SUMMARY: The plasma membrane and accompanying cortex serve as major hubs of signal transduction and cytoskeletal activities that collectively regulate cell physiological processes such as migration, polarity, macropinocytosis, phagocytosis, and cytokinesis. Yet, dynamically tracking membrane-cortex associated protein or lipid kinetics from live-cell image series remains challenging, primarily due to the difficulty of accurately extracting and aligning the cell boundary between consecutive frames as the cell continuously deforms and moves. Here, we present Membrane Kymograph Generator, a cross-platform software that accepts multichannel time-lapse live-cell fluorescent imaging datasets and automates boundary tracking, inter-frame alignment, and intensity sampling along the boundary. The software implements a rotational offset minimization algorithm that aligns boundaries across consecutive frames by exhaustively searching for the optimal angular shift that minimizes point-to-point distances, while handling variations in boundary point counts due to cell shape changes. The software outputs kymographs representing spatiotemporal dynamics of membrane-associated proteins or biosensors, allows users to fine-tune visualization parameters through an interactive interface, and provides built-in correlation analysis tools for multi-channel datasets. Furthermore, a native Python API enables programmatic usage for batch processing and further downstream analysis. Validation tests demonstrated that the Membrane Kymograph Generator accurately tracks, visualizes, and quantitates the spatial kinetics of a wide array of membrane proteins and lipid biosensors over extended time periods, in a variety of cell types including Dictyostelium amoeba, human neutrophils, mouse macrophages, and mammalian cancer cells. The GUI-based software is user-friendly, requires no technical expertise, and significantly reduces the manual effort required for kymograph generation and analysis while ensuring high accuracy and reproducibility. AVAILABILITY AND IMPLEMENTATION: Membrane Kymograph Generator is free and open-source, licensed under GNU General Public License 3.0 or later. It can be installed on both x86-64 and AArch64/ARM64 computers running Windows, macOS, or any standard Linux distribution. The software is distributed as standalone installer files and portable executables targeting specific architectures and operating systems, requiring no dependency resolution. The source code, documentation/wiki, installers, portable binaries, and test data are freely available at https://github.com/tatsatb/membrane-kymograph-generator. The software can also be installed via PIP (package ID: membrane-kymograph, https://pypi.org/project/membrane-kymograph) and accessed programmatically via a built-in Python API. The source code is also archived on Zenodo (DOI: 10.5281/zenodo.20318834).

Physical layer message integrity protection and authentication by countering signal-cancellation has been shown as a promising alternative to traditional pure cryptographic message authentication protocols, due to the non-necessity of neither pre-shared secrets nor secure channels. However, the security of such an approach remained an open problem due to the lack of systematic security modeling and quantitative analysis. In this paper, we first establish a novel signal cancellation attack framework to study the optimal signal-cancellation attacker's behavior and utility using game-theory, which precisely captures the attacker's knowledge using its correlated channel estimates in various channel environments as well as the online nature of the attack. Based on theoretical results, we propose a practical channel randomization approach to defend against signal cancellation attack, which exploits state diversity and swift reconfigurability of reconfigurable antenna to increase randomness and meanwhile reduce correlation of channel state information. We show that by proactively mimicking the attacker and placing restrictions on the attacker's location, we can bound the attacker's knowledge of channel state information, thereby achieve a guaranteed level of message integrity protection in practice. Besides, we conduct extensive experiments and simulations to show the security and performance of the proposed approach. We believe our novel threat modeling and quantitative security analysis methodology can benefit a wide range of physical layer security problems.

MU-MIMO transmission relies on exploiting multi-user diversity among a multitude of users. In this paper, to enhance MU-MIMO transmission, techniques for designing and exploiting reconfigurable antennas (RAs) are developed. A single-element RA is capable of generating modes with different radiation pattern and polarization states resulting in multiple different channel states for each user. This capability expands the search space for user-pairing optimization. To reduce complexity, an iterative user and antenna mode selection algorithm is proposed. Based on parasitic tuning, an RA optimized for MU-MIMO transmission and capable of creating four pattern types is designed using a genetic algorithm and full-wave electromagnetic analyses. Pattern- and channel-aware mode-set generation methods are developed, where mode groups are determined to reduce channel estimation overhead and mode selection complexity while achieving superior performance. Simulation results indicate that the proposed schemes need a lower number of users and/or relax user selection requirements to benefit from MU-MIMO transmissions. It is also seen that more legacy antennas are needed to achieve similar MU-MIMO performance provided by the RA system. Simulations show up to 16-dB signal-to-noise ratio gains for a single cell with 12 users, where the base station has four RAs and four single-antenna users are simultaneously served via MU-MIMO transmission.

This paper proposes an efficient downlink multi- user MIMO (MU-MIMO) scheme for multiple antenna transmission systems comprised of reconfigurable antenna (RA) elements. MU-MIMO schemes typically require a combined user pairing and precoding scheme where the channels of the selected users have to satisfy certain conditions to reduce interference among the users. Therefore, MU-MIMO typically requires a large number of users so that suitable user-tuples can be found. In this work, instead of relying on this requirement, we employ RAs at the transmitter to create multiple channel states for each user. An RA system accomplishes this by its dynamically changeable operation modes each of which creates a different channel realization. The availability of multiple channel states allow for an expanded search space for user pairing. We develop a joint user and antenna mode selection algorithm minimizing the error rates. A low-complexity iterative mode selection scheme is introduced to simplify the joint optimization problem. Simulation results indicate that one would need a lower number of users to benefit from MU-MIMO transmission if RAs are employed at the transmitter.

Multifunctional and reconfigurable multiple-input multiple-output (MR-MIMO) antennas are capable of dynamically changing the operation frequencies, polarizations, and radiation patterns, and can remarkably enhance system capabilities. However, in coherent communication systems, using MR-MIMO antennas with a large number of operational modes may incur prohibitive complexity due to the need for channel state estimation for each mode. To address this issue, we derive an explicit relation among the radiation patterns for the antenna modes and the resulting channel gains. We propose a joint channel estimation/prediction scheme where only a subset of all the antenna modes is trained for estimation, and then, the channels associated with the modes that are not trained are predicted using the correlations among the different antenna modes. We propose various training mechanisms with reduced overhead and improved estimation performance, and study the impact of channel estimation error and training overhead on the MR-MIMO system performance. We demonstrate that one can achieve significantly improved data rates and lower error probabilities utilizing the proposed approaches. For instance, under practical settings, we observe about 25% throughput increase or about 3-dB signal-to-noise ratio improvement under the same training overhead with respect to non-reconfigurable antenna systems.

Full-duplex systems are expected to achieve 100% rate improvement over half-duplex systems if the self-interference signal can be significantly mitigated. In this paper, we propose the first full-duplex system utilizing multireconfigurable antenna (MRA) with ~90% rate improvement compared with half-duplex systems. MRA is a dynamically reconfigurable antenna structure that is capable of changing its properties according to certain input configurations. A comprehensive experimental analysis is conducted to characterize the system performance in typical indoor environments. The experiments are performed using a fabricated MRA that has 4096 configurable radiation patterns. The achieved MRA-based passive self-interference suppression is investigated, with detailed analysis for the MRA training overhead. In addition, a heuristic-based approach is proposed to reduce the MRA training overhead. The results show that at 1% training overhead, a total of 95 dB self-interference cancellation is achieved in typical indoor environments. The 95-dB self-interference cancellation is experimentally shown to be sufficient for 90% full-duplex rate improvement compared with half-duplex systems.

This paper proposes low overhead training techniques for a wireless communication system equipped with a Multifunctional Reconfigurable Antenna (MRA) capable of dynamically changing beamwidth and beam directions. A novel microelectromechanical system (MEMS) MRA antenna is presented with radiation patterns (generated using complete electromagnetic full-wave analysis) which are used to quantify the communication link performance gains. In particular, it is shown that using the proposed Exhaustive Training at Reduced Frequency (ETRF) consistently results in a reduction in training overhead. It is also demonstrated that further reduction in training overhead is possible using statistical or MUSIC-based training schemes. Bit Error Rate (BER) and capacity simulations are carried out using an MRA, which can tilt its radiation beam into one of Ndir= 4 or 8 directions with variable beamwidth (≈2π/Ndir). The performance of each training scheme is quantified for OFDM systems operating in frequency selective channels with and without Line of Sight (LoS). We observe 6 dB of gain at BER = 10-4and 6 dB improvement in capacity (at capacity = 6 bits/sec/subcarrier) are achievable for an MRA with Ndir= 8 as compared to omni directional antennas using ETRF scheme in a LoS environment.