Search Results (89)

Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of color space transformations as a preprocessing technique to reorganize visual information and improve classification performance using isolated cell images. Twelve color space transformations were compared, including RGB, CMYK, HSV, Grayscale, CIELAB, YUV, the individual RGB channels, and combinations of these channels (RG, RB, and GB). Two classification strategies were employed: binary classification (normal vs. abnormal) and five-class classification. The SIPaKMeD dataset was used, with images resized to $256\times 256$ pixels via zero-padding. Data augmentation included random flipping and ±10° rotations applied with a 50% probability, followed by normalization. A custom CNN architecture was developed, comprising four convolutional layers followed by two fully connected layers and an output layer. The model achieved average precision, recall, and F1-score values of 91.39%, 91.34%, and 91.31% for the five-class case, respectively, and 99.69%, 96.68%, and 96.89% for the binary classification, respectively; these results were compared with a VGG-16 network. Furthermore, CMYK, HSV, and the RG channel combination consistently outperformed other color spaces, highlighting their potential to enhance classification accuracy. Full article

The growing digitization of healthcare has amplified concerns about the privacy and security of medical images, as conventional encryption methods often fail to provide sufficient protection. To address this gap, we propose a privacy-enhancing image encryption algorithm that integrates SHA-256 hashing, block-wise processing (16 × 16 with zero-padding), DNA encoding with XOR operations, and logistic map-driven key generation into a unified framework. This synergistic design balances efficiency and robustness by embedding data integrity verification, ensuring high sensitivity to initial conditions, and achieving strong diffusion through dynamic DNA rules. Experimental results confirm that the scheme achieves high NPCR (0.997), UACI (0.289), entropy (7.995), and PSNR (27.89 dB), outperforming comparable approaches while maintaining scalability to large image formats and robustness under compression (JPEG quality factors 90 and 70). These findings demonstrate that the proposed method offers an efficient and resilient solution for securing medical images, ensuring confidentiality, integrity, and practical applicability in real-world healthcare environments. Full article

Attackers can use Ethernet or WiFi sniffers to capture smart home device traffic and identify device events based on packet length and timing characteristics, thereby inferring users’ home behaviors. To address this issue, traffic obfuscation techniques have been extensively studied, with common methods including packet padding, packet segmentation, and fake traffic injection. However, existing research predominantly utilizes non-real-time traffic to verify whether traffic obfuscation techniques can effectively reduce the recognition rate of traffic analysis attacks on smart home devices. It often overlooks the potential impact of obfuscation operations on device connectivity and functional integrity in real network environments. To address this limitation, an online experimental framework for three fundamental traffic obfuscation techniques is proposed: packet padding, packet segmentation, and fake traffic injection. Experimental results demonstrate that the proposed framework maintains the continuous connectivity and functional integrity of smart home devices with a low system overhead, achieving an average CPU usage rate of less than 0.4% and an average memory occupancy rate of less than 2%. Evaluation results based on the random forest classification method show that the device event recognition accuracy for injected fake traffic exceeds 89%. In this context, a higher recognition accuracy indicates that attackers are more effectively deceived by the injected fake traffic. Conversely, the recognition accuracy for packet padding and packet segmentation methods is nearly zero, and a lower recognition accuracy in these cases implies a more effective implementation of those obfuscation techniques. Further evaluation results based on the deep learning classification method reveal that the packet segmentation approach significantly reduces device recognition accuracy for certain devices to below 5%, while simultaneously increasing the false recognition rate for other devices to over 95%. In contrast, fake traffic injection achieves a device recognition accuracy exceeding 90%. Moreover, the obfuscation effect of the packet padding method is found to be suboptimal, a finding consistent with existing literature suggesting that no single obfuscation technique can effectively withstand all types of traffic analysis attacks. Full article

The orthogonal time frequency space (OTFS) is a modulation designed to transmit data in high Doppler channels where the usage of the orthogonal frequency division multiplexing (OFDM) is challenging. The random padded OTFS (RP-OTFS) modulation, introduced recently, is an OTFS-like waveform optimized for more precise estimation of channel state information (CSI) and, in the case of integrated sensing and communication (ISAC), for radar detection as well. One of the main drawbacks of the RP-OTFS is the high level of interference between carriers (the inter-carrier interference—ICI) of Doppler-delay (DD) grid. In the article, we optimize the RP-OTFS waveform in terms of reducing the level of pilot-to-data interference and also offer a way to reduce the data carrier interference. The reduction in the pilot-to-data interference is achieved due to the introduction of the following: (1) redistributing interferences along the DD grid, and (2) special DD grid configuration. In turn, the reduction in data carrier interference is achieved by extrapolating the estimate of channel state information. The proposed approach allows us to reduce the influence of the interference component and, as a result, to improve the probability of correct demodulation in the ISAC RP-OTFS system. Various DD grid configurations for different use cases from a radar point of view are considered in the article. The questions of choosing appropriate values of the DD grid parameters depending on the operating environment are also discussed here. In simulations, the ICI-reduced RP-OTFS is compared with its predecessor, the regular RP-OTFS, and classical modulations: OFDM and zero-padded OTFS, and benefits of its usage are shown: lower bit error rate (BER) of the transmission and higher detection probability of the radar detection. Full article

In this study, aligned with the principles of the circular economy and sustainability, novel eugenol@natural zeolite (EG@NZ) and citral@natural zeolite (CT@NZ) nanohybrids were developed. These nanohybrids were successfully incorporated into a pork gelatin (Gel)/glycerol (Gl) composite matrix using an extrusion–compression molding method to produce innovative active packaging films: Gel/Gl/xEG@NZ (where x = 5, 10, and 15%wt.) and Gel/Gl/xCT@NZ (where x = 5 and 10%wt.). All films exhibited zero oxygen barrier properties. Release kinetic studies showed that both EG@NZ and CT@NZ nanohybrids adsorbed up to 58%wt. of their respective active compounds. However, EG@NZ exhibited a slow and nearly complete release of eugenol, whereas CT@NZ released approximately half of its citral content at a faster rate. Consequently, the obtained Gel/Gl/xEG@NZ films demonstrated significantly higher antioxidant activity as measured by the 2,2-diphenyl-1-picrylhydrazylradical (DPPH) assay and superior antibacterial effectiveness against Escherichia coli and Listeria monocytogenes compared to their CT-based counterparts. Overall, the Gel/Gl/xEG@NZ films show strong potential for applications as active pads for fresh pork ham slices, offering zero oxygen permeability, enhanced antioxidant and antibacterial properties, and effective control of total viable count (TVC) growth, maintaining a low and steady rate beyond the 10th day of a 26-day storage period. Full article

Low Earth orbit (LEO) satellites offer a revolutionary potential for positioning, navigation, and timing (PNT) services due to their stronger signal power and rapid geometric changes compared to traditional global navigation satellite systems (GNSS). However, dedicated LEO navigation systems face high costs, so opportunity navigation based on LEO satellites is a potential solution. This paper presents an orthogonal frequency division multiplexing (OFDM)-based LEO navigation system and analyzes its navigation performance. We use 5G new radio (NR) as the satellite transmitting signal and introduce the NR signal components that can be used for navigation services. The LEO NR system and a novel zero-padding correlation (ZPC) are introduced. This ZPC receiver can eliminate cyclic prefix (CP) and inter-carrier interference, thereby improving tracking accuracy. The power spectral density (PSD) for the NR navigation signal is derived, followed by a comprehensive analysis of tracking accuracy under different NR configurations (bandwidth, spectral allocation, and signal components). An extended Kalman filter (EKF) is proposed to fuse pseudorange and pseudorange rate measurements for real-time positioning. The simulations demonstrate an 80% improvement in ranging precision (3.0–4.5 cm) and 88.3% enhancement in positioning accuracy (5.61 cm) compared to conventional receivers. The proposed ZPC receiver can achieve centimeter-level navigation accuracy. This work comprehensively analyzes the navigation performance of the LEO NR system and provides a reference for LEO PNT design. Full article

The number of hologram points in near-field acoustical holography (NAH) for a vibro-acoustic system plays a vital role in conditioning the transfer function between the source and measuring points. The requirement for many overdetermined hologram points for extended sources to obtain high accuracy poses a problem for the practical applications of NAH. Furthermore, overdetermination does not generally ensure enhanced accuracy, stability, and convergence, owing to the problem of rank deficiency. To achieve satisfactory reconstruction accuracy with underdetermined hologram data, the best practice for choosing hologram points and regularization methods is determined by comparing cross-linked sets of data-sorting and regularization methods. Three typical data selection and treatment methods are compared: iterative discarding of the most dependent data, monitoring singular value changes during the data reduction process, and zero padding in the patch holography technique. To test the regularization method for inverse conditioning, which is used together with the data selection method, the Tikhonov method, Bayesian regularization, and the data compression method are compared. The inverse equivalent source method is chosen as the holography method, and a numerical test is conducted with a point-excited thin plate. The simulation results show that selecting hologram points using the effective independence method, combined with regularization via compressed sensing, significantly reduces the reconstruction error and enhances the modal assurance criterion value. The experimental results also support the proposed best practice for inverting underdetermined hologram data by integrating the NAH data selection and regularization techniques. Full article

This article presents a low-noise amplifier (LNA) with high image rejection ratio (IRR) operating in the 5G millimeter-wave band using a 65 nm CMOS process. The circuit adopts an inter-stage notch filtering structure composed of a transformer and a switched capacitor array to achieve image suppression and impedance matching with no die area overhead. By adjusting the values of the switch capacitor array, the transmission zeros are positioned in the stopband while the poles are placed in the passband, thereby realizing image rejection. Furthermore, the number and distribution of poles under the both real and complex impedance conditions are analyzed. Moreover, the quality factor (Q) of the zero is derived to establish the relationship between Q and the image rejection ratio, guiding the optimization of both gain and IRR of the circuit design. Measurement results demonstrate that the LNA exhibits a gain of 18 dB and a noise figure (NF) of 4.4 dB at 40 GHz, with a corresponding IRR of 53.4 dB when the intermediate frequency (IF) is 6 GHz. The circuit demonstrates a 3 dB bandwidth from 36.3 to 40.7 GHz, with an IRR greater than 42 dB across this frequency range. The power consumption is 25.4 mW from a 1 V supply, and the pad-excluded core area of the entire chip is 0.13 mm². Full article

Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications. Full article

To achieve on-board real-time processing for sliding-spotlight mode synthetic aperture radar (SAR), on the one hand, this paper proposes an adaptive and efficient imaging algorithm for the sliding-spotlight mode. On the other hand, a batch processing method was designed and optimized based on the AGX Orin platform to implement the algorithm effectively. Based on the chirp scaling (CS) algorithm, sliding-spotlight mode imaging can be achieved by adding Deramp preprocessing along with either zero-padding or performing an extra chirp scaling operation. This article analyzes the computational complexity of the two algorithms and provides a criterion called the Method Choice Indicator (MCI) for selecting the appropriate method. Additionally, the mathematical expressions for time–frequency transformation are derived, providing the theoretical basis for calculating the equivalent PRF and the azimuth width represented by a single pixel. To increase the size of the data that AGX Orin can process, the batch processing method was proposed to reduce peak memory usage during imaging, so that the limited memory could be better utilized. Meanwhile, this algorithm was also compatible with strip mode and TOPSAR (Terrain Observation by Progressive scans SAR) mode imaging. While batch processing increased data transfers, the integrated architecture of AGX Orin minimized the negative impact. Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. As a result, it took 19.25 s to complete the imaging process for sliding-spotlight mode data with a size of 42,966 × 27,648. Since satellite data acquisition time was 11.43 s, it can be considered that this method achieved near-real-time imaging. The experimental results demonstrate the feasibility of on-board processing. Full article

Deep neural networks for image denoising typically follow an encoder–decoder model, with convolutional (Conv) layers as essential components. Conv layers apply zero padding at the borders of input data to maintain consistent output dimensions. However, zero padding introduces ring-like artifacts at the borders of output images, referred to as border effects, which negatively impact the network’s ability to learn effective features. In traditional methods, these border effects, associated with convolutional/deconvolutional operations, have been mitigated using patch-based techniques. Inspired by this observation, patch-wise denoising algorithms were explored to derive a CNN architecture that avoids border effects. Specifically, we extend the patch-wise autoencoder to learn image mappings through patch extraction and patch-averaging operations, demonstrating that the patch-wise autoencoder is equivalent to a specific convolutional neural network (CNN) architecture, resulting in a novel residual block. This new residual block includes a mask that enhances the CNN’s ability to learn border features and eliminates border artifacts, referred to as the Border-Enhanced Residual Block (BERBlock). By stacking BERBlocks, we constructed a U-Net denoiser (BERUNet). Experiments on public datasets demonstrate that the proposed BERUNet achieves outstanding performance. The proposed network architecture is built on rigorous mathematical derivations, making its working mechanism highly interpretable. The code and all pretrained models are publicly available. Full article

Passive radiation cooling technology, as a new zero-energy refrigeration technology method, has received widespread attention in recent years. However, due to differences in the testing devices used by different teams, it becomes difficult to directly compare the cooling performance of the respective prepared materials. This study combines experimental and theoretical methods to explore the impact of testing equipment and sample size on the results of the radiative cooling capacity evaluation. The research results show that when evaluating the cooling performance of materials in thermal insulation chambers, if the sample diameter is equal to or larger than 10 cm, at a sample diameter ≥ 10 cm in insulated chambers, cooling capacity stabilizes at ~25 °C (daytime) and ~28 °C (nighttime), with <2% variation across larger sizes. The evaluation of cooling capacity is not affected by the structure of the test equipment or the size of the material. However, variations in sample placement depth will always have a significant impact on the evaluation results, so a uniform placement depth needs to be specified. In addition, when using an open device to evaluate the cooling performance of materials, if the sample diameter is greater than or equal to 10 cm and the foam pad thickness is greater than or equal to 8 cm, foam pad thickness ≥ 8 cm in open devices reduces thermal interference by 89%, enabling consistent evaluations. The measured value of the cooling capacity is also not affected by the structure and material size of the test device. This study provides a basis for the standardization of radiant cooling testing, thereby promoting the practical application of radiant cooling technology. Full article

Quantum key distribution (QKD), a key application of quantum information technology and “one-time pad” (OTP) encryption, enables secure key exchange with information-theoretic security, meaning its security is grounded in the laws of physics rather than computational assumptions. However, in QKD networks, achieving long-distance communication often requires trusted relays to mitigate channel losses. This reliance introduces significant challenges, including vulnerabilities to compromised relays and the high costs of infrastructure, which hinder widespread deployment. To address these limitations, we propose a zero-trust spatiotemporal diversification framework for multipath–multi-key distribution. The proposed approach enhances the security of end-to-end key distribution by dynamically shuffling key exchange routes, enabling secure multipath key distribution. Furthermore, it incorporates a dynamic adaptive path recovery mechanism that leverages a recursive penalty model to identify and exclude suspicious or compromised relay nodes. To validate this framework, we conducted extensive simulations and compared its performance against established multipath QKD methods. The results demonstrate that the proposed approach achieves a 97.22% lower attack success rate with 20% attacker pervasiveness and a 91.42% reduction in the attack success rate for single key transmission. The total security percentage improves by 35% under 20% attacker pervasiveness, and security enhancement reaches 79.6% when increasing QKD pairs. Additionally, the proposed scheme exhibits an 86.04% improvement in defense against interception and nearly doubles the key distribution success rate compared to traditional methods. The results demonstrate that the proposed approach significantly improves both security robustness and efficiency, underscoring its potential to advance the practical deployment of QKD networks. Full article

Background: Radiomics has become a valuable tool in medical imaging, but its clinical use is limited by data variability and a lack of reproducibility between centers. This study aims to assess the differences between two scanners and provide guidance on image acquisition methods to reduce variations between images obtained from different centers. Methods: This study utilized medical images obtained in two different imaging centers, with two different 3T MRI scanners. For each scanner, 3D T2 FLAIR sequences were acquired in two forms: the raw and the clinical practice images typically used in diagnostic workflows. The differences between images were analyzed regarding resolution, SNR, CNR, and radiomic features. To facilitate comparison, bias field correction was applied, and the data were standardized to the same scale using Z-score normalization. Descriptive and inferential statistical methods were used to analyze the data. Results: The results show that there are significant differences between centers. Filtering and zero-padding significantly influence the resolution, SNR, CNR values, and radiomics features. Applying Z-score normalization has resolved variations in features sensitive to scale differences, but features reflecting dispersion and extreme values remain significantly different between scanners. Some feature differences may be resolved by analyzing the raw images in both centers. Conclusions: Variations arise due to different acquisition parameters and the differing quality and sensitivity of the equipment. In multi-center studies, acquiring raw images and then applying standardized post-processing methods across all images can enhance the robustness of results. This approach minimizes technical differences, and preserves the integrity of the information, reflecting a more accurate representation of reality and contributing to more reliable and reproducible findings. Full article

Carbon-based polymer composites are widely used in wearable devices due to their exceptional electrical conductivity and flexibility. However, their temperature-dependent resistance variations pose significant challenges to device safety and performance. A negative temperature coefficient (NTC) can lead to overcurrent risks, while a positive temperature coefficient (PTC) compromises accuracy. In this study, we present a novel hybrid composite combining carbon nanotubes (CNTs) with NTC properties and carbon black (CB) with PTC properties to achieve a near-zero temperature coefficient of resistance (TCR) at an optimal ratio. This innovation enhances the safety and reliability of carbon-based polymer composites for wearable heating applications. Furthermore, a thermochromic pigment layer is integrated into the hybrid composite, enabling visual temperature indication across three distinct zones. This bilayer structure not only addresses the TCR challenge but also provides real-time, user-friendly temperature monitoring. The resulting composite demonstrates consistent performance and high precision under diverse heating conditions, making it ideal for wearable thermotherapy pads. This study highlights a significant advancement in developing multifunctional, temperature-responsive materials, offering a promising solution for safer and more controllable wearable devices. Full article