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In this paper, we designed and implemented a multi-antenna configured secure millimeter-wave (mmWave) CP-less multi-user orthogonal chirp division multiplexing (OCDM) transceiver. The proposed simulated system emphasizes more applicable performance metrics for a typically assumed case of four users and a passive eavesdropper for audio data transmission. We introduce a four-dimensional hyperchaotic system-based encryption algorithm to enhance physical layer security (PLS). In addition, low-density parity check (LDPC), TURBO, ( $3, 2$ ) single parity check (SPC), and repeat and accumulate (RA) channel coding with Cholesky decomposition-based zero-forcing (CD-ZF) and minimum mean square error (MMSE) signal detection techniques for a better bit error rate (BER) were also implemented. The simulation results signify the effectiveness of the proposed system in terms of PLS enhancement with low correlation coefficients (14.62%, 7.61%, 13.61%, and 15.39% for users 1, 2, 3, and 4, respectively), an achievable secrecy rate with a low signal-to-interference and noise ratio (SINR) of the passive eavesdropper, an achievable out-of-band (OOB) power emission of 341 dB, an estimated average short-time Fourier transform (STFT) spectral power difference of 7.68 dB and estimated peak-to-average power ratios (PAPRs) ranging from 7 to 7.5 dB at a complementary cumulative distribution function (CCDF) of $1\times 10^{-3}$ for different ground transmitting channels. At an identical signal-to-noise ratio (SNR) of 17 dB, all four users achieved a bit error rate of $1\times 10^{-4}$ under RA channel coding, CD-ZF, and 16-QAM (quadrature amplitude modulation) digital modulation.

The malignant neoplastic malady known as cancer appears to exhibit a significantly elevated rate of mortality owing to its virulence and pronounced propensity for metastasis. To augment the diagnostic efficacy, research endeavors have been undertaken utilizing complex deep learning architectures. However, the performance of these efforts remains circumscribed by smaller dataset size, quality of the data, the interclass variations present between lung adenocarcinoma and lung squamous cell carcinoma, and the complexity of deploying to mobile devices and failure to address both image and patient level accuracy measurements. To surmount these obstacles, the present study proposes a stage-based method for enhancing the images, in conjunction with utilizing a global context attention-guided convolutional neural network that effectively captures both channel and spatial information and semantic information extracted from the input image. Implementing the proposed methodology increased total image level accuracy to 99.76% and a patient level accuracy of 96.5%, a metric that has yet to be previously quantified. The addition of the global context attention module decreases the model’s parameter count by 0.47 million, reduces the computational costs by saving 10.54 million floating point operations per second (FLOPs) and 10.72 million multiply-accumulate operations (MACs), and results in a 0.03s improvement in inference time. Furthermore, this module enhances both image level and patient level accuracy, boosting them by 2.84% and 3.17%, respectively, compared to using only the convolutional block attention module in the baseline convolutional neural network. Consequently, this modification renders the model highly suitable for deployment on mobile devices due to its adaptability. Our findings provide supporting evidence for the potential of this method to serve as a noninvasive screening tool capable of reliably classifying lung and colon cancer subtypes.

Trash production and disposal have emerged as serious issues for underdeveloped nations as their populations have swelled. As manual classification can be both time-consuming and potentially dangerous, therefore, nowadays, it is increasingly being replaced by automated methods. Recent advances in AI and deep learning have allowed for significant advancements in trash detection and classification systems. Due to the lack of a comprehensive trash detection dataset tailored to Bangladesh, we set out to collect data that would accurately portray the complexity of Bangladesh’s scenario while also incorporating openlittermap. In this study, we employ a deep learning model known as YOLOv5. Several variants of the YOLOv5 model are used and assessed with both the freshly minted dataset and the already existing benchmark datasets. Simulation results indicate that the finetuned YOLOv5 model outperforms existing models in terms of mean average precision (mAP) and F1-score. On the Bangladeshi dataset, the model shows an mAP of 34.3% and an F1-score of 43.7%. The mAP and F1-score provide a holistic evaluation of YOLOv5’s object recognition accuracy, localization, and precision-recall balance. By incorporating the additional data from openlittermap into the new dataset, the mAP is increased to 45.4%. In addition, for some variants of YOLOv5, the suggested model produces greater mAP than the current literature on both the TACO and PlastOpol datasets. The model also achieves an mAP of 84.4% and an F1-score of 78.2% in single-class detection experiments with the newly created dataset. This is because concentrating on just one class helps eliminate class ambiguity, improves localization accuracy, and mitigates class imbalance.

Human pose estimation has drawn extensive attention recently and there has been significant progress on it due to the rising popularity of convolutional neural networks (CNN). However, existing state-of-the-art approaches suffer from occlusion, complicated backgrounds, and substantial position fluctuations because of disregarding the human body form. Human parsing is a very pertinent activity that can provide crucial semantic data about bodily parts for position estimation. To overcome the aforesaid limitations, this paper introduces a human pose estimation method using a group-based convolutional neural network model. The proposed method adopts a bottom-up parsing strategy that yields features to extract skeletal key points in the human body. Moreover, it creates a grouping of anatomical key points for individuals by utilizing the non-parametric description for the key point association vector field. Experimental results indicate that the proposed method provides superior performance than the state-of-the-art algorithms in terms of accuracy. In addition, it optimizes its output and detects occluded as well as invisible key points by incorporating feature representation. The proposed method surpasses the recent methods, achieving 93% of the mean average accuracy.

Digital watermarking has been utilized effectively for copyright protection of multimedia contents. This paper suggests a blind symmetric watermarking algorithm using fan beam transform (FBT) and QR decomposition (QRD) for color images. At first, the original image is transferred from RGB to L*a*b* color model and FBT is applied to b* component. Then the b*component of the original image is split into m × m non-overlapping blocks and QRD is conducted to each block. Watermark data is placed into the selected coefficient of the upper triangular matrix using a new embedding function. Simulation results suggest that the presented algorithm is extremely robust against numerous attacks, and also yields watermarked images with high quality. Furthermore, it represents more excellent performance compared with the recent state-of-the-art algorithms for robustness and imperceptibility. The normalized correlation (NC) of the proposed algorithm varies from 0.8252 to 1, the peak signal-to-noise ratio (PSNR) varies from 54.1854 to 54.1892, and structural similarity (SSIM) varies from 0.9285 to 0.9696, respectively. In contrast, the NC of the recent state-of-the-art algorithms varies from 0.5193 to 1, PSNR varies from 38.5471 to 52.64, and SSIM varies from 0.9311 to 0.9663, respectively.

This report describes the results of meteorological observations at Syowa Station from February 1st, 2007 to January 31st, 2008, carried out by the Meteorological Observation Team of the 48th Japanese Antarctic Research Expedition(JARE-48). The observation methods, instruments and statistical methods used by JARE-48 were almost the same as those used by the JARE-47 observation team. Remarkable weather phenomena observed during the period of JARE-48 are as follows.1) In October 2007, there were 6 blizzards and 15 blizzard days, and the monthly mean temperature was -10.8℃, which was the highest record for Syowa Station.2) In November 2007, the monthly sunshine duration at Syowa Station was 474.8 hours, which was the longest record, and minimum humidity 21% updated minimum.3) The monthly mean amount of total ozone over Syowa Station in July was 233 m atm-cm, which was the lowest record. The amount of total ozone became lower than or equal to 220 m atm-cm from mid-August to end of October. On October 5th, the amount of total ozone was 138 m atm-cm, which was the lowest value in 2007.

This paper proposes a new technique for improving the performance of linear prediction analysis by utilizing a refined version of the autocorrelation function. Problems in analyzing voiced speech using linear prediction occur often due to the harmonic structure of the excitation source, which causes the autocorrelation function to be an aliased version of that of the vocal tract impulse response. To estimate the vocal tract characteristics accurately, however, the effect of aliasing must be eliminated. In this paper, we employ homomorphic deconvolution technique in the autocorrelation domain to eliminate the aliasing effect occurred due to periodicity. The resulted autocorrelation function of the vocal tract impulse response is found to produce significant improvement in estimating formant frequencies. The accuracy of formant estimation is verified on synthetic vowels for a wide range of pitch frequencies typical for male and female speakers. The validity of the proposed method is also illustrated by inspecting the spectral envelopes of natural speech spoken by high-pitched female speaker. The synthesis filter obtained by the current method is guaranteed to be stable, which makes the method superior to many of its alternatives.

Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

We employ the small limit argument approximation (SLAA) to streamline the mathematical representation of a conventional Rician fading channel within the proposed single-input single-output (SISO) system model. The SLAA on the zeroth-ordered modified Bessel function of the first kind facilitates the transition of a newly modified Rician fading channel into the low signal-to-noise ratio (SNR) regime, where either the transmission power is low or the bandwidth is large. We reveal that, for a SISO system with an instantaneous SNR set at 20 dB and a Rician factor set at 0.05, the channel capacity amounts to 7.1 bits/s/Hz. [ABSTRACT FROM AUTHOR]

We enhanced the performance of linear prediction (LP) for bone-conducted (BC) speech signals by deriving a regularized version of the modified covariance (MC) method. The spectral estimation accuracy is discussed for the purpose of assessing the performance of LP. Inherently, BC speech has an expanded spectral dynamic range that causes ill-condition in conventional spectral estimation methods. To overcome this problem, we focus on the covariance method and its modified version, the MC method. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. To deal with this situation, the regularized least squares method is utilized. Motivated by this idea, we derive the regularized MC (RMC) method in this study for BC speech analysis. The RMC method has the effect of compressing the spectral dynamic range of the input speech signal, and this effect mitigates the ill-conditioned problem of LP. Through experiments, we show that the RMC method provides a more accurate spectral estimation than conventional spectral estimation methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. A way to determine the regularization parameter in practice is experimentally derived. The RMC method with such a setting provides the best performance of spectral estimation for BC speech. [ABSTRACT FROM AUTHOR]

In this paper, we propose the incorporation of noisy image patches and the impulse response of a low-pass filter (LPF) in a convolutional neural network (CNN) to denoise Poisson--Gaussian noise in low-dose computed tomography (LDCT) images. The approach is referred to as fast and flexible denoising CNN (FFDNet)-impulse response (FFDNet-IR) in this paper. The power spectrum sparsity LPF (SLPF) allows low-frequency components to pass through while suppressing higher frequency components by the sparsity approach of the power spectrum, and it is employed to determine the impulse response of LPF. Three well-known types of LPF, namely, Direct LPF, Gaussian LPF, and Butterworth LPF, are also considered to obtain the impulse response of LPF. In the FFDNet-IR, both the noisy image patches and the IR of the LPF are sequentially inputted into the FFDNet to eliminate the Poisson--Gaussian noise. This approach enhances the denoising performance in LDCT images compared with the conventional FFDNet in the evaluation metrics of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM). Moreover, the FFDNet-IR trained with the Poisson--Gaussian noise model demonstrates the generalization ability and effectively eliminates only Poisson or Gaussian noise. The experiments indicate that the FFDNet-IR more effectively suppresses the noise artifacts and preserves image details compared with the baseline FFDNet, as well as traditional methods such as block-matching and 3D filtering (BM3D) and nonlocal mean (NLM) for LDCT image denoising. [ABSTRACT FROM AUTHOR]

We propose a blind image quality assessment (BIQA) method of using the multitask-learningbased end-to-end convolutional neural network (CNN) approach. The architecture of the proposed method is integrated by two streams. In the first stream, multiscale image features are extracted by using the inception and pyramid pooling modules. Natural scene statistics (NSS)-based features are extracted in the second stream. The two streams are then integrated into fully connected layers to estimate the image quality score. The performance of the proposed method is validated with four public IQA databases and the obtained experimental results show the superiority of the proposed method over conventional IQA methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a blind noisy image quality estimation method of simultaneously utilizing three statistical features extracted from three different domains of the input noisy image. The statistical features used in this paper are (i) eigen-based variance by the covariance matrix of image blocks in the spatial domain, (ii) the spectral entropy of the power spectrum in the frequency domain, and the standard deviation in the wavelet domain. The extracted statistical features are fed into an extreme learning machine algorithm for mapping into perceptual quality scores. The model is trained and tested on images with six common noise distortion types commonly occurring in real-world applications: additive white Gaussian noise, additive Gaussian noise in color component, high-frequency noise, masked noise, impulse noise, and multiplicative noise. For the CSIQ, TID2008, TID2013, and KADID10k databases, the experimental results show that our method covers noise distortions wider than those of the conventional methods and achieves consistently better performance for blind noisy image quality assessment. [ABSTRACT FROM AUTHOR]

In this paper, we propose the sequential input of a noisy image patch and the impulse response of a low-pass filter (LPF) in the training of the conventional fast and flexible solution for CNN-based image denoising (FFDNet) architecture, which enhances denoising performance and edge preservation and achieves high perceptual quality. The proposed method consists of two steps. In the first step, the power spectrum sparsity is utilized to determine the impulse response of LPF and the resulting impulse response is added to the noisy image patch in a sequential form to estimate the low- and high-frequency components of the input image. In this step, the use of three different types of LPF is also considered. In the second step, the FFDNet architecture, a deep-learning-based image denoiser, is employed. The proposed method achieves satisfactory denoising performance for grayscale and color datasets on synthetic additive white Gaussian noise (AWGN) in terms of the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and learned perceptual image patch similarity (LPIPS) compared with the original FFDNet. The performances on realistic noise and for chest X-ray images are also investigated. [ABSTRACT FROM AUTHOR]