This article presents an Internet-of-Drones (IoD)-enabled system for real-time, high-resolution, in-situ water quality sensing, sampling, 2-D mapping, and 3-D depth profiling from discrete sensing. Unlike isolated mooring platforms and stationary sensor buoys, our Uncrewed Aerial Vehicle (UAV) platform features a modular IoD architecture with wireless communication for adaptive sampling, parameter mapping, and real-time monitoring. The system integrates pH, temperature, turbidity, total dissolved solids (TDS), and depth sensors within a single-actuator multi-cartridge vessel that collects samples via TDS, depth, and ML-based triggers into four 50mL tubes. The proposed embedded system incorporates LoRa/LoRaWAN for low-power telemetry and LTE for wireless data transmission, with SD card logging, GPS geo-tagging, and Real-time Clock (RTC) synchronization. A high-resolution 3-D interpolation framework is implemented that reconstructs water quality fields from UAV-based missions over a 0.8m× 0.8m× 1mvolume and a comprehensive 2-D mapping over a 180m× 180marea. The system incorporates a smart sampling logic based on predefined thresholds (e.g., TDS and depth triggers), along with a machine–learning–assisted mode for adaptive chlorophyll-adetection and real-time decision-making. Comprehensive field tests at the Lake Erie digital test bed validate system performance.

This article presents a Pre-Amplifier Physical Unclonable Function (PUF) fabricated in a 3-nm CMOS technology. The design achieves a “zero” Bit Error Rate (BER,$\lt 8.17\times 10^{-9}$) derived from the worst-case combination of enrollment (low voltage, low temperature) and key reconstruction (high voltage, high temperature) conditions. The design accommodates VDD voltages ranging from 650 mV to 950 mV and temperatures from$- 40~^{\circ }$C to$125~^{\circ }$C, meeting the requirements for most commercial applications in the technology sector. The BER reduction is achieved using a novel hybrid voting scheme. This scheme incorporates conservative unanimous voting during the stable bit identification (enrollment) and temporal majority voting during key reconstruction. Additionally, a gain enhancement stage using NFET cross-coupled devices is employed to maximize signal margins during reads. The design passes NIST SP800-90B and SP800-22 randomness tests while achieving 49.83% inter-chip hamming distance and 49.95% average hamming weight. The PUF can be reconfigured into a multi-stage self-destruction mode of operation in response to tamper events. The design combines electromigration (EM) and Time-Dependent Dielectric Breakdown (TDDB) to intentionally target the damage directly to the PUF data array containing stable (repeatable) bitcells. The result is an irreversible corruption of PUF encryption key to restrict all future authentication attempts.

In this paper, a 28 GHz/39 GHz dual-band monolithic microwave integrated circuit (MMIC) gallium nitride (GaN) power amplifier has been proposed for the long-range millimeter-wave radar and 5G communication applications. The amplifier integrates a driver and a power circuit, utilizing a commercial 40 nm GaN technology. Unique low-loss dual frequency matching circuits are employed to ensure high efficiency at the desired output power. The simulation results show that the power amplifier achieves a saturated output power of above 31 dBm at both frequencies, with a power-added efficiency (PAE) exceeding 36%.

The Sub-sampling PLL (SSPLL) has attracted significant interest from researchers due to its low phase error and high operating frequency. However, the sub-sampling phase detector (SSPD) is sensitive only to phase error, not frequency error, which may cause false locking into undesired frequencies. This paper provides a detailed analysis of the sub-sampling process and discusses false locking scenarios. Furthermore, the lock-in range, which is the boundary between the system's linear and non-linear behavior, is also investigated. It is a key characteristic of system stability and a constraint in the design of auxiliary devices. This paper develops a state-space model that considers the sinusoidal characteristics of the SSPD to analyze frequency acquisition behavior and estimate the lock-in range.

Accurate and real-time detection of precipitation phases is essential for hydrological modeling, water resource management, and climate impact assessments. However, conventional methods struggle to distinguish precipitation phase near freezing and are unsuitable for distributed deployment in remote, complex terrain due to cost, size, and power constraints. In this work, we present an integrated acoustic sensing system that combines machine learning on the edge with acoustic sensing and Mel-Frequency Cepstral Coefficients (MFCC)-based feature extraction, all implemented on our designed edge device. The device is low-cost, with LAN and WAN capabilities for near real-time reporting, and can integrate multiple sensors for snow hydrology studies, in addition to detecting the precipitation phase. We present the design, fabrication, and validation of the Advanced Unified Rainfall and Atmospheric Monitoring (AURA) system, integrating sensors for temperature, humidity, ultrasonic snow depth, solar radiation, and wind velocity. AURA features an embedded machine learning algorithm that enables accurate classification of precipitation phases. Short-time Fourier Transform (STFT) and MFCCs are computed on the edge from recorded precipitation acoustics via novel Mel filter bank computation methods. Using support vector machine (SVM) and random forest (RF) classifiers, the RF model achieves testing accuracy of 97.35% on simulated precipitation acoustics and 85.67% in consolidated class (CC) environmental recordings. The SVM classifier achieves 98.07% accuracy on simulated acoustics and 85.99% on CC environmental recordings. The low-power AURA network uses a LoRa star topology with TDMA and an Iridium gateway for reliable data transfer from remote sites. SNR analysis and comprehensive field tests confirm and validate system performance.

Sensor nodes’ localization in wireless Internet of Things (IoT) sensor networks is crucial for the effective operation of diverse applications, such as smart cities and smart agriculture. Existing sensor nodes’ localization approaches heavily rely on anchor nodes within wireless sensor networks (WSNs). Anchor nodes are sensor nodes equipped with global positioning system (GPS) receivers and thus, have known locations. These anchor nodes operate as references to localize other sensor nodes. However, the presence of anchor nodes may not always be feasible in real-world IoT scenarios. Additionally, localization accuracy can be compromised by fluctuations in received signal strength indicator (RSSI), particularly under non-line-of-sight (NLOS) conditions. To address these challenges, we propose UBiGTLoc, a Unified bidirectional long-short-term memory (BiLSTM)-Graph Transformer Localization framework. The proposed UBiGTLoc framework effectively localizes sensor nodes in both anchor-free and anchor-presence WSNs. The framework leverages BiLSTM networks to capture temporal variations in RSSI data and employs Graph Transformer layers to model spatial relationships between sensor nodes. Extensive simulations demonstrate that UBiGTLoc consistently outperforms existing methods and provides robust localization across both dense and sparse WSNs while relying solely on cost-effective RSSI data.

A reconfigurable preamplifier physical unclonable function (Pre-Amp PUF) is designed to support multiple challenge–response pairs per bitcell with zero array area impact. A novel addressing scheme executed on balanced pull-up and pull-down networks of the bitcell allow for reconfigurability of the PUF key and obfuscation of the data located within the chip. The Pre-Amp PUF bitcell is uniquely situated to replace other array-based PUF designs through improved hardware security and resistance to reverse engineering using imaging. Data is sensed using tens of millivolts of differential signal and remains valid only when a bitcell is selected for a read operation.

In GPR B-scan images, ground surface clutter is the main source of interference, often obscuring or distorting subsurface target signals. We propose a deep autoencoder-based method to mitigate rough surface clutter in GPR images by treating it as an anomaly detection problem. Firstly, the rough surface region in a B-scan image is partitioned into small patches, which act as the training dataset for the deep autoencoder. Through the training process, the autoencoder learns and captures the patterns associated with the rough surface patches. Following training, the entire B-scan image is divided into small patches of the same size as the training patches, and each of them is fed into the autoencoder to compute an anomaly score. To reconstruct a clutter-reduced B-scan image, we employ a weighted sum approach to aggregate all patches based on their anomaly scores. We evaluate our method against conventional subspace projection techniques using simulated and field-collected B-scans. The results clearly indicate that our approach surpasses these subspace methods. Furthermore, we employ t-SNE analysis to gain deeper insights into our method’s effectiveness in reducing rough surface clutter. The outcomes of this analysis reinforce the practical viability of our approach for GPR image processing.

Physical unclonable functions (PUFs) produce full security keys without needing to store the key directly in nonvolatile memory (NVM) or publicly. PUFs rely on a stable entropy source across voltage, temperature, and lifetime. A high-gain preamplifier (Pre-Amp) bit cell was built into a dense 2-D array configured as 16 cells per bitline (BL) and 64 cells per wordline (WL). A hardware (HW) sample of 40 chips (1 Kb/chip) was manufactured in GLOBAL FOUNDRIES (GF) 12-nm (12 lp) CMOS technology for a total raw bit count of 40 960 bits. HW characterization was performed to support power supply ranging from 0.7 to 1.0 V and junction temperatures ranging from −40 °C to 125 °C with a worst case bit error rate (BER) of 0.174% after stabilization. The entropy source array is complete for productization with control logic and analog power system block for process, voltage, and temperature (PVT) compensation. A stable bit identification and sensing circuit is designed to identify bit cells that are robust against varied test conditions. The NIST 800-90B test suite was run on the native array and stable entropy source bits with minimum entropy scores of 0.65/bit and 0.697/bit, respectively. This article then introduces for the first time ever the ability for a PUF key to be corrupted and physically destroyed, which can be utilized to stop a tamper event or to corrupt obsolete chips. In the self-destruct (SD) mode, the entropy source data can be irreversibly destroyed, blocking all future authentication attempts. HW data show the before and after SD bitmaps, where electromigration (EM) physically breaks the connection of the entropy source bit cells from the sensing circuits. A safety lock circuit is also included to prevent inadvertent SD.

This paper presents the design and analysis of an automated traffic signage inventory management system employing the radio-frequency identification (RFID) technology. Traffic signage inventory management faces the operational challenge of recognizing a large number of traffic signs spread in a large area without obvious identifications on them. Thanks to the remote sensing ability of RFID, the operational efficiency can be significantly improved by scanning traffic signs remotely in motion. The system consists of an in-vehicle RFID reader and a handheld RFID reader, both of which connect to mobile applications with user-friendly interfaces. Additionally, user-friendly control software and a database are developed. In this paper, the adequacy of the RFID technology for the task is assessed. On the theoretical side, this paper investigates the radio-wave propagation model and the antenna radiation range. Additionally, the influences of the vehicle speed and the sign-to-reader distance on the reading performance are studied and empirical tests of the tag positioning are conducted. Finally, a system protocol is validated by a field test.

Ground penetrating radars (GPRs) carried by mobile platforms, such as vehicles and drones, have been applied in various applications, for instance, subsurface utility detection, structural health inspection, and autonomous driving. However, existing GPR systems are not able to operate autonomously and adaptively due to several challenges, including the lack of intelligence, uncertain and dynamic nature of sensing environments, and huge state and action spaces. To overcome these challenges, in this article, we propose an autonomous cognitive GPR (AC-GPR) enabled by a deep reinforcement learning (DRL) approach. Specifically, the operation of the proposed AC-GPR is first formulated as a sequential decision process. A novel reward function is developed for the DRL model by defining and combining two different types of entropy-based rewards resulting from object detection and recognition, respectively. A deep Q-learning network (DQN) is developed to address the extreme curse of dimensionality in the state space and learn a policy directing the actions of the AC-GPR. The AC-GPR is evaluated using software called GprMax by combining DRL with GPR modeling and simulation. Results show that our proposed DRL-based AC-GPR outperforms other GPR systems using different approaches in terms of detection accuracy and operating time.

In this paper, a low phase noise 28/38GHz dual-band cascaded PLL is designed with a dual-mode voltage controlled oscillator (VCO). The cascaded PLL consists of two stages, where the 1st stage is a charge-pump PLL and the 2nd stage is a sub-sampling PLL. A quadrature dual-mode VCO is designed that is switchable in two frequency bands of 27.5-28.35GHz and 37-40GHz. The circuit is designed using a 130nm BiCMOS technology. The simulated phase noise is -110 and -115 dBc/Hz at 1MHz offset at 28GHz and 38GHz respectively.

This paper focuses on detecting the respiratory of a person by utilizing a doppler radar to monitor the chest movement during respiration. Specifically, machine learning approach in conjunction with the radar sensor is utilized to capture the radar reflection pulse signal and its movement patterns. By analyzing the evolution of the reflected pulse while breathing, the respiratory rate can be accurately measured. In addition, the change of respiration ratio and respiration patterns can be characterized.

Ground penetrating radar (GPR) is a useful instrument for smarter infrastructure applications, in particular, for the localization and mapping of underground infrastructure and other subsurface assets, due to its ability to sense metallic and nonmetallic buried objects. For instance, air-coupled, multistatic GPR could potentially be employed to quickly produce subsurface maps for public and private stakeholders, enabling rational and more efficient planning of underground infrastructure inspection, maintenance, and construction. An application of interest in such context is a faster identification of underground utilities location and depth by innovative data visualization methods, such as augmented reality. A 3-D model of the subsurface asset is desirable for such applications. However, raw GPR data is often hard to interpret. Imaging algorithms are applied to improve GPR data readability and signal-to-noise ratio by focusing the spread energy. Here, a processing pipeline that takes raw 3-D multistatic GPR data as input and yields a 3-D model as output is proposed. Initially, a 3-D back-projection algorithm is applied to air-coupled, multistatic GPR data to recover buried target localization. An enhancement filter, tailored for tubular structures, is applied to reduce background noise and highlight structures of interest in the 3-D image. This process is successfully applied to three laboratory scenarios of plastic buried targets with different sizes and shapes.

With distributed communication, computation, and storage resources close to end users/devices, fog computing (FC) makes it very promising to develop cognitive portable ground penetrating radars (GPRs) operating intelligently and adaptively under varying sensing conditions. However both strict performance requirement and tradeoffs between communication and computation pose significant challenges. This paper presents a fog computing framework for cognitive portable GPRs. Specifically, the system architecture of an FC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the fog server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by fog computing.

The development of modern cities heavily relies on the availability and quality of underground utilities that provide drinking water, sewage, electric power, and telecommunication services to sustain its growing population. However, the information of localization and condition of subterranean infrastructures is generally not readily available, especially in areas with congested pipes, which impacts urban development, as poorly documented pipes may be hit during construction, affecting services and causing costly delays. Furthermore, aging components are prone to failure and may lead to resources waste or the interruption of services. Ground penetrating radar (GPR) is a promising remote sensing technique that has been recently used for mapping and assessment of underground infrastructure. However, current commercial GPR survey systems are designed with wheel-encoders or GPS for positioning. Wheel-encoder based GPR surveys are restrained to linear-route only, preventing the use of GPR for accurate localization of city wide underground infrastructure inspection. While GPS signal is degraded in urban canyons and unavailable in city tunnels. In this work, we present a new GPR system integration with augmented reality (AR) based positioning that can overcome the limitations of current GPR systems to enable arbitrary-route scanning with a high fidelity. It has the potential for automation of GPR survey and integration with AR smartphone applications that could be used for better planning in urban development.

This paper investigates a switchable dual-band low noise amplifier design that can operate at X-band and Ka-band using monolithic SiGe heterojunction bipolar transistors (HBT) technology. The LNA is based on a single stage cascade common emitter amplifier with emitter degeneration. Diode connected HBT switch with series-shunt configuration has been utilized for its excellent broadband switch characteristic encompassing X- to Ka-band frequency spectrum. The total switchable dual band LNA dimension is 1.05 × 0.98 mm2, consuming 33 mA from 1.2 V supply (39 mW). To our knowledge, this paper is the first one to discuss feasibility of SiGe integrated switchable dual band LNA at X band and Ka band.

Ground penetrating radar (GPR) has been extensively used as a sensory system for non-destructive evaluation of transportation infrastructure. High operation efficiency is one critical specification for GPR system design. Compressive sensing (CS) coupled orthogonal frequency division multiplexing (OFDM) techniques are utilized in GPR system design to leverage the operating efficiency. OFDM technique enables multi-tone signal transmission and receiving, while CS technique allows sensing with reduced set of frequency tones. The image reconstruction in compressive OFDM GPR is highly dependent on the degree of sparsity of the object signal. A frequency domain clutter removal technique is developed to leverage the sparsity of GPR signal so as to accomplish the higher compression ratio. This technique is evaluated using the real field sensing data.

This letter presents a new transverse electromagnetic flared horn antenna for the demanding requirement of an air-coupled impulse ground-penetrating radar. Structure anatomy is performed focusing on achieving good impedance matching throughout the wide frequency band. The design procedure starts with constructing an analytic model to evaluate the preliminary physical dimensions to achieve minimum reflections. Structural fine tunings are then performed for optimization. The antennas are fabricated and tested. Experimental results validate the design effectiveness.

For through-the-wall-radar signal processing, there exist extensive studies on removing the wall surface reflection signal, while how to eliminate/alleviate the in-wall structure reflection is not well addressed. In many building structures, a layer of reinforced steel bars and utility pipes exist inside the wall which can cause strong clutter to overwhelmingly mask the reflection signal from the targets under test behind the wall. Such clutter cannot be mitigated using the conventional wall clutter removal methods. Thus, a new effective technique to remove the strong inside-wall rebar or pipe reflection is indispensable. Considering the correlated features of the in-wall rebar or pipes and the spatial sparsity of the behind-wall targets under test, a low-rank and sparse representation model-based in-wall clutter suppression algorithm is developed in this letter for target feature enhancement and detection. Experiments on both simulation data and field test data are performed for performance evaluation and validation.

In this paper, we propose a new GPR design method by combining OFDM (Orthogonal Frequency Division Multiplexing) and compressive sampling (CS) techniques. Comparing with traditional SFCW (step frequency continuous wave) radar, OFDM allows transmitting/receiving multiple frequency tones simultaneously to reduce frequency sweeping time. Compressive sampling reduces the number of frequency tones required to further improve GPR operating efficiency. To show the design effectiveness, SFCW GPR and OFDM-CS continuous wave GPR are both simulated in conjunction with FDTD (finite difference time domain) numerical modeling to characterize a concrete slab model.

Testing radar circuitry is challenging which requires characterizing system response over a set range of frequencies. In this research, we propose a method that can effectively leverage radar test speed with little design overhead. Currently radar circuitry is tested mainly through the functional test, where step frequency continuous wave (SFCW) technique is primarily adopted. In SFCW, many individual frequency tones must are generated and applied for testing sequentially. This process is time consuming, especially for modern ultra wideband radar systems. In this project, we develop an alternative solution to replace SFCW testing by utilizing OFDM so that multiple frequency tones can be generated for testing simultaneously. To show test results equivalence, simulations are performed to compare both SFCW and OFDM characterization techniques for ground penetrating radar through a simulated ground channel. The simulation results show good agreements.