Search Results (106)

As the use of Internet of Things (IoT) devices becomes extensive, ensuring their security has become a critical issue for both individuals and organizations, particularly as these devices collect, transmit, and analyze diverse data. The firmware of IoT devices plays a key role in ensuring system security; any vulnerabilities in the firmware can expose the system to threats such as hacking or malware infections. Consequently, fuzzing is used to analyze firmware vulnerabilities during the update process. However, conventional single-core and random fuzzing-based firmware vulnerability analysis techniques suffer from low efficiency, limited security, and high memory usage. Each time the firmware is updated, the entire file—including previously analyzed code—must be reanalyzed. Moreover, given that the firmware is not layered, unaffected code segments are redundantly reanalyzed. To address these limitations, this study proposes a dual-core-based hierarchical partial fuzzing technique for wireless networks using dual cores. Experimental results show that the proposed technique detects 11 more unique crashes within 300 s and finds 2435 more total crashes than that of the conventional scheme. It also reduces memory usage by 35 KiB. The proposed technique improves the speed, effectiveness, and reliability of firmware updates and vulnerability detection. Full article

Titanium dioxide (TiO₂) is a wide-bandgap semiconductor material with broad application potential, known for its excellent photocatalytic performance, high chemical stability, low cost, and non-toxicity. These properties make it highly attractive for applications in photovoltaic energy, environmental remediation, and optoelectronic devices. For instance, TiO₂ is widely used as a photocatalyst for hydrogen production via water splitting and for degrading organic pollutants, thanks to its efficient photo-generated electron–hole separation. Additionally, TiO₂ exhibits remarkable performance in dye-sensitized solar cells and photodetectors, providing critical support for advancements in green energy and photoelectric conversion technologies. Boron-doped diamond (BDD) is renowned for its exceptional electrical conductivity, high hardness, wide electrochemical window, and outstanding chemical inertness. These unique characteristics enable its extensive use in fields such as electrochemical analysis, electrocatalysis, sensors, and biomedicine. For example, BDD electrodes exhibit high sensitivity and stability in detecting trace chemicals and pollutants, while also demonstrating excellent performance in electrocatalytic water splitting and industrial wastewater treatment. Its chemical stability and biocompatibility make it an ideal material for biosensors and implantable devices. Research indicates that the combination of TiO₂ nanostructures and BDD into heterostructures can exhibit unexpected optical and electrical performance and transport behavior, opening up new possibilities for photoluminescence and rectifier diode devices. However, applications based on this heterostructure still face challenges, particularly in terms of photodetector, photoelectric emitter, optical modulator, and optical fiber devices under high-temperature conditions. This article explores the potential and prospects of their combined heterostructures in the field of optoelectronic devices such as photodetector, light emitting diode (LED), memory, field effect transistor (FET) and sensing. TiO₂/BDD heterojunction can enhance photoresponsivity and extend the spectral detection range which enables stability in high-temperature and harsh environments due to BDD’s thermal conductivity. This article proposes future research directions and prospects to facilitate the development of TiO₂ nanostructured materials and BDD-based heterostructures, providing a foundation for enhancing photoresponsivity and extending the spectral detection range enables stability in high-temperature and high-frequency optoelectronic devices field. Further research and exploration of optoelectronic devices based on TiO₂-BDD heterostructures hold significant importance, offering new breakthroughs and innovations for the future development of optoelectronic technology. Full article

As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time. Full article

Perovskite quantum dots (PVK QDs) are gaining significant attention as potential materials for next-generation memory devices leveraged by their ion dynamics, quantum confinement, optoelectronic synergy, bandgap tunability, and solution-processable fabrication. In this review paper, we explore the fundamental characteristics of organic/inorganic halide PVK QDs and their role in resistive switching memory architectures. We provide an overview of halide PVK QDs synthesis techniques, switching mechanisms, and recent advancements in memristive applications. Special emphasis is placed on the ionic migration and charge trapping phenomena governing resistive switching, along with the prospects of photonic memory devices that leverage the intrinsic photosensitivity of PVK QDs. Despite their advantages, challenges such as stability, scalability, and environmental concerns remain critical hurdles. We conclude this review with insights into potential strategies for enhancing the reliability and commercial viability of PVK QD-based memory technologies. Full article

The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities. Full article

By combining density functional theory with the non-equilibrium Green’s function method, we conducted a first-principles investigation of spin-dependent transport properties in a molecular device featuring a dynamic covalent chemical bridge connected to zigzag graphene nanoribbon electrodes. The effects of spin-filtering and spin-rectifying on the I–V characteristics are revealed and explained for the proposed molecular device. Interestingly, our results demonstrate that all three devices exhibit significant single-spin-filtering behavior in parallel (P) magnetization and dual-spin-filtering effects in antiparallel (AP) configurations, achieving nearly 100% spin-filtering efficiency. At the same time, from the I–V curves, we find that there is a weak negative differential resistance effect. Moreover, a high rectifying ratio is found for spin-up electron transport in AP magnetization, which is explained by the transmission spectrum and local density of state. The fundamental mechanisms governing these phenomena have been elucidated through a systematic analysis of spin-resolved transmission spectra and spin-polarized electron transport pathways. These results extend the design principles of spin-controlled molecular electronics beyond graphene-based systems, offering a universal strategy for manipulating spin-polarized currents through dynamic covalent interfaces. The nearly ideal spin-filtering efficiency and tunable rectification suggest potential applications in energy-efficient spintronic logic gates and non-volatile memory devices, while the methodology provides a framework for optimizing spin-dependent transport in hybrid organic–inorganic nanoarchitectures. Our findings suggest that such systems are promising candidates for future spintronic applications. Full article

Satellite and edge computing designers develop algorithms that restrict resource utilization and execution time. Among these design efforts, optimizing Fast Fourier Transform (FFT), key to many tasks, has led mainly to in-place FFT-specific hardware accelerators. Aiming at improving the FFT performance on processors and computing devices with limited resources, the current paper enhances the efficiency of the radix-2 FFT by exploring the benefits of an in-place technique. First, we present the advantages of organizing the single memory bank of processors to store two (2) FFT elements in each memory address and provide parallel load and store of each FFT pair of data. Second, we optimize the floating point (FP) and block floating point (BFP) configurations to improve the FFT Signal-to-Noise (SNR) performance and the resource utilization. The resulting techniques reduce the memory requirements by two and significantly improve the time performance for the overall prevailing BFP representation. The execution of inputs ranging from 1K to 16K FFT points, using 8-bit or 16-bit as FP or BFP numbers, on the space-proven Atmel AVR32 and Vision Processing Unit (VPU) Intel Movidius Myriad 2, the edge device Raspberry Pi Zero 2W and a low-cost accelerator on Xilinx Zynq 7000 Field Programmable Gate Array (FPGA), validates the method’s performance improvement. Full article

Modern computer applications have become highly data-intensive, giving rise to an increase in data traffic between the processor and memory units. Computing-in-Memory (CiM) has shown great promise as a solution to this aptly named von Neumann bottleneck problem by enabling computation within the memory unit and thus reducing data traffic. Many simulation tools in the literature have been proposed to enable the design space exploration (DSE) of these novel computer architectures as researchers are in need of these tools to test their designs prior to fabrication. This paper presents a collection of classical nonvolatile memory (NVM) and CiM simulation tools to showcase their capabilities, as presented in their respective analyses. We provide an in-depth overview of DSE, emerging NVM device technologies, and popular CiM architectures. We organize the simulation tools by design-level scopes with respect to their focus on the devices, circuits, architectures, systems/algorithms, and applications they support. We conclude this work by identifying the gaps within the simulation space. Full article

The traditional von Neumann architecture encounters significant limitations in computational efficiency and energy consumption, driving the development of neuromorphic devices. The optoelectronic synaptic device serves as a fundamental hardware foundation for the realization of neuromorphic computing and plays a pivotal role in the development of neuromorphic chips. This study develops a ternary heterojunction synaptic transistor based on perovskite quantum dots to tackle the critical challenge of synaptic weight modulation in organic synaptic devices. Compared to binary heterojunction synaptic transistor, the ternary heterojunction synaptic transistor achieves an enhanced hysteresis window due to the synergistic charge-trapping effects of acceptor material and perovskite quantum dots. The memory window decreases with increasing source-drain voltage (V_DS) but expands with prolonged program/erase time, demonstrating effective carrier trapping modulation. Furthermore, the device successfully emulates typical photonic synaptic behaviors, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). This work provides a simplified strategy for high-performance optoelectronic synaptic transistors, showcasing significant potential for neuromorphic computing and adaptive intelligent systems. Full article

In recent years, high-power microwave (HPM) technology has developed rapidly. However, the current research mainly focuses on how to improve its performance and its impact on electronic devices, and there has been relatively little research on its effects on organisms. In particular, the research on the biological effects of HPMs in the X-band is even more limited. The purpose of this paper is to conduct a study on the effects of HPMs in the X-band with a frequency of 9.375 GHz on mood, learning, and cognitive abilities, as well as the antioxidant defense system. Upon observation, it was noted that the mice in the exposed groups, when compared to the control group, did not display significant signs of depression or anxiety. Furthermore, their learning capabilities, memory retention, and cognitive functions remained intact and were not adversely affected. The results of oxidative-stress-related indicators in serum and brain tissue showed increased levels of antioxidant enzymes including superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px), reduced levels of protein carbonyl (PCO) and malondialdehyde (MDA), and no significant changes in reactive oxygen species (ROS). In summary, acute exposure to 9.375 GHz HPM did not cause significant damage to the organisms, and the body could defend against the acute stress caused by HPMs through its own antioxidant system. This investigation provides substantial theoretical foundations and robust experimental evidence for establishing safety parameters and potential biomedical applications of microwave radiation within defined exposure limits. Full article

In this work, floating-gate organic field-effect transistor memory using the n-type semiconductor poly-{[N,N′-bis(2-octyldodecyl) naphthalene-1,4,5,8-bis (dicarbo- ximide)-2,6-dili]-alt-5,5′-(2,2′-bithiophene)} (N2200) as a charge-trapping layer is presented. With the assistance of a technology computer-aided design (TCAD) tool (Silvaco-Atlas), the storage characteristics of the device are numerically simulated by using the carrier injection and Fower–Nordheim (FN) tunneling models. The shift in the transfer characteristic curves and the charge-trapping mechanism after programming/erasing (P/E) operations under different P/E voltages and different pulse operation times are discussed. The impacts of different thicknesses of the tunneling layer on storage characteristics are also analyzed. The results show that the memory window with a tunneling layer thickness of 8 nm is 16.1 V under the P/E voltage of ±45 V, 5 s. After 1000 cycle tests, the memory shows good fatigue resistance, and the read current on/off ratio reaches 10³. Full article

Memristors are promising candidates for next-generation non-volatile memory devices, offering low power consumption and high-speed switching capabilities. However, conventional metal oxide-based memristors are constrained by fabrication complexity and high costs, limiting their commercial viability. Organic–inorganic hybrid perovskites (OIHPs), known for their facile solution processability and unique ionic–electronic conductivity, provide an attractive alternative. This study presents a conjugated polyelectrolyte (CPE), PhNa-1T, as an interlayer for OIHP memristors to enhance the high-resistance state (HRS) performance. A post-treatment process using n-octylammonium bromide (OABr) was further applied to optimize the interlayer properties. Devices treated with PhNa-1T/OABr achieved a significantly improved ON/OFF ratio of 2150, compared to 197 for untreated devices. Systematic characterization revealed that OABr treatment improved film morphology, reduced crystallite strain, and optimized energy level alignment, thereby reinforcing the Schottky barrier and minimizing current leakage. These findings highlight the potential of tailored interlayer engineering to improve OIHP-based memristor performance, offering promising prospects for applications in non-volatile memory technologies. Full article

As IoT devices with microcontroller (MCU)-based firmware become more common in our lives, memory corruption vulnerabilities in their firmware are increasingly targeted by adversaries. Fuzzing is a powerful method for detecting these vulnerabilities, but it poses unique challenges when applied to IoT devices. Direct fuzzing on these devices is inefficient, and recent efforts have shifted towards creating emulation environments for dynamic firmware testing. However, unlike traditional software, firmware interactions with peripherals that are significantly more diverse presents new challenges for achieving scalable full-system emulation and effective fuzzing. This paper reviews 27 state-of-the-art works in MCU-based firmware emulation and its applications in fuzzing. Instead of classifying existing techniques based on their capabilities and features, we first identify the fundamental challenges faced by firmware emulation and fuzzing. We then revisit recent studies, organizing them according to the specific challenges they address, and discussing how each specific challenge is addressed. We compare the emulation fidelity and bug detection capabilities of various techniques to clearly demonstrate their strengths and weaknesses, aiding users in selecting or combining tools to meet their needs. Finally, we highlight the remaining technical gaps and point out important future research directions in firmware emulation and fuzzing. Full article

Due to their ability to reversibly isomerize under the influence of external stimuli, spiropyrans represent the most interesting class of organic photochromic molecules. The photochromic properties of the isomeric forms of spiropyrans differ significantly from each other, which makes it possible to use these photochromes as sensors, optoelectronic and holographic devices, memory elements, etc. Also, an undoubted advantage of spiropyrans compared to other classes of organic photochromes is the relative ease of their preparation and chemical transformation. At the same time, modification of the structure of spiropyrans by introducing various functional groups opens up great synthetic possibilities for obtaining new photochromic molecules with various spectral-kinetic characteristics. In the development of research aimed at expanding the boundaries of the use of spirophotochromic compounds, in order to obtain new light-controlled materials with different characteristics, as well as to study the influence of functional groups in the spirophotochromic molecule on the spectral and photochromic properties, we have synthesized a new spiropyran. In this work, we synthesized a new salt of photochromic spiropyran containing various functional groups (–CHO, –NO₂, –OCH₃, –(CH₂)₅N(CH₃)₂*HBr), capable of reversibly responding to external influences. Photoinduced transformations and the spectral and kinetic characteristics of the synthesized compound were studied. Full article

The simulation of realistic systems plays a crucial role in modern sciences. Complex organs such as the brain can be described by mathematical models to reproduce biological behaviors. In the brain, the hippocampus is a critical region for memory and learning. In the literature, a model to reproduce the memory consolidation mechanism has been proposed. This model exhibits a high degree of biological realism, though it is accompanied by a significant increase in computational complexity. This paper proposes the development of parallel simulation targeting different devices, namely multicore CPUs and GPUs. The experiments highlighted that the biological realism is maintained, together with a significant decrease of the processing times. Finally, the conducted analysis highlights that the GPU is one of the most suitable technologies for this kind of simulation. Full article