Search Results (965)

The rapid advancement of terahertz (THz) communication systems has positioned this technology as a key enabler for next-generation telecommunication networks, including 6G, secure communications, and hybrid wireless-optical systems. This review comprehensively analyzes THz communication, emphasizing its integration with free-space optical (FSO) systems to overcome conventional bandwidth limitations. While THz-FSO technology promises ultra-high data rates, it is significantly affected by atmospheric absorption, particularly absorption beyond 500 GHz, where the attenuation exceeds 100 dB/km, which severely limits its transmission range. However, the presence of a lower-loss transmission window at 680 GHz provides an opportunity for optimized THz-FSO communication. This paper explores recent developments in high-power THz sources, such as quantum cascade lasers, photonic mixers, and free-electron lasers, which facilitate the attainment of ultra-high data rates. Additionally, adaptive optics, machine learning-based beam alignment, and low-loss materials are examined as potential solutions to mitigating signal degradation due to atmospheric absorption. The integration of THz-FSO systems with optical and radio frequency (RF) technologies is assessed within the framework of software-defined networking (SDN) and multi-band adaptive communication, enhancing their reliability and range. Furthermore, this review discusses emerging applications such as self-driving systems in 6G networks, ultra-low latency communication, holographic telepresence, and inter-satellite links. Future research directions include the use of artificial intelligence for network optimization, creating energy-efficient system designs, and quantum encryption to obtain secure THz communications. Despite the severe constraints imposed by atmospheric attenuation, the technology’s power efficiency, and the materials that are used, THz-FSO technology is promising for the field of ultra-fast and secure next-generation networks. Addressing these limitations through hybrid optical-THz architectures, AI-driven adaptation, and advanced waveguides will be critical for the full realization of THz-FSO communication in modern telecommunication infrastructures. Full article

This paper presents an ultra-low-power, inverter-based, universal Gm-C filter capable of operating in multiple modes: voltage, current, transconductance, and trans-resistance. The proposed filter features orthogonal tunability of the center frequency (ω₀) and quality factor (Q). To achieve ultra-low power consumption, all transistors are biased in the subthreshold region with a supply voltage of 0.5 V. A Nauta inverter-based gm block is utilized as the operational transconductance amplifier (OTA), further enhancing power efficiency. The filter is capable of generating all filtering responses across a supply voltage ranges from 1.2 V down to 0.5 V. Moreover, the center frequency and quality factor can be tuned by adjusting capacitance values. The proposed Gm-C filter achieves a power consumption of 48 nW, with the center frequency ranging from 50.6 Hz to 1270 Hz. Full article

Ultra-high-temperature (>1500 °C) sensors play vital roles in ensuring operational excellence in variety of energy-related applications, such as power plant boilers and gas turbine engines. Crystalline sapphire fibers have enormous potential to replace conventional expensive precious metal (e.g., Pt/Rh)-based high-temperature (>1500 °C) sensors by offering higher environmental robustness and distributed sensing capabilities. However, a lack of proper cladding substantially compromises the performance of the sensor. To overcome this fundamental limitation, we develop a hybrid growing method to fabricate low-loss clad crystalline sapphire fibers. We grow a higher-refractive-index doped crystalline sapphire fiber core using the laser-heated pedestal growth (LHPG) method and lower-refractive-index undoped crystalline sapphire fiber cladding using the liquid-phase epitaxy (LPE) method. Furthermore, due to the existence of this cladding layer, a single mode of operation can be achieved at a core diameter size of 30 μm. The experimental results confirm that the grown clad crystalline sapphire fiber can survive in extremely high-temperature (>1500 °C) harsh environments due to the matched coefficient of thermal expansion (CTE) between the fiber core and the cladding. The numerical results also indicate a temperature sensing accuracy of 3.5 °C. This opens the door for developing point and distributed fiber sensor networks capable of enduring extremely harsh environments at extremely high temperatures. Full article

The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI₃, Fe₃GaTe₂, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures. Full article

Superhydrophobic metal wires have shown great application prospects in oil–water separation, anti-corrosion, anti-icing, and other fields due to their excellent water repellency. However, how to fabricate a superhydrophobic surface on ultra-fine metal wire remains a challenge. Here, we proposed a method using laser processing to efficiently fabricate superhydrophobic ultra-fine brass wire. Firstly, we analyzed the mechanism of the laser processing of curved surfaces and designed a controllable angle rotation fixture to avoid the machining error caused by secondary positioning in the machining process. Then, we investigated the influences of the laser power, scanning speed, and scanning times on the surface morphology and wettability of the ultra-fine brass wire. The optimal laser processing parameters were obtained: laser power of 6 W, scanning speed of 500 mm/s, and scanning time of 1. After low surface energy modification, the water contact angle and surface roughness Sa of the ultra-fine brass wire were 156° and 1.107 μm, respectively. This work is expected to enrich the theory and technology for fabricating superhydrophobic ultra-fine brass wire. Full article

Radio-frequency (RF) microwave wireless power transfer (WPT) offers an efficient means of delivering energy to a wide array of devices over long distances. Previous RF WPT systems faced significant challenges, including complex hardware and control systems, software deficiencies, insufficient rectification power, lack of high-performance substrate materials, and electromagnetic radiation hazards. Addressing these issues, this paper proposes the world’s first watt-level RF WPT system capable of intelligent continuous tracking and occlusion judgment. Our 5.8 GHz band RF WPT system integrates several advanced technologies, such as millimeter-precision lidar, the multi-object image recognition algorithm, the accurate 6-bit continuous beamforming algorithm, a compact 16-channel 32 W high-power transmitting system, a pair of ultra-low axial ratio circularly polarized antenna arrays, ultra-low-loss high-strength ceramic substrates, and a 2.4 W high-power Schottky diode array rectifier achieving a rectification efficiency of $66.8\%$. Additionally, we construct a platform to demonstrate the application of the proposed RF WPT system in battery-free vehicles, achieving unprecedented ${360}^{\circ }$ uninterrupted power supply to the battery-free vehicle. In summary, this system represents the most functionally complete RF WPT system to date, serving as a milestone for several critical fields such as smart living, transportation electrification, and battery-less/free societies. Full article

Growing demands for self-powered, low-maintenance devices—especially in sensor networks, wearables, and the Internet of Things—have intensified interest in capturing ultra-low-frequency ambient vibrations. This paper introduces a hybrid energy harvester that combines elastic buckling with magnetically induced forces, enabling programmable transitions among monostable, bistable, and multistable regimes. By tuning three key parameters—buckling amplitude, magnet spacing, and polarity offset—the system’s potential energy landscape can be selectively shaped, allowing the depth and number of potential wells to be tailored for enhanced vibrational response and broadened operating bandwidths. An energy-based modeling framework implemented via an in-house MATLAB^® R2024B code is presented to characterize how these parameters govern well depths, barrier heights, and snap-through transitions, while an inverse design approach demonstrates the practical feasibility of matching industrially relevant target force–displacement profiles within a constrained design space. Although the present work focuses on systematically mapping the static potential landscape, these insights form a crucial foundation for subsequent dynamic analyses and prototype validation, paving the way for advanced investigations into basins of attraction, chaotic transitions, and time-domain power output. The proposed architecture demonstrates modularity and tunability, holding promise for low-frequency energy harvesting, adaptive vibration isolation, and other nonlinear applications requiring reconfigurable mechanical stability. Full article

Sarcomas are rare malignant tumors of mesenchymal origin with a high misdiagnosis rate due to their heterogeneity and low incidence. Conventional diagnostic techniques, such as Fluorescence In Situ Hybridization (FISH) and Next-Generation Sequencing (NGS), have limitations in detecting structural variations (SVs), copy number variations (CNVs), and predicting clinical behavior. Optical genome mapping (OGM) provides high-resolution genome-wide analysis, improving sarcoma diagnosis and prognosis assessment. This study analyzed 53 sarcoma samples using OGM. Ultra-high molecular weight (UHMW) DNA was extracted from core and resection biopsies, and data acquisition was performed with the Bionano Saphyr platform. Bioinformatic pipelines identified structural variations, comparing them with known alterations for each sarcoma subtype. OGM successfully analyzed 62.3% of samples. Diagnostic-defining alterations were found in 95.2% of cases, refining diagnoses and revealing novel oncogenic and tumor suppressor gene alterations. The challenges included DNA extraction and quality issues from some tissue samples. Despite these limitations, OGM proved to be a powerful diagnostic and predictive tool for bone and soft tissue sarcomas, surpassing conventional methods in resolution and scope, enhancing the understanding of sarcoma genetics, and enabling better patient stratification and personalized therapies. Full article

Currently, autonomous flight control for unmanned aerial vehicles (UAVs) has become increasingly critical in remote-sensing applications, such as high-resolution data acquisition and road disease detection. However, this task also faces significant challenges, particularly the weak GNSS signals in flight areas and the complex flight environment. Furthermore, many existing autonomous-flight-control algorithms for UAVs are computationally demanding, which limits their deployment on embedded devices with constrained memory and processing power, thereby affecting both operational efficiency and the safety of UAV missions. To address these issues, we propose PISCFF-LNet, a lightweight road-extraction network that integrates prior knowledge and spatial contextual features. The network employs a dual-branch encoder architecture to separately extract spatial and contextual features, thus obtaining multi-dimensional feature representations. In addition, to enhance the integration of different features and improve the overall feature representation, we also introduce a feature-fusion module. To further enhance UAV performance, we introduce an improved ray-based eight neighborhood algorithm (RENA), which efficiently extracts road-edge information with a remarkably low latency of just 7 ms, providing accurate flight guidance and reducing misidentification. To provide a comprehensive evaluation of the model’s performance, we have developed a new drone remote-sensing road-semantic-segmentation dataset, DRS Road, which includes approximately 2600 ultra-high-resolution remote-sensing images across six scene categories. The experimental results demonstrate that PISCFF-LNet achieves improvements of 1.06% in Intersection over Union (IoU) and 0.83% in F1-Score on the DeepGlobe Road dataset, and 1.03% in IoU and 0.57% in F1-Score on the DRS Road dataset, compared to existing methods. Finally, we applied the algorithm to a UAV, using a PID-based flight-control algorithm. The results show that drones employing our algorithm exhibit superior flight performance in both simulated and real-world environments. Full article

Photovoltaic power generation as a green energy source is often used in power systems, but the volatility of PV output and randomness of the problem affect the stability of the power-grid power supply; so, for the problem of low prediction accuracy of photovoltaic power generation under different weather conditions, this paper proposes a Variational Mode Decomposition (VMD), combined with a Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) secondary decomposition method for the original signal decomposition, to reduce the signal volatility and reduce the complexity of feature mapping the PV data, followed by the use of a BiLSTM model to model the timing information of the decomposed IMF. Simultaneously, the Informer model predicts the components obtained from the secondary decomposition, and finally, the subsequence is reconstructed and superimposed to obtain the PV power prediction value. The results show that the RMSE and MAE of the proposed model are improved by up to 10.91% and 17.33% on the annual PV dataset, with high prediction accuracy and stability, which can effectively predict the ultra-short-term power of PV power plants. Full article

With the intensification of armed conflicts driven by regional incentives, global geopolitical conflicts are becoming increasingly intense. In addition, the possibility of another financial crisis is approaching, and global inflation is rapidly rising. As a result, Europe and the United States must restart coal-fired power generation to cope with energy shortages and delay carbon neutrality goals. However, the current political and public opinion about the environment has led to a one-sided exaggeration and political criticism of China’s carbon emissions, resulting in China’s contribution to carbon neutrality being intentionally or unintentionally ignored. The uncertainty surrounding future low-carbon policies has made climate observers increasingly concerned about the threat of environmental degradation to fragile intergovernmental decarbonization efforts. This article aims to clarify the one-sided view of China’s carbon emissions internationally, clarify China’s measurement indicators for carbon emissions, analyze China’s advantages in responding to the global warming crisis under complex historical and political conditions, summarize China’s efforts to achieve its dual carbon goals in the current situation, and thus summarize China’s unique and absolute advantages in cooperation in clean energy, energy storage, and ultra-high voltage transmission networks that are beneficial to global climate change. This will clarify the truth regarding China’s carbon emissions in the global context and boost global confidence in addressing climate change. Full article

An ultralow-power, high-performance online-learning and anomaly-detection system has been developed for edge security applications. Designed to support personalized learning without relying on cloud data processing, the system employs sample-wise learning, eliminating the need for storing entire datasets for training. Built using memristor-based analog neuromorphic and in-memory computing techniques, the system integrates two unsupervised autoencoder neural networks—one utilizing optimized crossbar weights and the other performing real-time learning to detect novel intrusions. Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. The system demonstrates 87% anomaly-detection accuracy; achieves a performance of 16.1 GOPS—774× faster than the ASUS Tinker Board edge processor; and delivers an energy efficiency of 783 GOPS/W, consuming only 20.5 mW during anomaly detection. Full article

There are over 300 million smart homes worldwide and 60.4 million smart homes in the US, using devices like smart thermostats, smart plugs, smart door locks, etc. Yet in this age of smart and connected devices, we still use paper-based sticky notes on doors to display messages such as “Busy, do not disturb”, “In a Zoom meeting”, etc. In this project, a novel IoT-connected digital sticky note system was developed where the user can wirelessly send messages from a smartphone to a sticky note display. The sticky note displays can be hung on the doors of offices, hotels, homes, etc. The display could be updated with the user’s message sent from anywhere in the world. The key design challenge was to develop the display unit to consume as little power as possible to increase battery life. A prototype of the proposed system was developed comprising ultra-low-power sticky note display units consuming only 404 µA average current and having a battery life of more than six months, with a Wi-Fi-connected hub unit, an MQTT server, and a smartphone app for composing the message. Full article

Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method. Full article

In this paper, a NOR2 standard-cell-based dynamic comparator providing rail-to-rail input common mode range (ICMR) is presented, together with a novel standard-cell oriented design methodology. The proposed topology provides better speed performance and lower power-delay-product than the previously presented standard-cell-based dynamic comparators with rail-to-rail ICMR features. The NOR2 topology, which is also better than the complementary NAND2-based topology previously presented by the authors, is even able to guarantee improvements in the order of 8× –16× higher speed and 7× lower PDP, with respect to the other rail-to-rail ICMR standard-cell-based topologies in the literature. Concerning the standard-cell oriented design methodology, it is focused on the impact of the cell’s strength, which is the only free parameter, on delay, power consumption, ICMR and offset. The circuit performances are demonstrated for supply voltages equal to 600 mV, 300 mV and 150 mV, considering a 45 nm CMOS technology. Full article