1,720,964 research outputs found
SMIMO Radar: MIMO Radar with Subarray Elements of Phased-Array Antenna
Full text linkUnlike Phased-MIMO Radar (PMIMO) which employs overlapping equal subarrays (OES) only on the transmit (Tx), Subarray-MIMO (SMIMO) radar utilizes the combination of subarrays, both in the transmit (Tx) and receive (Rx). SMIMO radar is MIMO radar with subarray elements acting as Phased-Array (PA). It simultaneously combines the primary advantages of PA and the MIMO radar; they are high directional gain and high diversity gain, respectively. High directional gain is beneficial to improve the range target, while high diversity gain is beneficial to improve the number of target detection. The use of the subarray methods in the Tx-Rx array could be configured such as in verlapping subarray (OS), non-overlapping subarray (NOS), equal subarray (ES), unequal subarray (US), and/or the combination of all configurations. Various configurations in Tr-Rx would determine the performance of radar, such as the number of virtual arrays, the maximum number of target detections, the detection accuracies, and the angular resolutions along with its effectivity compared to PA, MIMO, and Phased-MIMO radar. Numerical results and simulation showed that SMIMO provided higher flexibility than other radars by configuring Tx-Rx to easily adapt to various changes of target conditions and their surroundings
FPMIMO: Radar MIMO dengan Subarray-Subarray Non-Koheren
No full textTidak seperti radar Phased Array (PA) yang memberikan array gain proporsional dengan jumlah elemen-elemen antena, radar MIMO memberikan perbaikan terhadap estimasi parameter, deteksi target, dll. Terkini, kombinasi dari konsep radar PA dan MIMO yaitu radar Phased MIMO (PMIMO) menggunakan overlapping subarray pada array transmit yang bertujuan untuk mengeksploitasi kelebihan utama dari radar PA (gain koheren) dan kelebihan utama radar MIMO (diversitas waveform). Pendekatan radar Full PMIMO (FPMIMO) yang digunakan pada penelitian ini (evolusi lanjut dari radar PMIMO) yaitu mengimplementasikan overlapping subarray pada kedua sisi array baik di transmit maupun di receive sehingga kombinasi-kombinasi berbagai variasi waveform dapat secara simultan dikonfigurasikan untuk meningkatkan gain koheren dan diversitas waveform. Peningkatan pada kedua gain tersebut membentuk kinerja radar yang lebih robust melawan noise dan interferensi pada target yang dituju. Parameter-parameter kinerja radar FPMIMO yang diperhitungkan seperti transmit-receive (T-R) gain, SINR (Signal-to-Interference-plus-Noise-Power Ratio), maximum range, identifiabilitas parameter (jumlah maksimum target terdeteksi dari virtual array), deteksi target (probabilitas deteksi, probabilitas false alarm, dll), dan fungsi ambiguitas (yang mendeskripsikan resolusi range dan/atau frekuensi Doppler) telah diformulasikan dan keefektivitasannya juga telah dibandingkan dengan jenis-jenis radar lain seperti radar PA, MIMO, dan PMIMO. Hasil-hasil evaluasi numerik menunjukkan bahwa radar FPMIMO, membentuk general form dari radar multi-antena, secara fleksibel mampu mengkompromikan antara jumlah subarray pada Tx-Rx array untuk memperoleh kinerja-kinerja radar yang adaptif, fleksibel, dan terprogram dengan radar PA, MIMO, dan PMIMO merupakan kondisi khususnya. Dengan demikian radar FPMIMO memiliki keunggulan fleksibilitas tinggi terhadap berbagai kondisi target seperti contoh desain radar untuk aplikasi radar burung dan radar kendaraan yang memenuhi mission requirement tertentu. Pengaruh penggunaan elemen realistik seperti elemen antena tipe-cos(θ) juga turut disajikan.
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Unlike the phased array (PA) radar that achieves array gain in proportion to the number of elements, the MIMO radar provides improvement to parameter estimation, target detection, and so on. Recently, the combination of the PA and MIMO concepts into the Phased MIMO (PMIMO) radar uses overlapping subarrays on transmit array that aim to exploit the main advantage of the Phased Array radar (PA) i.e. coherent gain, and the main advantage of the MIMO radar i.e. waveform diversity. The Full PMIMO radar approach used in this research, the future evolution of the PMIMO radar, is to implement overlapping subarrays on both sides of the arrays, namely transmit and receive so that combinations of various waveforms can be simultaneously configured to increase coherent gain and waveform diversity. To increase in both gains provides a robust of radar performance against noise and interference in the desired target. The radar performance in terms of the transmit-receive (T-R) gain, the SINR (Signal-to-Interference-plus-Noise-Power Ratio), the maximum range, the parameter identifiability i.e. the maximum number of detectable target and virtual array, the target detection i.e. probability of detection, probability of false alarm, etc, and the ambiguity function i.e. the function indicates the range resolution and the Doppler shift, has been formulated and its effectiveness has been compared to PA, MIMO, and PMIMO radar. The numerical evaluation results demonstrate that, being the more general form of multi-antenna radars, the FPMIMO radar is able to flexibly compromise the number of subarrays in the Tx-Rx array to obtain high radar performance, adaptable, flexible, and programmable where the PA, MIMO, and PMIMO radar are special conditions. Thus the FPMIMO radar has advantages of high flexibility so that they are easily adapted to various target conditions such us radar design example for bird and vechicular radar applications by meeting certain of mission requirements. The impact of the use of cos(θ)-type elements is also presented
Optimized Suitable Propagation Model for GSM 900 Path Loss Prediction
Get PDFThis paper present how COST-231 Hata model is chosen and optimizedfor path loss prediction in suburban area of Tarakan, Indonesia in the GSM 900 MHz system.Thispredicted and optimized path loss model is based on the empirical measurement collected in the GSM system on Tarakan City. It is developed by comparing the calculatedpath loss from collected measurements with the well-known path loss models within applicable frequency range of GSM system, such as COST-231 Hata, Ericsson, SUI, Walfish, ECC-33, and Lee Model. The COST-231 Hata model was chosen based on the closest and smallest mean error ascompared to the measured path loss. This optimized COST-231 Hata model is implemented in the path loss predictionduring the validation process. Thus, this optimized model is successfully improved and would be more reliableto be applied in the TarakanGSM900 MHz system for path loss prediction. DOI: http://dx.doi.org/10.11591/telkomnika.v14i1.7470
Range and Velocity Resolution of Linear- Frequency-Modulated Signals on Subarray-Mimo Radar
Full text linkThe most important radar system performance is determining the range-velocity of the detected target. This performance is obtained from processing an ambiguity-function (AF) between signals from target reflections and radar radiation signals. Selection of the appropriate waveform transmitted by the radar is a key factor in supporting high resolution radar performance in the AF. There are many waveforms that have been studied in radar systems, especially for multi-antenna radars, i.e., subarray-MIMO (SMIMO) radar which can form phased array (PA) and MIMO radars simultaneously, in the form of linear-frequency-modulated (LFM) signals. In this paper, we examine the use of LFM waveforms combined with SMIMO radar to produce plots of three-dimensional AF as a function of time delay and Doppler shift. The results of the comparison with the Hadamard signal determine the effectiveness of the observed AF performance on parameters such as magnitude, range-velocity resolution, peak sidelobe level ratio, and integrated sidelobe ratio by taking into account the factors of the number of Tx antennas on the PA radar and the number of Tx subarrays on the MIMO radar. The evaluation results of the SMIMO radar configuration (M = 6) with the number of Tx-Rx antenna elements the being 8 provide the best mainlobe magnitude, sidelobe magnitude, range resolution, velocity resolution, PSLR, and ISLR of AF LFM signals compared to conventional radars are 235.2dB, 7.54dB, 37.5m, 75km/s, 29.89dB, and 29.8dB, respectively. Meanwhile, the LFM signal is far superior to the Hadamard signal which has PSLR and ISLR 1.16dB and -3.36dB, respectively
Optimized Suitable Propagation Model for GSM 900 Path Loss Prediction
No full textThis paper present how COST-231 Hata model is chosen and optimizedfor path loss prediction in suburban area of Tarakan, Indonesia in the GSM 900 MHz system.Thispredicted and optimized path loss model is based on the empirical measurement collected in the GSM system on Tarakan City. It is developed by comparing the calculatedpath loss from collected measurements with the well-known path loss models within applicable frequency range of GSM system, such as COST-231 Hata, Ericsson, SUI, Walfish, ECC-33, and Lee Model. The COST-231 Hata model was chosen based on the closest and smallest mean error ascompared to the measured path loss. This optimized COST-231 Hata model is implemented in the path loss predictionduring the validation process. Thus, this optimized model is successfully improved and would be more reliableto be applied in the TarakanGSM900 MHz system for path loss prediction. DOI: http://dx.doi.org/10.11591/telkomnika.v14i1.7470
Metode k-means clustering dan morfologi berbasis computer vision dan analisis regresi untuk aplikasi sistem grading udang Vaname
Full text linkPenentuan mutu udang secara konvensional menggunakan visual mata memiliki beberapa kelemahan, salah satunya adalah tingkat persepsi manusia yang berbeda-beda. Solusi yang ditawarkan adalah menggunakan teknologi computer vision dalam menentukan ukuran udang berdasarkan citra yang ditangkap kamera. Penelitian ini bertujuan untuk menerapkan kombinasi metode dari pengelompokan k-rata-rata dan morfologi untuk menentukan ukuran udang Vaname yaitu berdasarkan perbandingan area dalam dimensi piksel dari hasil olah citra dengan teknologi computer vision terhadap massa dalam dimensi gram. Analisis regresi digunakan untuk mendapatkan persamaan yang mengkonversi antar nilai piksel tersebut ke dalam massa udang. Keefektifan kombinasi metode ini dibandingkan dengan hanya menggunakan metode pengelompokkan k-rata-rata dan nilai ambang. Hasil evaluasi dari metode yang diusulkan menunjukkan bahwa nilai RMSE yang diperoleh sebesar 0,68 yang lebih baik dari dua metode terdahulu berturut-turut adalah 0,73 dan 2,89. Sementara akurasi sistemnya untuk pengukuran massa diperoleh sebesar 93,64%, akurasi ukuran/besar sebesar 93,37% dan akurasi klaster dari ukuran sebesar 95,45%
Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis
Full text linkPixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively
Optimizing 2.4GHz Wireless Networks in Shrimp Ponds with Particle Swarm Optimization
Full text linkThis paper focuses on enhancing wireless sensor networks (WSNs) for monitoring water quality in aquaculture, specifically shrimp ponds, by improving pathloss (PL) models. Radio wave propagation in such environments is challenging due to unpredictable signal attenuation caused by factors like distance, antenna height, terrain, vegetation, and weather conditions. Reliable PL modeling is essential for optimizing network performance. The research evaluates the performance of theoretical PL models, including ITU, Fitting-ITU (FITU), and Weissberger, by comparing their predictions with actual 2.4GHz radio frequency (RF) measurements. Statistical metrics such as root-mean-square error (RMSE) and the coefficient of determination (R²) were used to assess model accuracy. Initial results showed significant discrepancies, with an average RMSE of 28.7dB and an R² of only 5%. To address these issues, the study employed modification techniques (quadratic and cubic polynomial adjustments) and optimization methods, particularly particle swarm optimization (PSO). These approaches refined the theoretical models, aligning them more closely with real-world data. The optimized PSO model reduced the RMSE to 8.34dB and further to 1.89dB, while improving R² from 5% to 95.6%, demonstrating a near-perfect fit. This study highlights the critical role of PSO and similar techniques in bridging the gap between theoretical predictions and practical applications, ensuring more reliable WSN performance in aquaculture environments. The findings contribute to the development of robust, high-accuracy models tailored to the unique challenges of aquaculture settings
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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