Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes an image-guided platform for chronic and patient-specific FUS neuromodulation. The system uses custom wearable probes containing separate ultrasound imaging and modulation transducer arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dual-mode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam toward the target. The ultrasound transmit voltage used to excite the modulation array was optimized in real-time by automatically regulating functional feedback signals (the average rates of emulated muscle twitches detected by an on-board motion sensor) through a proportional and integral (PI) controller, thus providing robustness to inter-subject variability and probe positioning errors. The proposed closed-loop neuromodulation paradigm was experimentally demonstrated in vitro using an active tissue phantom that integrates models of the posterior tibial nerve and nearby blood vessels together with embedded sensors and actuators.

Printed circuit Boards (PCBs) are becoming increasingly vulnerable to malicious design alteration, also known as Trojan attacks, due to a distributed business model that often involves various untrusted parties. Such attacks can be mounted at various stages in the PCB life cycle. The relative ease of alteration of PCB hardware even after fabrication (due to physical access to surface-mounted critical components and traces) makes them attractive for an adversary to manipulate their functional/physical behavior for malicious intent. There is a growing need to explore viable Trojan attacks in a PCB, analyze their functional and physical characteristics (e.g., impact on power or delay), and study the effectiveness of countermeasures against these attacks. While simulation-based approaches for PCB Trojan insertion are effective at creating a large population of possible Trojans, they fail to provide functional feasibility analysis with a realistic workload for a trigger circuit. Also, they cannot estimate a Trojan's side-channel footprint due to the unavailability of physical models of diverse PCB components. To address these deficiencies, in this paper, we present PRISTINE, a PCB-level emulation system for any integrity or physical tampering issues, specifically, hardware Trojan insertion. The need for building such an emulation platform to resolve PCB trust issues in the supply chain is also surveyed and discussed. Both custom Hardware Hacking (HaHa) boards and multiple commercial PCBs are then used to test the ability of the proposed system to emulate various hardware Trojans specially designed to exploit board-specific hardware characteristics. Experimental results on emulated board-level Trojans show that a wide range of Trojans can be successfully activated, thus enabling the expected payload effects on both types of boards to be studied and quantified. The resulting data are further analyzed to create PCB-level Trojan benchmarks. In particular, a comparative evaluation of the experimental results is used to propose a risk level metric that quantifies the probability of detection and degree of payload impact of each Trojan on a given commercial PCB.

This paper introduces Fusion Intelligence (FI), a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms such as bees and ants are integrated with the computational power of Artificial Intelligence (AI). This interdisciplinary field seeks to create systems that are not only smart but also adaptive and responsive in ways that mimic the nature. As FI evolves, it holds the promise of revolutionizing the way we approach complex problems, leveraging the best of both biological and digital worlds to create solutions that are more effective, sustainable, and harmonious with the environment. We demonstrate FI's potential to enhance agricultural IoT system performance through a simulated case study on improving insect pollination efficacy (entomophily).

Modern microelectronics life-cycle and supply chain ecosystem bring multiple untrusted entities, which can compromise their integrity.A major integrity issue of microelectronics stems from piracy of intellectual properties (IP) and counterfeiting, which causes significant revenue loss to the semiconductor manufacturers.Further, these components often lead to compromised functionality, reliability, security, and safety of an electronic system.This paper presents secure information transmission and probing methods for verifying the integrity of digital integrated circuit (ICs) based on their electromagnetic (EM) near-field emissions and thereby protecting systems against counterfeit components.The proposed method has been tested on both high-level instructions executed by microprocessors or Systems-on-Chip (serving as examples of software), and also logic circuits within FPGA fabrics and ASICs (serving as examples of hardware).The authentication information required by each digital system is generated using a pseudo-random number generator circuit and securely transmitted via near-field magnetic emissions.The authorized party can probe these emissions using a near-field probe, process the acquired signals to improve the signal-to-noise ratio (SNR), and then recover the secure information through matched filtering.Experimental results from commercial SoCs are used to demonstrate the proposed technique.Methods for reducing EM interference during integrity verification of both FPGAs and ASICs are also described.

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes custom body-conformal probes used within an image-guided platform for chronic and patient-specific FUS neuromodulation. The probes contain separate ultrasound imaging and modulation arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dualmode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam towards the target.

Energy efficiency in digital systems faces challenges due to the constraints imposed by small-scale transistors. Moreover, the growing demand for portable consumer electronics necessitates the use of compact energy sources. To address these challenges, heterogeneous 3D IC technology has emerged as a promising solution for the former. Regarding the latter, we propose the concept of distributed batteries within a heterogeneous 3D IC. This approach involves utilizing multiple smaller batteries with different specifications among different modules of 3D ICs. This approach optimizes performance and overcomes limitations associated with both 3D ICs and conventional power delivery methods. Distributed batteries play a vital role in effectively managing the heat generated by energy sources and modules within a 3D IC. Furthermore, they contribute to achieving a uniform distribution of heat throughout the entire structure, which ultimately ensures the optimal performance of the batteries and modules. The simulation results indicate a 40 percent enhancement in achieving a more even distribution of generated heat. Additionally, the proposed distributed battery techniques improve power delivery, enhance reliability, and enable optimized voltage regulation while improving efficiency. In addition to the primary benefits, alternative configurations of the proposed approach can offer extra energy storage capacity and act as efficient electromagnetic shields, resulting in an impressive reduction of external electromagnetic noises by 60 dB.

This paper describes a wearable, open-source wrist temperature monitoring system that enables the reliable identification of slowly-varying skin temperature patterns that may be indicative of infections. The hardware platform uses a Bluetooth Low Energy (BLE) wireless interface and includes three skin temperature sensors, a thermally-isolated ambient temperature sensor, an inertial measurement unit (IMU), and a Galvanic skin response (GSR) sensor. A template-matching algorithm is used to detect weak but long-lived anomalous temperature patterns that deviate from the normal circadian rhythm are thus may be driven by infections. Experimental and simulation results confirm that small temperature anomalies (peak value <0.4°C) extending over 2–3 weeks can be detected with a total error rate <10%.

The paper describes a device-level encryption approach for implementing intrinsically secure non-volatile memory (NVM) using resistive RAM (ReRAM). Data are encoded in the ReRAM filament morphology, making it robust to both electrical and optical probing methods. The encoded resistance states are randomized to maximize the entropy of the ReRAM resistance distribution, thus providing robustness to reverse engineering (RE) attacks. Simulations of data encryption and decryption using experimental data from Ru(BE)/ALD-HfO2 (MO)/Zr/W(TE) ReRAM devices reveals an uncorrected bit error rate (BER) < 0.02 and a maximum key entropy of ≈17.3 bits per device. A compensation procedure is also developed for maintaining BER in the presence of temperature changes.

Abstract Automatic recognition of unique characteristics of an object can provide a powerful solution to verify its authenticity and safety. It can mitigate the growth of one of the largest underground industries—that of counterfeit goods–flowing through the global supply chain. In this article, we propose the novel concept of material biometrics, in which the intrinsic chemical properties of structural materials are used to generate unique identifiers for authenticating individual products. For this purpose, the objects to be protected are modified via programmable additive manufacturing of built-in chemical “tags” that generate signatures depending on their chemical composition, quantity, and location. We report a material biometrics-enabled manufacturing flow in which plastic objects are protected using spatially-distributed tags that are optically invisible and difficult to clone. The resulting multi-bit signatures have high entropy and can be non-invasively detected for product authentication using $$^{35}$$ 35 Cl nuclear quadrupole resonance (NQR) spectroscopy.

This paper describes a GaNFET-based high-speed charge injection circuit to study fast redox processes at electrode-electrolyte interfaces. The circuit allows the rates of electrode processes, which are much faster than those accessible with a conventional potentiostat, to be measured. It is able to inject charge across the interface within a few nanoseconds, and also to hold the potential generated across the cell following injection for up to 1 s without appreciable (less than 1%) decay. In addition, the circuit can still monitor the current flowing through the cell, as in a conventional potentiostat. Preliminary test results with both a dummy load and a custom two-electrode electrochemical cell confirm the functionality of the proposed circuit.