Object detection is a critical component in various applications, yet conventional detection architectures are typically developed on RGB or grayscale databases, limiting their effectiveness in challenging real-world scenarios. To address this, this work utilizes broad-spectral databases to enhance detection in low-light and complex environments. We introduce a graph-based representation that captures both in-channel and cross-channel features in broad-spectral images, enabling a more comprehensive scene understanding. A Graph Convolutional Network (GCN)-based aggregation strategy is proposed to integrate information from multiple channels effectively. A data enhancement method is also proposed based on the imbalanced characteristics in the foreground and background regions. Experimental results show that our model achieves superior mean Average Precision (mAP@50) across multiple datasets.

Radio Frequency Fingerprints (RFFs) imprinted on the RF signals by imperfections of hardware manufacturing enable device authorization augmentation in the Internet of Things (IoT) communication. However, the identifiable RF fingerprints are exposed to potential adversary attackers that can mimic the identifiable RF fingerprints of the authorized devices and fool the classifier at the receiver to obstruct secure communications in an open environment. This work proposes an attacker based on a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) to extract the identifiable RF fingerprints generated from over 220 authorized transmitters. At the receiver, a Convolutional-Neural-Network-(CNN)-based classifier is deployed and shows a multi-class False Positive Rate (FPR) of 97.7% on the signals synthesized by the attacker at 30 dB Signal-to-Noise Ratio (SNR). In dealing with secure communication featuring time-varying RFFs, it’s essential to also monitor the time required for attackers to adapt. Within 60 seconds, results indicate that there’s a 92.9% probability of deceiving the receiver at 30 dB, with the possibility remaining at 87.4%s even when the SNR drops to 5 dB, which shows the attacker’s capability to overcome a wide range of SNR conditions. Furthermore, an evaluation of the distance effect highlights its robustness to device movement.

A reconfigurable power amplifier (PA) is implemented in CMOS 65nm to enable radio identification for secure wireless communication by injecting tunable radio frequency fingerprints (RFFs) into the physical layer. The large ensemble of RFFs is achieved by offsetting the distributions of process variations affecting the PA’s hardware features. The resulting large RFF capacity is exploited to increase resilience to noise and temperature changes by selecting distinct RFFs from the ensemble and reconfiguring the PA to restore nominal RFFs following temperature shifts. The secure PA achieves over 14000 time-varying RFFs while consuming only 22 mW and occupying a core area of$<$0.0951 mm$^{2}$. A reinforcement learning (RL)-based control has been implemented on FPGA for closed-loop reconfiguration of the transmitter to achieve robust and low-overhead security measures that overcome noise and temperature influences in dynamic environments.

This paper proposes a new primitive that allows soft robots to be physically controlled in a completely non-line-of-sight context using wireless energy - a process we call wireless actuation. Soft robots, which are composed entirely of soft materials and exclude any rigid components, are highly flexible platforms that can change their shape. This paper considers a specific class of soft robots composed of liquid-crystal elastomers (LCE) that are entirely electronics-free and engineered to change shape when heated to 60 °C. Traditionally, such robotic systems must be in line-of-sight of a light source, such as infrared to be moved, or require an external power supply for Joule heating and often take several tens of seconds to heat. We present WASER, a novel RF-based heating platform that allows electronics-free robots to be actuated rapidly (within a few seconds) and potentially in non-line-of-sight. WASER achieves this through innovations in both wireless systems and material science. On the wireless front, WASER develops a new blind beamforming solution that directs high-power wireless energy at fine spatial granularity without electronics on the robot to provide feedback. On the material science front, WASER exhibits heat-responsive shape-morphing and energy-harvesting material functionalities that allow for rapid wireless heating. We implement and evaluate WASER and demonstrate diverse shape-morphing capabilities.

No abstract available.

One means by which the security of Internet-of-Things (IoT)-enabled devices may be augmented is through radio-frequency fingerprinting-based authentication methods. As variability in CMOS processes increases with technology scaling, the hardware imperfections that form RF fingerprints can be controlled with small reconfigurable elements, enabling the feasibility of RF fingerprinting as a low overhead security measure for device authentication. To achieve rapid RF identification, we present an inherently secure RF power amplifier and a convolutional neural network-based machine learning classifier through an exploration of combinatorial randomness and self-aware detection mechanisms. By selecting different subsets of thinly sliced power amplifier elements, combinations of random process variations are exploited and updated to form a large search space of distinct RF fingerprints and improve fingerprint prominence. The rich features enabled by augmented device primitives are updated in a time-varying manner to strengthen built-in hardware security. Measurement results demonstrate the effectiveness of this approach at generating distinguishable RF fingerprints across a significant number of configurations.

The equipment includes a laptop, Xilinx ZCU102 Dev Board, and a hard-disk drive (HDD) interface module. Fig. 1 illustrates the demonstration setup. All of the devices will be powered by their own power adaptors. The HDD interface module, which is provided by the Data Storage Systems Center (DSSC) at Carnegie Mellon University, can be controlled by the laptop to generate the raw readback signals for the machine- learning (ML) module implemented on the Xilinx ZCU102 to perform data symbol detection. The classified outputs are sent back to the laptop for result analysis and demonstration with a graphical user interface (GUI).

Machine-learning-based readout channels are presented for direct data symbol detection via decision-tree classification with gradient boosting for multiple-actuator data storage systems. The proposed learning module integrates energy-efficient linear classifiers to extract features and structures from raw readback signals. The results demonstrate high detection accuracy, which is robust to inter-symbol interference (ISI) and jitter noise. The low-complexity machine learning module classifies low signal-to-noise ratio raw data with an accuracy rate higher than 95.8% in real-time and consumes only 53 mW.

This work presents an ultra-low-power classifier that can be integrated within energy-constrained bio-sensors to enable rapid analysis for continuous health monitoring. The in-sensor classifier saves significant transmission energy by extracting critical information locally to eliminate the need of transmitting raw data to centralized servers for remote signal processing. The convolutional-neural-network (CNN)-based classifier is built by using reconfigurable delay-locked loops (DLLs) to carry out classification algorithms with time-domain multiply-accumulate (MAC) operations. Pseudo sigmoid activation functions are realized by regenerative comparators that transform weighted timing to probabilities. The presented classifier achieves low-power consumption of 240.34 nW while performing up to 20 k operations per second. The proposed time-domain classifier reduces the energy to 36% of the previous works.