Search Results (74)

Covert channels enable hidden communication that poses significant security risks, particularly when smartphones are used as transmitters. This paper presents the first end-to-end implementation and evaluation of an electromagnetic (EM) covert channel on modern Samsung Galaxy S21, S22, and S23 smartphones (Samsung Electronics Co., Ltd., Suwon, Republic of Korea). We first demonstrate that a previously proposed method relying on zero-volume playback is no longer effective on these devices. Through a detailed analysis of EM emissions in the 0.1–2.5 MHz range, we discovered that consistent, volume-independent signals can be generated by exploiting the hardware’s recovery delay after silent audio playback. Based on these findings, we developed a complete system comprising a stealthy Android application for transmission, a time-based modulation scheme, and a demodulation technique designed around the characteristics of the generated signals to ensure reliable reception. The channel’s reliability and robustness were validated through evaluations of modulation time, probe distance, and message length. Experimental results show that the maximum error-free bit rate (bits per second, bps) reached 0.558 bps on Galaxy S21 and 0.772 bps on Galaxy S22 and Galaxy S23. Reliable communication was feasible up to 0.5 cm with a near-field probe, and a low alignment-aware bit error rate (BER) was maintained even for 100-byte messages. This work establishes a practical threat, and we conclude by proposing countermeasures to mitigate this vulnerability. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article

The integration of low Earth orbit (LEO) satellite constellations into the Global Navigation Satellite System (GNSS) has emerged as a prominent research focus, as LEO satellites can significantly enhance the precision of GNSS positioning, navigation, and timing (PNT) services. In the design of LEO navigation constellations, the development of an efficient broadcast ephemeris model is critical for delivering high-accuracy navigation solutions. This study extends the conventional 16-parameter Keplerian broadcast ephemeris model by proposing enhanced 18-, 20-, 22-, and 24-parameter models, ensuring compatibility with existing GNSS ephemeris standards. The performance of these models was evaluated using precise science orbit from five satellites at varying altitudes, ranging from 320 km to 1336 km. By analyzing fitting errors, Signal-in-Space Range Error (SISRE), and Message Size Bits (MSB) across different fitting arc durations and parameter counts, the optimal model configuration was identified. The results demonstrate that the 22-parameter model, which was constructed by augmenting the standard 16-parameter ephemeris with $(\dot{a}$, $\dot{n}$, $Crs3$, $Crc3$, $Crs1$, $Crc1$) delivers the best balance of accuracy and efficiency. With a fitting arc length of 20 min, the SISRE for the GRACE-A (320 km), GRACE-C (475 km), Sentinel-2A (786 km), HY-2A (966 km), and Sentinel-6A (1336 km) satellites were measured at 8.88 cm, 6.21 cm, 2.87 cm, 2.11 cm, and 0.75 cm, respectively. Meanwhile, the corresponding MSB remained compact at 501, 490, 491, 487, and 476 bits. These findings confirm that the proposed 22-parameter broadcast ephemeris model meets the stringent accuracy requirements for next-generation LEO-augmented GNSSs, paving the way for enhanced global navigation services. Full article

The enhanced Loran (eLoran) system, a critical terrestrial backup for the Global Satellite Navigation System (GNSS), traditionally utilizes a Reed-Solomon (RS) code for its data communication, which presents limitations in error performance, particularly due to its decoding method. This paper introduces a significant advancement by proposing the replacement of the conventional RS code with a 128-ary polar code, which is designed to maintain compatibility with the established 128-ary Pulse Position Modulation (PPM) scheme integral to eLoran’s positioning function. A Soft–Soft (SS) demodulation method, based on a correlation receiver, is developed to provide the requisite soft information for the effective Successive Cancellation List (SCL) decoding of the 128-ary polar code. Comprehensive simulations demonstrate that the proposed 128-ary polar code with SS demodulation achieves a substantial error performance improvement, yielding an approximate 9.3 dB gain at the 0.01 FER level over the RS code in eLoran data communication with EPD-MD demodulation. Additionally, the proposed scheme improves data transmission efficiency—either reducing transmission duration by 2/3 or increasing message bit number by 250% for comparable error performance—without impacting the system’s primary positioning capabilities. Full article

This paper proposes a GNSS-compatible method for characterizing the motion of UAV-based navigation enhancement platforms, designed to provide reliable navigation and positioning services in emergency scenarios where GNSS signals are unavailable or severely degraded. The method maps UAV trajectories into standard GNSS navigation messages by establishing a correspondence between ephemeris parameters and platform positions through coordinate transformation and Taylor series expansion. To address modeling inaccuracies, the approach incorporates truncation error analysis and motion-assumption compensation via parameter optimization. This design enables UAV-mounted pseudolite systems to broadcast GNSS-compatible signals without modifying existing receivers, significantly enhancing rapid deployment capabilities in complex or degraded environments. Simulation results confirm precise positional representation in static scenarios and robust error control under dynamic motion through higher-order modeling and optimized broadcast strategies. UAV flight tests demonstrated a theoretical maximum error of 0.4262 m and an actual maximum error of 3.1878 m under real-world disturbances, which is within operational limits. Additional experiments confirmed successful message parsing with standard GNSS receivers. The proposed method offers a lightweight, interoperable solution for integrating UAV platforms into GNSS-enhanced positioning systems, supporting timely and accurate navigation services in emergency and disaster relief operations. Full article

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process. Full article

Electrically charged particles present in this layer of the Earth’s atmosphere can alter radio waves, such as those from GPS, Galileo, or BeiDou, resulting in non-estimated errors with respect to the available navigation models for the end user. For most positioning algorithms based in sequential filters, this effect is translated into a slow convergence towards a solution around the decimeter error level. If we consider that the ionosphere’s effect varies based on the user’s location and solar activity due to the atmosphere particle composition, it becomes clear that a global accurate model, valid across wide areas accounting for different seasons and timespans, is, at the very least, quite challenging. The focus of this paper is the demonstration of a global ionosphere model designed to improve the positioning accuracy of the end user through the estimation of ionospheric corrections to the broadcasted navigation message. Mathematically, this method is based on a spherical harmonic expansion model. This approach has the advantage of reducing the dependency from a highly densified station network where the ionosphere delay must be constantly estimated in dozens of locations, in favor of a simplified model that barely needs to be adjusted with a limited set of real-time data (around 40 stations). In this case, GMV’s global station network was used, which comprises geodetic-grade receivers tracking the signal in open-sky locations around the globe. The global ionospheric model is configured to process signals from GPS and Galileo constellations. To evaluate the performances of this model on the final user position estimation, several precise point positioning (PPP) solutions were computed at different locations. The results were compared with PPP solutions calculated without ionospheric corrections at the same stations. The goal of this paper is to show the significant performance improvement observed with the implementation of the global model. Full article

The growing adoption of supercomputers across various scientific disciplines, particularly by researchers without a background in computer science, has intensified the demand for parallel applications. These applications are typically developed using a combination of programming models within languages such as C, C++, and Fortran. However, modern multi-core processors and accelerators necessitate fine-grained control to achieve effective parallelism, complicating the development process. To address this, developers commonly utilize high-level programming models such as Open Multi-Processing (OpenMP), Open Accelerators (OpenACCs), Message Passing Interface (MPI), and Compute Unified Device Architecture (CUDA). These models may be used independently or combined into dual- or tri-model applications to leverage their complementary strengths. However, integrating multiple models introduces subtle and difficult-to-detect runtime errors such as data races, deadlocks, and livelocks that often elude conventional compilers. This complexity is exacerbated in applications that simultaneously incorporate MPI, OpenMP, and CUDA, where the origin of runtime errors, whether from individual models, user logic, or their interactions, becomes ambiguous. Moreover, existing tools are inadequate for detecting such errors in tri-model applications, leaving a critical gap in development support. To address this gap, the present study introduces a static analysis tool designed specifically for tri-model applications combining MPI, OpenMP, and CUDA in C++-based environments. The tool analyzes source code to identify both actual and potential runtime errors prior to execution. Central to this approach is the introduction of error dependency graphs, a novel mechanism for systematically representing and analyzing error correlations in hybrid applications. By offering both error classification and comprehensive static detection, the proposed tool enhances error visibility and reduces manual testing effort. This contributes significantly to the development of more robust parallel applications for high-performance computing (HPC) and future exascale systems. Full article

Every time the proper functioning of the vehicles must be guaranteed, as well as safety and efficiency. To achieve this, some expensive solutions are used, with few connectivity options and that fail to meet consumer demand. This paper presents a low-cost hardware system for the design of a real-time communication protocol between the electronic control unit (ECU) of a vehicle and a remote server based in a embedded system. A dual tone multi-frequency (DTMF) approach is implemented, so error codes (DTCs) are always available on a unit equipped with this system. The vehicle-to-infrastructure (V2I) communication protocol through voice channels is provided by cellular technology infrastructure, in which primary information is shared to monitor vehicles. With real-time data transmission, communication is established through a voice phone call between the vehicle’s ECU and the destination server, communicating the DTC codes. The system shows that the communication protocol has an effectiveness of 78.23%, which means that with the use of 2G technology, which is active and operating in many regions, it allows the information with the data to be received by the receiving user. Through this implemented system, it is ensured that if a vehicle suffers an accident or stops due to a mechanical failure in a region where there is no cellular technology coverage, information or a message can be sent so that through communication the rescue can be carried out using an cellular technology coverage. Full article

We propose two decoding algorithms for quasi-cyclic LDPC codes (QC-LDPC) and implement the more efficient one in this paper. These algorithms depend on the split row for the layered decoding method applied to the Min-Sum (MS) algorithm. We designate the first algorithm “Split-Row Layered Min-Sum” (SRLMS), and the second algorithm “Split-Row Threshold Layered Min-Sum” (SRTLMS). A threshold message passes from one partition to another in SRTLMS, minimizing the gap from the MS and achieving a binary error rate of 3 × 10⁻⁵ with Imax = 4 as the maximum number of iterations, resulting in a decrease of 0.25 dB. The simulation’s findings indicate that the SRTLMS is the most efficient variant decoding algorithm for LDPC codes, thanks to its compromise between performance and complexity. This paper presents the two invented algorithms and a comprehensive study of the co-design and implementation of the SRTLMS algorithm. We executed the implementation on a Xilinx Kintex-7 XC7K160 FPGA, achieving a maximum operating frequency of 101 MHz and a throughput of 606 Mbps. Full article

Group chat socialization is increasingly central to online activities, yet design strategies to enhance this experience remain underexplored. This study builds on the Stimuli–Organism–Response (SOR) framework to examine how usability, chat rhythm, and user behavior influence emotions and participation in group chats. Using data from 546 users in China, a relevant demographic given the dominance of platforms like WeChat in both social and professional settings, we uncover insights that are particularly applicable to highly connected digital environments. Our analysis shows significant relationships between usability (γ = 0.236, p < 0.001), chat rhythm (γ = 0.172, p < 0.001), user behavior (γ = 0.214, p < 0.001), and emotions, which directly impact participation. Positive emotions (γ = 0.128, p < 0.05) boost participation, while negative emotions (γ = −0.144, p < 0.01), particularly when linked to user behaviors, reduce it. Additionally, we discussed the mediating effects, notably that usability significantly impacts participation through positive emotions, while user behavior exerts a significant influence on participation through negative emotions. This research offers actionable design strategies, such as tailoring sensory inputs to reduce cognitive load and implementing reward systems to motivate participation. Positive feedback mechanisms enhance engagement by leveraging the brain’s reward systems, while optimized error messages can minimize frustration. These insights, which are particularly relevant for China’s active group chat culture, provide a framework to improve platform design and contribute valuable findings to the broader HCI field. Full article

Parallel Concatenated Block (PCB) codes are conventionally represented as high-rate codes with low error correcting capability. To form a reliable and outstanding code, this paper presents a modification on the structure of PCB codes, which is accomplished by encoding some parity bits of one of their component codes. For the newly proposed code, named as the braided code, non-stuff bit-based convolutional interleavers are applied, aiming to minimize the design complexity while ensuring the proper permutations of the original message and selected parity bits. To precisely determine the error correcting capability, a tight bound for the minimum weight of braided code is presented. Additionally, further analyses are provided, which verify iterative decoding performance and the complexity of the constructed code. It is concluded that an outstanding braided code is formed by utilizing a reasonable number of iterations applied at its decoding processes, while maintaining its design complexity at a level similar to other well-known codes. The significant performance of short and long-length-based braided codes is evident in both waterfall and error floor regions. Full article

Vehicular ad hoc networks (VANETs), which are the backbone of intelligent transportation systems (ITSs), facilitate critical data exchanges between vehicles. This necessitates secure transmission, which requires guarantees of message availability, integrity, source authenticity, and user privacy. Moreover, the traceability of network participants is essential as it deters malicious actors and allows lawful authorities to identify message senders for accountability. This introduces a challenge: balancing privacy with traceability. Conditional privacy-preserving authentication (CPPA) schemes are designed to mitigate this conflict. CPPA schemes utilize cryptographic protocols, including certificate-based schemes, group signatures, identity-based schemes, and certificateless schemes. Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. Combining certificateless schemes with batch verification leads to certificateless aggregate signature (CLAS) schemes. In this paper, cryptanalysis of Xiong’s CLAS scheme revealed its vulnerabilities to partial key replacement and identity replacement attacks, alongside mathematical errors in the batch verification process. Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). The signature and verification modules are also redesigned to address the identified vulnerabilities in Xiong’s scheme. Additionally, we implemented the small exponents test within the batch verification module to achieve Type III security. While this enhances security, it introduces a slight performance trade-off. Our scheme has been subjected to formal security and performance analyses to ensure robustness. Full article

Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a defense scheme which detects and blacklists a fake base station and then, informed by the detection, avoids it through link routing for connectivity availability. For detection and blacklisting, our scheme uses the real-time information of both the time duration and the number of request transmissions, the features of which are directly impacted by the fake base station’s threat and which have not been studied in previous research. Upon detection, our scheme takes an active measure called link routing, which is a novel concept in mobile/4G/5G networking, where the user equipment routes the connectivity request to another base station. To defend against a Sybil-capable fake base station, we use a history–reputation-based link routing scheme for routing and base station selection. We implemented both the base station and the user on software-defined radios using open-source 5G software (srsRAN v23.10 and Open5GS v2.6.6) for validation. We varied the base station implementation to simulate legitimate vs. faulty but legitimate vs. fake and malicious base stations, where a faulty base station notifies the user of the connectivity disruption and releases the session, while a fake base station continues to hold the session. We empirically analyzed the detection and identification thresholds, which vary with the fake base station’s power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives, to avoid blacklisting a temporarily faulty base station that cannot provide connectivity at the time. Furthermore, our link routing scheme enables the base station to switch in order to restore the connectivity availability and limit the threat impact. We also discuss future directions to facilitate and encourage R&D in securing telecommunications and base station security. Full article

Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural networks to identify the crimes happening in the real world. Through our approach, it will be easier for police agencies to discover and assess criminal activity more quickly using our method, which will reduce the burden on their staff. In this paper, we aim to provide anomaly detection using surveillance videos as input specifically for the crimes of arson, burglary, stealing, and vandalism. It will provide an efficient and adaptable crime-detection system if integrated across the smart city infrastructure. In our project, we trained multiple accurate deep learning models for object detection and crime classification for arson, burglary and vandalism. For arson, the videos were trained using YOLOv5. Similarly for burglary and vandalism, we trained using YOLOv7 and YOLOv6, respectively. When the models were compared, YOLOv7 performed better with the highest mAP of 87. In this, we could not compare the model’s performance based on crime type because all the datasets for each crime type varied. So, for arson YOLOv5 performed well with 80% mAP and for vandalism, YOLOv6 performed well with 86% mAP. This paper designed an automatic identification of crime types based on camera or surveillance video in the absence of a monitoring person, and alerts registered users about crimes such as arson, burglary, and vandalism through an SMS service. To detect the object of the crime in the video, we trained five different machine learning models: Improved YOLOv5 for arson, Faster RCNN and YOLOv7 for burglary, and SSD MobileNet and YOLOv6 for vandalism. Other than improved models, we innovated by building ensemble models of all three crime types. The main aim of the project is to provide security to the society without human involvement and make affordable surveillance cameras to detect and classify crimes. In addition, we implemented the Web system design using the built package in Python, which is Gradio. This helps the registered user of the Twilio communication tool to receive alert messages when any suspicious activity happens around their communities. Full article