Search for: All records

Highly integrated, flexible, and ultrathin wireless communication components are in significant demand due to the explosive growth of portable and wearable electronic devices in the fifth‐generation (5G) network era, but only conventional metals meet the requirements for emerging radio‐frequency (RF) devices so far. Here, it is reported on Ti₃C₂T_xMXene microstrip transmission lines with low‐energy attenuation and patch antennas with high‐power radiation at frequencies from 5.6 to 16.4 GHz. The radiation efficiency of a 5.5 µm thick MXene patch antenna manufactured by spray‐coating from aqueous solution reaches 99% at 16.4 GHz, which is about the same as that of a standard 35 µm thick copper patch antenna at about 15% of its thickness and 7% of the copper weight. MXene outperforms all other materials evaluated for patch antennas to date. Moreover, it is demonstrated that an MXene patch antenna array with integrated feeding circuits on a conformal surface has comparable performance with that of a copper antenna array at 28 GHz, which is a target frequency in practical 5G applications. The versatility of MXene antennas in wide frequency ranges coupled with the flexibility, scalability, and ease of solution processing makes MXene promising for integrated RF components in various flexible electronic devices.

 

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology to aid wireless communication. However, the potential of using RIS to mitigate directed energy weapons (DEW) is not widely recognized. In this paper, we propose to leverage RIS (based on spiral antenna elements) to aid the mitigation of high-energy radio-frequency (RF) sources applied to a DEW. For example, integrating a broadband circularly-polarized antenna system with RIS technology can successfully mitigate DEW attacks across a wide range of frequencies regardless of how the radio waves are polarized. We simulated a spiral antenna that operates within a frequency band of 1.3 GHz to 7 GHz with a 3-dB axial ratio bandwidth (ARBW) covering from 2 GHz to 7 GHz. Full-wave simulation results show the potential promising application of RIS for the mitigation of DEW attacks. 

We present a directed high-energy radio wave exposure detection sensor using radio frequency (RF) energy harvesting techniques. The sensor comprises a small dipole antenna and a tunable rectifier circuit. Reverse biasing the diode allows high levels of RF radiation to be detected by the sensor. We also demonstrate how the frequency-dependent nature of the rectifier can be alleviated. The proposed sensor performance is experimentally evaluated in the 5–10 GHz range. 

Although much of the work in behaviorally detecting malware lies in collecting the best explanatory data and using the most efficacious machine learning models, the processing of the data can sometimes prove to be the most important step in the data pipeline. In this work, we collect kernel-level system calls on a resource-constrained Internet of Things (IoT) device, apply lightweight Natural Language Processing (NLP) techniques to the data, and feed this processed data to two simple machine learning classification models: Logistic Regression (LR) and a Neural Network (NN). For the data processing, we group the system calls into n-grams that are sorted by the timestamp in which they are recorded. To demonstrate the effectiveness, or lack thereof, of using n-grams, we deploy two types of malware onto the IoT device: a Denial-of-Service (DoS) attack, and an Advanced Persistent Threat (APT) malware. We examine the effects of using lightweight NLP on malware like the DoS and the stealthy APT malware. For stealthier malware, such as the APT, using more advanced, but far more resource-intensive, NLP techniques will likely increase detection capability, which is saved for future work. 

Passive ultra high frequency (UHF) radio frequency identification (RFID) tags have the potential to find ubiquitous use in indoor object tracking, localization, and contact tracing. We propose a machine learning-based method for RFID indoor localization using a pattern reconfigurable UHF RFID reader antenna array. The received signal strength indicator (RSSI) values (from 10,000 tags) recorded at the reader antenna units are used as features to evaluate the machine learning models with a train-test split of 75%-25%. The training and testing data is generated by a wireless ray tracing simulator. Five machine learning models: random forest regressor, decision tree regressor, Nu support vector regressor, k nearest regressor, and kernel ridge regressor are compared. Random forest regressor has the lowest localization error both in terms of average Euclidean distance (AED) and root-mean-square error (RMSE). For random forest regressor, localization error results show that 90% of the tags are within 1 meter of their true position, and 67% are within 50 cm of their true position based on Euclidean distance. 

Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant’s body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model training and validation, and model testing and deployment. The model used is a 1-D convolutional neural network (1DCNN) architecture with one convolution layer, one pooling layer, and three fully-connected layers, achieving 97.15% classification accuracy. To address the energy limitations of wearable processing, several quantization techniques are explored, and their performance and energy consumption are analyzed for the respiratory classification task. Results demonstrate a reduction of energy footprints and model storage overhead with a considerable degradation of the classification accuracy, meaning that quantization and other model compression techniques are not the best solution for respiratory classification problem on wearable devices. To improve accuracy while reducing the energy consumption, we propose a novel spiking neural network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic hardware platforms. To this end, we propose an approach to convert the analog operations of our baseline trained 1DCNN to their spiking equivalent. We perform a design-space exploration using the parameters of the converted SNN to generate inference solutions having different accuracy and energy footprints. We select a solution that achieves an accuracy of 93.33% with 18x lower energy compared to the baseline 1DCNN model. Additionally, the proposed SNN solution achieves similar accuracy as the quantized model with a 4× lower energy.