Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
In this paper, an innovative reconfigurable microstrip RF device design method is proposed, which is inspired by origami structures. The experimental results of the reconfigurable low-pass filter indicate that the maximum origami folding height is 3 mm, resulting in the frequency tuning range of the filter being 524~568 MHz, the return loss is below −15.0 dB and the insertion loss is below 2.5 dB up to 500 MHz. It is demonstrated that the proposed design method for reconfigurable microstrip RF devices is fairly effective through theoretical and experimental research. This work provides a groundbreaking method for reconfigurable RF devices with origami structures....
With the rapid growth of unmanned aerial vehicles (UAVs) and IoT users, spectrum resources are becoming increasingly scarce, making cognitive radio (CR) technology a key approach to improving spectrum utilization. However, traditional antennas are difficult to meet the lightweight, compact, and low-drag requirements of small UAVs due to spatial constraints. This paper proposes a tri-mode frequency reconfigurable flexible antenna that can be conformally integrated onto UAV wing arms to enable CR dynamic frequency communication. The antenna uses a polyimide (PI) substrate and has compact dimensions of 31.4 × 58 × 0.05 mm3. A microstrip line-based frequency-selective network is designed, incorporating PIN and varactor diodes to realize three operation modes, dualband (2.25~3.55 GHz, 5.6~6.75 GHz), single-band (3.35~5.3 GHz), and continuous tuning (4.3~6.1 GHz), covering WLAN, WiMAX, and 5G NR bands. Test results show that the antenna maintains stable performance under conformal conditions, with frequency shifts less than 4%, gain (3.65~4.77 dBi), and radiation efficiency between 67.2% and 82.9%. The tuning ratio reaches 38.8% in the continuous mode. This design offers a new solution for CR communication in compact UAV platforms and shows promising application potential....
This submission is focused on the implementation of a system that acquires data from various types of sensors and securely stores them after encryption on a chip with a reconfigurable architecture. The system has the unique capability of encrypting the input data with a single secret cryptographic key, which is stored only inside the hardware of the system itself, so the key remains unrecognizable upon completion of the system synthesis for any unauthorized user. Being stored as a part of the whole system architecture, the cryptographic key cannot be attained. It is not stored separately on the system RAM or any other supported memory, making the collected data fully protected. The reported work shows a data acquisition system which measures temperature with a high level of precision, transforms it to degrees Celsius, stores the collected data, and transfers them via serial interface when requested. Before storage, the data are encrypted with a 256-bit key, applying the AES algorithm. The data which are stored in the system memory and sent as UART packets towards the main computer do not include the cryptographic key in the data stream, so it is impossible for it to be retrieved from them. We show the flexibility of such kinds of data acquisition systems for sensing different types of signals, emphasizing secure storage and transferring, including data from meteorological sensors or highly confidential or biometrical data....
A bias-free antenna tuning technique that eliminates conventional DC biasing networks is presented. The tuning mechanism is based on a Light-Dependent Resistor (LDR) embedded within the antenna structure. Optical illumination is used to modulate the LDR’s resistance, thereby altering the antenna’s effective electrical length and enabling tuning of its resonant frequency and operating bands. By removing the need for bias lines, RF chokes, blocking capacitors, and control circuitry, the proposed approach minimizes parasitic effects, losses, biasing energy, and routing complexity. This makes it particularly suitable for compact and energy-constrained platforms, such as Internet of Things (IoT) devices. As proof of concept, an LDR is integrated into a ring monopole antenna, achieving tri-band operation in both high and low resistance states. In the high-resistance (OFF) state, the fabricated prototype operates across 2.1–3.1 GHz, 3.5–4 GHz, and 5–7 GHz. In the low-resistance (ON) state, the LDR bridges the two arcs of the monopole, extending the current path and shifting the lowest band to 1.36–2.35 GHz, with only minor changes to the mid and upper bands. The antenna maintains linear polarization across all bands and switching states, with measured gains reaching up to 5.3 dBi. Owing to its compact, bias-free, and low-cost architecture, the proposed design is well-suited for integration into portable wireless devices, low-power IoT nodes, and rapidly deployable communications systems where electrical biasing is impractical....
Silicon photonic computing system is expected to replace traditional electronic computing systems in specific applications in the future, owing to its advantages in high speed, large bandwidth, low power consumption, and resistance to electro-magnetic interference. In this paper, we propose a tunable time-delay photonic computing architecture based on chirped Bragg gratings (CBG), which replaces traditional dispersion fibers to achieve the required delay function in system architecture, while providing reconfigurability capabilities of time delay control. Simulation results, using 3rd-order and 4th-order input matrices to convolve with 2nd-order convolution kernel matrices, demonstrates that the proposed photonic computing architecture can effectively perform matrix convolutional operations of various orders. Furthermore, the functionality and performance of design tunable time delay module based on CBG is also verified in the system. Therefore, our proposed scheme can be employed in the matrix multiplications of photonic computing architecture, which provides an optional efficient solution for future photonic convolutional neural networks....
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