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Irregular repetition slotted ALOHA (IRSA) based non-orthogonal multiple access (NOMA) with priority adaptive traffic load (ATL) (PATL-IRSA-NOMA) is proposed to prioritize the performance of emergency devices in 6G enabled massive internet of things. In PATL-IRSA-NOMA, emergency devices (ED) and regular devices (RD) are coexisting and differentiated by access probability before sending packets, where RD performing traffic load estimation and applying ATL to access the channel. Different from conventional ATL that is performed by all users/devices, priority ATL in PATL-IRSA-NOMA is only performed by RD and reduces the contention of RD in IRSA-NOMA channel. The superior of PATL-IRSA-NOMA over conventional IRSA-NOMA is shown in terms of packet loss rate and throughput for both ED and RD groups.

Computed tomography (CT) in medical is an imaging procedure employed to generate detailed images of bones, soft tissue, internal organs, and blood vessels. However, prolonged acquisition time is yet a bottleneck that can lead to patient discomfort in addition to the cost constrain and exposure to X-rays used by CT. In medical imaging technologies and implementations, effective sampling and transmission techniques are some of the main areas of study to overcome such problems. To fulfill this requirement, the compressive sensing (CS) technique was introduced demonstrating that such compression is possible and can be accomplished throughout the process of data restoration; and that the uncompressed frames can be recovered employing a scalable approach of computational optimization. Sparsity averaging reweighted analysis (SARA) was proposed in compressed imaging, exploiting multi-basis sparsity with averaging approach and basis pursuit denoise (BPDN) with high signal to noise (SNR) results. In SARA, the processing time is not considered due to the high processing time because of iteration in the reweighted process and it is not feasible for the medical image that needs fast processing with high SNR result. To fulfill this gap, this paper proposes total variation based average sparsity model with reweighted analysis for CT imaging. The SNR, structural similarity index (SSIM), and processing time are used as performance metrics for the comparison of the proposed and existing techniques. From detailed experimental results, the proposed technique outperforms the existing CS techniques and is considered as a feasible solution for compressed sensing (CS) based CT images compression with faster delay process and better visual quality for medical images.

This paper proposes coordinated beamforming (CB) that works over non-orthogonal multiple access (NOMA) scheme integrated with spatial modulation (SM), termed as CB NOMA-SM, to improve capacity and user fairness. User capacity, ergodic sum capacity (ESC), and user fairness of K number of coordinated base stations (BSs) are simulated and analyzed by comparing them with joint transmission coordinated multi-point based NOMA with SM (JT-CoMP NOMA-SM), NOMA, and orthogonal multiple access (OMA). The results show that the proposed system outperforms the other schemes both in ESC and user fairness due to the enhancing CEU while CCU capacity still can be maintained.

Reconfigurable intelligent surface (RIS) as a supportive technology for aiding downlink non-orthogonal multiple access (NOMA) can enhance the bit error rate (BER) performance. In this paper, a novel BER-aware reflecting elements allocation (REA) on an RIS is proposed to maintain the BER order among paired RIS-NOMA users. The RIS REA is useful for minimizing the average user BER, ompared with a system that allocates the same number of elements to all users. Additionally, the Ricean fading is considered instead of Rayleigh fading as it is more practical and general. Furthermore,an REA optimization objective function for equalizing the user BER is proposed. In order to solve the problem, a modified exhaustive search is proposed to reduce complexity. The distribution of the objective function is observed first; subsequently, the exhaustive search range is determined. Both the analytical and simulation results show that the proposed algorithm can minimize the average user BER.

Communication technology, multimedia, copyright protection, content data have gained attention in recent years. In addition, privacy and confidentiality are also major challenges in handling. A robust hybrid based on speeded-up robust features (SURF), discrete cosine transform (DCT), singular value decomposition or SVD, and chaotic (Arnold's Cat Map) scheme is proposed in this paper. The use of chaotic maps is for watermarking medical images, which can provide protection and security on medical images. In the watermark image, a method is applied that will increase the security of the watermark image, namely Arnold's Cat Maps. SVD method is used to decompose input data into three submatrices. To produce a watermarked image by combining the watermark image and the host image with the SURF-DCT-SVD method, the embedding stage is carried out. At the extraction stage, it will produce a watermark image from the watermarked image. Furthermore, various attacks were carried out against the proposed method. Experimental results show SVD can increase the robustness of DCT and SURF-based watermarking schemes. The proposed watermarking technique is resistant to JPEG compression attacks, noise addition, signal processing, and geometry attacks. In addition, the other state-of-the-art techniques are compared to the performance of the proposed method. Thus, the proposed watermarking scheme can protect ownership and medical records of medical images.

Unmanned aerial vehicles (UAV) technology has been an innovative advancement in the scientific environment over recent years. In this paper, we propose an approach that facilitates UAVs with a monocular camera combined with light detection and range (LiDAR) sensor to navigate autonomously for stairs climbing in completely unknown, GPS-denied indoor environments. The suggested approach utilizes a state-of-the-art CNN model for the task. We suggest a novel approach utilizing the video feed derived from the UAV front camera to determine the next maneuver in the deep neural network model. The process is viewed as a classification activity, where the deep neural network model classifies the image as a stair or no-stair and LiDAR sensor data are used for distance calculation. The training is performed from a dataset of images obtained from multiple stairs. We show the effectiveness of the proposed device in indoor stairs scenarios in real-time.

In medical imaging and applications, efficient image sampling and transfer are some of the key fields of research. The compressed sensing (CS) theory has shown that such compression can be performed during the data retrieval process and that the uncompressed image can be retrieved using a computationally flexible optimization method. The objective of this study is to propose compressed medical imaging for a different type of medical images, based on the combination of the average sparsity model and reweighted analysis of multiple basis pursuit (M-BP) reconstruction methods, referred to as multiple basis reweighted analysis (M-BRA). The proposed algorithm includes the joint multiple sparsity averaging to improves the signal sparsity in M-BP. In this study, four types of medical images are opted to fill the gap of lacking a detailed analysis of M-BRA in medical images. The medical dataset consists of magnetic resonance imaging (MRI) data, computed tomography (CT) data, colonoscopy data, and endoscopy data. Employing the proposed approach, a signal-to-noise ratio (SNR) of 30 dB was achieved for MRI data on a sampling ratio of M / N = 0.3 . SNR of 34, 30, and 34 dB are corresponding to CT, colonoscopy, and endoscopy data on the same sampling ratio of M / N = 0.15 . The proposed M-BRA performance indicates the potential for compressed medical imaging analysis with high reconstruction image quality.

Watermarking is a method to protect the copyright of a work. Watermark can be either text, image, audio, or video. Data insertion is done in such a way so that the data will not damage the secured digital information. The data that is inserted cannot be removed from the digital information and must be able to be re-extracted. This research will analyze the compressive sampling based on DCT-DWT for watermark compression on video watermarking using DWT-SVD and OMP reconstruction with RS Code to check on the error bit. Our system produces BER 0.25, PSNR 54.577 dB of PSNR, and MSE 0.215. This system is resistant to rescaling attack.

P ada makalah ini, perancangan audio watermarking memanfaatkan kode PN yang terdistribusi Gaussian atau Normal dengan menggunakan citra sebagai watermark yang disisipkan pada audio. Watermark yang berupa citra biner diubah ke dalam vektor 1 dimensi, kemudian dijumlahkan dengan kode PN terdistribusi normal yang disaring dengan filter psikoakustik. Setelah itu, sinyal dikalikan dengan faktor gain α sebelum dijumlahkan dengan host audio untuk mendapatkan watermarked audio. Hasil dari simulasi menunjukkan bahwa sistem memiliki kapasitas watermark yang tinggi pada 689.06 bps, imperseptibilitas yang baik pada SNR>26 dB, dan tahan terhadap serangan LPF mulai frekuensi cut off 6 kHz keatas, serangan Additive Noise mulai 40 dB keatas, resampling pada rate 16 kHz, LSC 1% - 10%, dan kompresi MP3 untuk rate 192 kbps. Kata kunci: Audio Watermarking, Kode PN, distribusi normal, filter sikoakustik ABSTRACT In this paper, the design of audio watermarking utilizes PN code that is Gaussian or Normal distributed by using the image as a watermark inserted in the audio. The watermark in the form of binary images is converted into a 1-dimensional vector, then summed up with a normally distributed PN code filtered by a psychoacoustic filter. After that, the signal is multiplied by α gain factor before adding it to the audio host to get the watermarked audio. The result of the simulation shows that the system has a high watermark capacity at 689.06 bps, good imperceptibility at SNR> 26 dB, and withstand LPF attacks starting from 6 kHz cut-off frequency and above, Additive Noise attacks from 40 dB up, resampling at 16 kHz , LSC 1% - 10%, and MP3 compression for 192 kbps rate. Keywords: Audio Watermarking, PN code, normal distribution, psychoacoustic filter

Kolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik. Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACT Cholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds. Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network

Pemisahan reff lagu secara manual untuk sebuah database membutuhkan waktu yang sedikit lama. Hal ini disebabkan untuk menentukan semua posisi reff dalam sebuah lagu, seseorang harus mengetahui lagu tersebut dengan mendengarkan lagu secara keseluruhan. Penelitian ini mengusulkan sistem penambahan database otomatis secara cepat dan tepat dengan memanfaatkan perkembangan audio processing dalam industri musik. Kami membuat sistem dengan menggunakan lagu sebagai input dilengkapi dengan ekstraksi ciri menggunakan metode Fast Fourier Transform (FFT). Kemudian sistem akan melakukan pencocokan data antara potongan reff pertama lagu yang sudah diketahui posisinya dengan lagu tersebut secara utuh. Hal ini dilakukan untuk mendapatkan output berupa posisi reff kedua dan ketiga dalam sebuah lagu. Penggunaan metode FFT pada makalah ini diharapkan memiliki akurasi tinggi dan waktu komputasi yang baik.

This paper proposes a Luby Transform (LT)-like network coding (LTL-NC) scheme for wireless Internet-of-Things (IoT) super-dense network with massive number of nodes and packets as an alternative to the existing networks that require retransmission for the lost or erased packets. The capability of the proposed LTL-NC allows the system not to retransmit the erased packets because the destination can still recover the packets, using other packets received later, in random time-slots. Furthermore, LTL-NC is expected to have low computational complexity and flexibly decode random arrival packets. In this paper, we model both simple and complex wireless networks as well as their random packets arrival using LTL-NC expressed by their different degree distributions. Performances of the proposed LTL-NC are evaluated under frequency-flat Rayleigh fading channels and binary erasure channel (BEC) in terms of packet loss rate (PLR), throughput, and extrinsic information transfer (EXIT) chart obtained from computer simulations. For fairness, wireless networks using the proposed LTL-NC are compared to networks applying existing transfer control protocol/internet protocol (TCP/IP) with the same number of packets and same length of time-slots. We evaluate the performances using a series of computer simulations and confirm the effectiveness of the proposed LTL-NC scheme indicated by higher throughput under the same given network traffic.

In this paper, we propose a new access technique to prioritize human over machines for future wireless Internet-of-Things (IoT) networks. This paper develops coded random access (CRA) using repetition codes as a multiple access scheme for ultra high dense networks, which is expected to serve millions of nodes, covering human and machines simultaneously. Repetition codes are selected because of its simplicity in design and implementation, where a packet is copied and transmitted to the common destination according to the designed rate. This paper aims to maximize the number of users (both human and machines), where the priority of access is given to the human. In this study, human and machines are using coding and modulation system that match each other, according to extrinsic information transfer (EXIT) chart, to minimize loss. We evaluate the performance of the proposed access scheme using simple degree distribution for human and machines groups in terms of packet-loss-rate and throughput. We found that human group can be prioritized indicated by low packet-loss rate (PLR) and higher throughput. These results are believed to be useful for future application of super dense networks involving devices in the Internet-of-Things (IoT) networks.