Tank System, as one of the applications under the multivariable loop principle. The paper describes an improved way to obtain the best Proportional-Integral (PI) controller tunings in reducing the dependency on engineering knowledge, practical experiences and complex mathematical calculations. Relative Gain Array (RGA) calculation justified the degree of relation and the best pairing for both interacted control loops. Genetic Algorithm (GA), as one of the most prestigious techniques, was used to analyze the best controller tunings based on factor parameters of iterations, populations and mutation rates to the applied First Order plus Dead Time (FOPDT) models in the multivariable loop. Amid simulation analysis, GA analysis’s reliability was justified by comparing its performance with the Particle Swarm Optimization (PSO) analysis. The research outcome was visualized by generating the process responses from the LOOP-PRO’s multi-tank function, whereby the GA tunings’ responses were compared with the conventional tuning methods. In conclusion, the result exhibits that the GA optimization analysis has successfully demonstrated the most satisfactory performance for both servo and regulatory controls

Exploiting LDPC Codes For Improving The Performance of Clipped-OFDM System

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier transmission technique that becomes the best choice in wireless high-data-rate transmission. The drawbacks of OFDM are high Peak-to-Average Power Ratio (PAPR) and sensitivity to frequency offset. High PAPR decreases the amplifier’s efficiency. The simplest PAPR reduction method is clipping, but it gives in-band and out-of-band distortion that degrades the performance of the system. There are various types of clipping, such as classical clipping, deep clipping, and smooth clipping. This paper analyses the use of low-density parity-check (LDPC) codes as an error correction coding (ECC) for those various types of clipping. The simulation results show that classical clipping gives the best performance in PAPR reduction and error probability

Analyzing factors influencing global precious metal markets: A feature selection study

Precious metals are valuable commodities providing superior protection against risky financial exposure. Identifying factors influencing the market is crucial for anticipating changes. Forecast applications utilize stochastic models capable of learning from historical data to project future values. The dataset is a vital component for prediction tools since all estimations begin with constructing the appropriate information. Detecting the association between input and output is essential to filter data, as including unrelated variables could destabilize the response. Feature selection considers removing uncorrelated attributes before incorporating them as inputs to the predictor. This study employs three regression-based algorithms to examine 58 precious assets from gold, silver, platinum, and palladium markets against several variables cited in the literature. Relationships were detected using regressive feature selection methods, known as least absolute shrinkage and selection operator (LASSO), ridge, and elastic net (EN). Results demonstrate that the proposed algorithms achieved satisfactory performance on 42 assets, justified through a reliable fit and acceptable error. The remaining 16 assets exhibited large deviations with considerably poor regression quality, indicating considerable nonlinearity. Attributes were selected with a detailed emphasis on those exerting the most substantial impact on a particular metal. Based on computational analysis, most investments are susceptible to macroeconomic factors. Some assets may present hedging capabilities towards key features, including stock index, exchange rates, and bond yield. An assessment of common variables among each metal revealed that real GDP growth and interest rates are vital indicators for the precious metal market. Overall, the simulation outcomes show no consistent commonalities amongst attributes within the same asset class in a country. Feature selection from this research offers necessary information regarding time-series dynamics, serving as a basis to project trends. The filtered dataset is expected to enhance the reliability of nonlinear predictive algorithms by removing inaccurate correlations to lower computational load. Furthermore, the outcome provides information regarding correlations affecting global precious metal investments over five-year period. These discussions are necessary for investors considering such commodities as potential portfolio diversifiers

IMPLEMENTATION AND ANALYSIS OF THE INTERNET OF THINGS SYSTEM FOR ELECTRICAL ENERGY MONITORING AT INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Measurement of electric power usage is carried out using simple measuring instruments and the recording is still manual so that the data obtained is not real-time and accurate. This research aims to implement an electrical energy monitoring system using the Internet of Things (IoT) to obtain real-time information related to electrical energy in the education industry. This research uses an Industrial Grade Power Meter to get a more accurate measurement value. To connect the Power Meter device with the IoT system, this research uses Modbus RS485 communication and a mini PC to process data from the meter, so that the data can be sent to a server using the MQTT communication protocol, and displayed on the Dashboard. The test results of this study indicate that the monitoring system can be implemented and the system runs well with end-to-end measurement results. From the measurement results, the current value (3 phase average) has an average deviation of 0.001 Amperes, Voltage (3 phase average) has an average deviation of 0.519 V, Power factor has an average deviation of 0.012, Active power has a deviation average of 0.000 kW, reactive power with an average deviation of 0.000 kVAR, apparent power with an average deviation of 0.000 kVA and frequency with an average deviation of 0.124 Hz. Then the MQTT protocol has a quality of service with index 4 based on TIPHON standardization on delay, throughput, and packet loss parameters, and index 3 based on TIPHON standardization on jitter parameters

Portable Micro-Doppler Radar with Quadrature Radar Architecture for Non-Contact Human Breath Detection

Recently, rapid advances in radio detection and ranging (radar) technology applications have been implemented in various fields. In particular, micro-Doppler radar has been widely developed to perform certain tasks, such as detection of buried victims in natural disaster, drone system detection, and classification of humans and animals. Further, micro-Doppler radar can also be implemented in medical applications for remote monitoring and examination. This paper proposes a human respiration rate detection system using micro-Doppler radar with quadrature architecture in the industrial, scientific, and medical (ISM) frequency of 5.8 GHz. We use a mathematical model of human breathing to further explore any insights into signal processes in the radar. The experimental system is designed using the USRP B200 mini-module as the main component of the radar and the Vivaldi antennas working at 5.8 GHz. The radar system is integrated directly with the GNU Radio Companion software as the processing part. Using a frequency of 5.8 GHz and USRP output power of 0.33 mW, our proposed method was able to detect the respiration rate at a distance of 2 m or less with acceptable error. In addition, the radar system could differentiate different frequency rates for different targets, demonstrating that it is highly sensitive. We also emphasize that the designed radar system can be used as a portable device which offers flexibility to be used anytime and anywhere

Diabetic Retinopathy Detection and Grading: A Transfer Learning Approach Using Simultaneous Parameter Optimization and Feature-Weighted ECOC Ensemble

Early detection of Diabetic Retinopathy (DR) is crucial as it may cause blindness. Manual diagnosis of DR severity by ophthalmologists is challenging and time consuming. Therefore, there has been a significant focus on developing an automated system for identifying DR using retinal fundus images. Recent research has revealed that utilizing pre-trained deep learning networks for diverse image classification tasks provides notable benefits in this context. In this paper, a Transfer Learning (TL) approach with optimized feature weights and parameters is proposed for DR detection and grading tasks. To obtain better generalization during training and to optimize classification, features are extracted from the average pooling layers and fed to an Error Correction Output Code (ECOC) ensemble configuration. Two pre-trained networks (ShuffleNet and ResNet-18) are considered as each pre-trained network offers a different “point of view” of the fundus images, thereby providing more opportunities for accurate “grade-wise” discrimination. A simultaneous feature selection and parameter tuning of the ensemble is applied to further enhance the overall DR detection and grading. Adaptive Differential Evolution (ADE) is chosen for this purpose because it automatically configures the parameters, eliminating the need for manual parameter selection. In this paper, we evaluate two public domain datasets: 1) APTOS and 2) combination of EyePac + Messidor-2. Simulation results show that our proposed method performs better that the conventional deep learning models and are on a par with the existing research work. In particular, the optimal configuration for APTOS 5-class DR grading achieves an accuracy rate of 82%, while for APTOS 2-class grading, it achieves a higher accuracy rate of 96%. Finally, the best configuration for EyePac + Messidor-2 3-class grading results in 75% accuracy

A Novel Differentially Fed Compact Dual-Band Implantable Antenna for Biotelemetry Applications

© 2016 IEEE. A novel differentially fed dual-band planar antenna operating at the Medical Implant Communication Service band (402-405 MHz) and the Industrial, Scientific, and Medical band (2400-2480 MHz) is presented. The measured 10-dB differential reflection coefficient bandwidth is 389-419 MHz (7.4%) at the lower band and 2395-2563 MHz (6.6%) at the upper band, respectively. With the use of symmetric meandered strip and shorting pin, a differentially fed compact dual-band design is obtained, where the volume of the prototype is only 642.62 mm3 (22 mm × 23 mm × 1.27 mm). Due to its small size and dual-band operation, the proposed antenna can be connected to differential circuits in implantable biotelemetric devices

Rectangular Microstrip Array Feed Antenna for C-Band Satellite Communications: Preliminary Results

This paper proposes a rectangular array configuration of microstrip antennas combined with a parabolic reflector for C-band satellite communications. The antenna operates in the frequency range of 3.8–4.2 GHz. In particular, the proposed antenna is a 2 × 2 feed antenna on a parabolic system. It uses a multilayer microstrip array antenna with proximity coupling and coaxial probe techniques as a feeding technique. The fabricated antenna operates at 3.8–4.4 GHz and 12.1 dBi gain at frequency 4.148 GHz. Through simulation, combining the antenna with a 2.4 m parabolic reflector results in a gain of 33.1 dBi. In conclusion, the proposed antenna configuration achieves the expected high gain and narrow beamwidth for the E plane and the H plane