Search Results (484)

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

In this paper, inspired by the structural characteristics of the human eyeball, a bionically designed circular resonant structure is proposed, and a flexible chipless radio frequency identification (RFID) tag based on this concept is developed. By selectively adding or removing branch structures, the proposed tag achieves controllable resonant frequency shifts and distinguishable geometric pattern variations. Fabricated on a polyimide substrate with a compact size of 20 × 26 × 0.2 mm³, the tag achieves a coding capacity exceeding 45 bits while operating within an effective frequency bandwidth in 4–12 GHz, realizing a synergistic improvement in coding capacity and structural compactness under limited spectrum constraints. Simulation analyses are performed to investigate the encoding stability of the tag under various bending and rotational conditions relevant to flexible applications. Experimental results obtained under the unbent condition are consistent with the simulations, demonstrating the feasibility of the proposed chipless RFID tag. Full article

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based on absolute moment block truncation coding (AMBTC). In the proposed scheme, two identical AMBTC-decoded images are exploited as twin covers, and secret bits are adaptively embedded into paired pixels using a variable embedding rate. To ensure data integrity, a lightweight Hash-based Message Authentication Code (HMAC) mechanism is integrated, allowing reliable detection of tampering without additional side information. Experimental results demonstrate that the proposed method achieves high embedding capacity while preserving good visual quality and provides effective authentication against representative tampering cases, including pixel modification, noise addition, and cropping. These contributions highlight the advantages of combining DIRDH with AMBTC, offering a practical and secure solution for high-capacity reversible data hiding. Full article

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article

In the big-data-driven artificial intelligence era, similarity search, as a core operation in machine learning and data mining, demands high speed, energy efficiency, and scenario adaptability. Conventional electronic content-addressable memory (ECAMs) suffer from inherent RC delay bottlenecks, whereas existing optical content-addressable memory (OCAMs) are restricted by fixed bit-widths and limited distance metrics. In this work, we propose a variable bit-width all-optical CAM leveraging multi-segment modulators and phase-change material (PCM) Sb₂Se₃. The multi-segment memory unit (MSMU) therein compresses N-bit binary data into a single analog photonic unit, supporting direct data writing/loading without digital-to-analog converters (DACs) and flexible trade-offs between precision, storage capacity, noise immunity, and energy while enabling Hamming and nonlinear distance metrics. A six-element three-bit OCAM prototype was fabricated on a silicon nitride silicon-on-insulator (SiN-SOI) platform. Despite the absence of integrated high-speed phase shifters, the device still achieves reliable optical data storage and retrieval. K-nearest neighbor (kNN) simulations based on experimentally derived statistical data—validated on the iris, wine, and breast cancer datasets—show that the three-bit operating mode achieves classification accuracy comparable to Manhattan/Euclidean distances at high signal-to-noise ratios (SNRs), while the one-bit mode exhibits strong noise robustness. Energy consumption is 364 fJ/bit (3-bit) and 890 fJ/bit (1-bit). This work provides a high-speed, energy-efficient, and reconfigurable all-optical similarity search solution with experimentally verified device performance and dataset-validated applicability, showing great potential for widespread deployment in data-intensive machine learning and data-mining applications. Full article

Non-orthogonal multiple access (NOMA) has been identified as one of the key technologies for 6G capacity and latency gains. However, existing implementation challenges of the NOMA technique, related to carrier, timing, and phase offsets, successive interference cancellation (SIC) error propagation, packet loss dynamics, and host to software defined radios processing jitter, create obstacles in the practical implementation of NOMA. This paper bridges the gap between theory and hardware by introducing a complete two-user NOMA transmit–receive chain on a low-cost ADALM-Pluto software defined radio (SDR) platform. The proposed implementation integrates matched filtering, offset estimation and correction, SIC with waveform reconstruction and subtraction, and reliability reinforcement via rate-1/2 convolutional coding with Viterbi decoding. We have performed a complete validation of the proposed design in both downlink and uplink modes. We collected data regarding the packet-level and system-related metrics, such as end-to-end latency, bit error rate (BER), and success rate. Moreover, we demonstrate the implementation of the uplink NOMA without need for expensive GPS-disciplined oscillators by leveraging the Pluto Rev-C dual-transmit channels that share a common oscillator. We present detailed experimental results at 915 MHz with BPSK modulation for the downlink performance, and also show a full implementation of the uplink NOMA. We observe excellent reliability for the downlink setup and good reliability for the uplink system. Full article

With the evolution of smart grids, power communication networks are increasingly required to support high-bandwidth and diversified services such as high-definition video, real-time control, and positioning—services that impose dual challenges of communication capacity and spectrum constraints—under severe resource limitations. Conventional orthogonal modulation schemes exhibit significant limitations in spectral efficiency and concurrent access capabilities, particularly in supporting high-density user environments. To address this, we propose a communication system based on non-orthogonal overlapped chirp modulation, in which the intrinsic symmetry properties of chirp waveforms are utilized to enhance system design and performance. We first construct the system architecture with a multi-symbol concurrent transmission scheme and introduce continuous orthogonal phase modulation to improve symbol distinguishability and mitigate inter-symbol interference—an approach that effectively harnesses signal symmetry for interference suppression. At the receiver, a low-complexity demodulation algorithm based on correlation matrix computation is developed, further improved through oversampling techniques that exploit temporal and spectral symmetry in signal design. Monte Carlo simulations confirm that the proposed system outperforms traditional orthogonal chirp and orthogonal frequency division multiplexing systems in bit error rate performance and spectral efficiency across varying signal-to-noise ratios and modulation schemes. The proposed NOOC system achieves spectral efficiency scaling linearly with concurrency level K, reaching up to 16 bits/s/Hz for K = 16 with BPSK, compared to 1 bit/s/Hz in orthogonal systems. The study provides both a theoretical foundation and practical insights for developing symmetry-aware, efficient, and reliable air interface technologies suitable for future power-private networks. Full article

In this paper, a novel hybrid edge-detection steganography technique is proposed, which greatly increases the payload capacity without losing much of its invisibility. Conventional least significant bit (LSB) steganography has a low payload capacity and is sensitive to statistical analysis. Our approach combines Canny and Sobel edge-detection methods to find the optimal embedding regions and then performs Most Significant Bit (MSB) modifications in edge areas where the human visual system (HVS) is less sensitive to changes. Experimental results show that the performance of our proposed method outperforms conventional LSB-based steganographic methods by an average of 42.3% in payload capacity, while maintaining a PSNR greater than 38 dB and an SSIM above 0.95. The proposed method is also more robust against statistical attacks, such as chi-square analysis and RS steganalysis, which are critical challenges in secure data transmission. Full article

The increasing demand for higher data rates in optical communication systems, especially within data centers and backbone networks, calls for the development of advanced modulation formats that can significantly enhance system performance. In this work, we introduce a novel modulation format based on the Kramers–Kronig relations, designed to improve upon traditional techniques such as Pulse Amplitude Modulation (PAM) and Carrier-less Amplitude Phase (CAP) modulation. The novel modulation format was rigorously validated through experimental investigations using an optical wireless communication (OWC) link. The results demonstrate a notable improvement in bit error rate (BER) performance and receiver sensitivity when compared to the conventional PAM-4 modulation scheme and CAP-16 modulation schemes. Moreover, the proposed scheme effectively reduces the complexity of digital filtering required by CAP while lowering the demands on the Digital-to-Analog Converter (DAC), making it a more practical solution for high-speed optical communication. This advancement facilitates higher data transmission rates, proving the Kramers–Kronig relations modulation format as a promising alternative to existing methods. Its potential for enhancing the efficiency and capacity of optical communication systems is evident. Future research will focus on optimizing the modulation parameters and exploring their application in more complex scenarios, such as high-speed underwater visible light communication systems, where advanced modulation formats are crucial for overcoming bandwidth limitations. Full article

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I²C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation. Full article

Underwater wireless optical communication (UWOC) has emerged as a key enabler for Internet of Underwater Things (IoUT) and autonomous sensing networks, but its reliability is severely affected by salinity-induced attenuation, scattering, and turbulence. This work presents a high-speed and salinity-resilient UWOC architecture that jointly exploits Polarization Division Multiplexing (PDM) and Multiple-Input Multiple-Output (MIMO) diversity to enhance link capacity and robustness in realistic oceanic conditions. Two 1 Gbps NRZ data channels at 1550 nm were transmitted using continuous-wave lasers and evaluated using a hybrid OptiSystem–MATLAB simulation framework with full channel modeling of absorption, scattering, turbulence, and salinity (32–36 ppt). Results reveal that the proposed PDM-MIMO system achieves more than an order-of-magnitude bit-error-rate (BER) reduction compared with non-MIMO or single-polarization baselines, maintaining acceptable BER levels up to 20 m. Performance degradation with increasing salinity is quantified, and results confirm that combined PDM and spatial diversity effectively mitigate salinity-induced losses. The presented design demonstrates a viable and scalable solution for next-generation underwater sensing and communication networks in coastal and deep-sea ecosystems. Full article

In flight tests, to meet the requirements of consistent acquisition and storage of multiple targets, multiple systems, and multiple data types, various data types are processed into Pulse Code Modulation (PCM) data streams using PCM encoding for storage. Aiming at the requirement of real-time storage of high-bit-rate PCM data streams, a large-capacity storage system based on Serial Advanced Technology Attachment 3.0 (SATA3.0) is designed. The system uses the Kintex 7 series Field-Programmable Gate Array (FPGA) as the control core, receives PCM data streams through the Low-Voltage Differential Signaling (LVDS) low-voltage differential interface, stores the received PCM data streams into the mSATA disk via the SATA3.0 transmission bus, and transmits the stored data back to the host computer through the USB3.0 interface for analysis. Meanwhile, to solve the problem of complex data export, the storage system constructs a FAT32 file system through the MicroBlaze soft core to optimize the management and operation of the large-capacity storage system. Test results show that the storage system can perform stable high-rate storage at −40 °C~80 °C. Full article

Millimeter-wave (mmWave) massive multiple-input, multiple-output (MIMO) systems are a cornerstone technology for integrated sensing and communication (ISAC) in sixth-generation (6G) mobile networks. These systems provide high-capacity backhaul while simultaneously enabling high-resolution environmental sensing. However, accurate channel estimation remains highly challenging due to intrinsic noise sensitivity and clustered sparse multipath structures. These challenges are particularly severe under limited pilot resources and low signal-to-noise ratio (SNR) conditions. To address these difficulties, this paper proposes HASwinNet, a deep learning (DL) framework designed for mmWave channel denoising. The framework integrates a hierarchical Swin Transformer encoder for structured representation learning. It further incorporates two complementary branches. The first branch performs sparse token extraction guided by angular-domain significance. The second branch focuses on angular-domain refinement by applying discrete Fourier transform (DFT), squeeze-and-excitation (SE), and inverse DFT (IDFT) operations. This generates a mask that highlights angularly coherent features. A decoder combines the outputs of both branches with a residual projection from the input to yield refined channel estimates. Additionally, we introduce an angular-domain perceptual loss during training. This enforces spectral consistency and preserves clustered multipath structures. Simulation results based on the Saleh–Valenzuela (S–V) channel model demonstrate that HASwinNet achieves significant improvements in normalized mean squared error (NMSE) and bit error rate (BER). It consistently outperforms convolutional neural network (CNN), long short-term memory (LSTM), and U-Net baselines. Furthermore, experiments with reduced pilot symbols confirm that HASwinNet effectively exploits angular sparsity. The model retains a consistent advantage over baselines even under pilot-limited conditions. These findings validate the scalability of HASwinNet for practical 6G mmWave backhaul applications. They also highlight its potential in ISAC scenarios where accurate channel recovery supports both communication and sensing. Full article

The merging of the Internet of Things (IoT) and Artificial Intelligence (AI) advances has intensified challenges related to data authenticity and security. These advancements necessitate a multi-layered security approach to ensure the security, reliability, and integrity of critical infrastructure and intelligent surveillance systems. This paper proposes a two-layered security approach that combines a discrete cosine transform least significant bit 2 (DCT-LSB-2) with artificial neural networks (ANNs) for data forensic validation and mitigating deepfakes. The proposed model encodes validation codes within the LSBs of cover images captured by an IoT camera on the sender side, leveraging the DCT approach to enhance the resilience against steganalysis. On the receiver side, a reverse DCT-LSB-2 process decodes the embedded validation code, which is subjected to authenticity verification by a pre-trained ANN model. The ANN validates the integrity of the decoded code and ensures that only device-originated, untampered images are accepted. The proposed framework achieved an average SSIM of 0.9927 across the entire investigated embedding capacity, ranging from 0 to 1.988 bpp. DCT-LSB-2 showed a stable Peak Signal-to-Noise Ratio (average 42.44 dB) under various evaluated payloads ranging from 0 to 100 kB. The proposed model achieved a resilient and robust multi-layered data forensic validation system. Full article

The development and use of large-scale quantum computers relies on integrating quantum error-correcting (QEC) schemes into the quantum computing pipeline. A fundamental part of the QEC protocol is the decoding of the syndrome to identify a recovery operation with a high success rate. In this work, we implement a decoder that finds the recovery operation with the highest success probability by mapping the decoding problem to a spin system and using Population Annealing to estimate the free energy of the different error classes. We study the decoder performance on a 4.8.8 color code lattice under different noise models, including code capacity with bit-flip and depolarizing noise, and phenomenological noise, which considers noisy measurements, with performance reaching near-optimal thresholds for bit-flip and depolarizing noise, and the highest reported threshold for phenomenological noise. This decoding algorithm can be applied to a wide variety of stabilizer codes, including surface codes and quantum Low-Density Parity Check (qLDPC) codes. Full article