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This article presents a receiver for narrowband IoT (NB-IoT) that eliminates the need for an RF local oscillator (LO) via a subsampling architecture. A pseudo-balun Q-boosted LNA provides sharp anti-aliasing filtering with a noise figure (NF) of 5.6 dB. A direct-coupling derivative superposition technique where low- $V_{t}$ and thick-gate transistors with opposite nonlinear characteristics are combined to improve the measured IIP3 by 7 dB to −18 dBm with little NF overhead. Fabricated in 65-nm CMOS, the entire receiver, including the LNA, an S/H circuit, and a 10-bit SAR ADC, consumes only 0.69 mW while meeting NB-IoT specifications.

Hybrid switched-capacitor converters (HSCCs) have gained attention due to their promising efficiency and power density compared to traditional inductor- or capacitor-based converters. However, with the recent development of various HSCC topologies, it has become increasingly challenging to choose the most suitable one for a particular application. To address this challenge, this paper proposes a benchmarking framework that enables direct comparison of direct HSCC topologies based on various performance metrics such as passives volume and bandwidth. The proposed approach, which compares all topologies at the same efficiency and output voltage ripple, provides guidelines for topology selection and optimization, ultimately contributing to wider industrial adoption and exploration of new topologies. Downloadable open-access code is also provided to recreate presented results and expand to other topologies not discussed in the paper.

This article introduces a dual-mode backscatter integrated circuit that supports both WiFi and Bluetooth low-energy (BLE) transmissions. It enables a multiantenna WiFi mode with reconfigurable beam steering of single-sideband (SSB) quadrature phase shift-keying (QPSK) signals, while also facilitating omnidirectional SSB BLE-to-BLE backscatter communication. To achieve beam steering, two techniques are proposed: 1) a transmission-line-less fully reflective SP4T backscatter switch is employed to minimize power loss and maximize the communication range and 2) a multiantenna array is constructed using the aforementioned SP4T switches together with a baseband phase-shifting technique to reradiate the incident WiFi signal with a controllable angle of direction. The chip implementation is based on a 65-nm CMOS process and operates at a power consumption of $5.5 \mu \text{W}$ in standby mode. In backscattering mode, it consumes $39 \mu \text{W}$ for the single-antenna approach and $88 \mu \text{W}$ for the multiantenna approach. The proposed design achieves a worst-case access point (AP)-to-AP range of 35 and 56 m for the single-antenna and multiantenna approaches, respectively.

Wake-up receivers (WuRXs) offer a potentially energy-efficient means to enable asynchronous wake-up of higher power and higher performance radios without needing frequent (often energy-expensive) synchronization. Since WuRXs are typically on for a large percentage of the time, keeping their power consumption very low is critical to minimizing the total energy draw. However, this is difficult while maintaining good sensitivity, interference resiliency, and robustness, all with application-appropriate wake-up latencies and form factors. This article reviews the main challenges facing WuRXs, outlines the most popular WuRX architectures, and details essential design techniques and tradeoffs toward enabling utility in emerging applications.

Radios are everywhere. They allow us to watch terrestrial TV broadcasts, connect our cars to satellite-based navigation systems, and connect our computers, phones, and other smart devices to the Internet. As the Internet of Things continues to proliferate, radios will start to connect food packaging, pets, environmental monitors, and all sorts of other things to the Internet as well. A large percentage of these emerging applications will operate on either very small batteries or small energy harvesters, and thus must support all application requirements on very tight power budgets. Since radios often dominate the power consumption of low-power sensing nodes, anything we can do to help reduce the power consumption of wireless communications will help enable these new applications. Of course, this should be accomplished thoughtfully, with careful consideration of coexistence, standards, regulations, security, privacy, and other application-level constraints.

Context: In this paper, we study distributed and decentralized systems in which each part is modeled as an agent in a multi-agent system. Those systems provide more scalable and easier ways to control complex, distributed and interconnected systems of embedded components. We are particularly interested in methods to secure these systems. Objectives: This study aims to identify the main security properties studied, the parts of a multi-agent architecture that are considered most often in security studies and the technical solutions used to secure those systems. Methods: We conducted a systematic mapping study on research works addressing the security of multi-agent systems with embedded agents. We identified which security features were addressed, and their roles in global security architecture. Results: We identified 70 papers published in journals and conferences. We classified the extracted data reporting a tendency to focus on securing the availability of systems under attack by means of trust schemes, sometimes supported by cryptographic primitives. Conclusion: The use of cryptography appears to be limited in decentralized systems. However, solutions should be provided to overcome those limits as other solutions such as trust schemes do not protect the system from the same type of attacks.

Unsupervised matrix completion algorithms mostly model the data generation process by using linear latent variable models. Recently proposed algorithms introduce non-linearity via multi-layer perceptrons (MLP), and self-supervision by setting separate linear regression frameworks for each feature to estimate the missing values. In this article, we introduce an MLP based algorithm called feature-specific neural matrix completion (FSNMC), which combines self-supervised and non-linear methods. The model parameters are estimated by a rotational scheme which separates the parameter and missing value updates sequentially with additional heuristic steps to prevent over-fitting and speed up convergence. The proposed algorithm specifically targets small to medium sized datasets. Experimental results on real-world and synthetic datasets varying in size with a range of missing value percentages demonstrate the superior accuracy for FSNMC, especially at low sparsities when compared to popular methods in the literature. The proposed method has particular potential in estimating missing data collected via real experimentation in fundamental life sciences.

This paper presents a novel framework for the demystification of convolutional deep learning models for time-series analysis. This is a step toward making informed/explainable decisions in the domain of time series, powered by deep learning. There have been numerous efforts to increase the interpretability of image-centric deep neural network models, where the learned features are more intuitive to visualize. Visualization in the domain of time series is significantly challenging, as there is no direct interpretation of the filters and inputs compared with the imaging modality. In addition, a little or no concentration has been devoted to the development of such tools in the domain of time series in the past. TSViz provides possibilities to explore and analyze the network from different dimensions at different levels of abstraction, which includes the identification of the parts of the input that were responsible for a particular prediction (including per filter saliency), importance of the different filters present in the network, notion of diversity present in the network through filter clustering, understanding of the main sources of variation learned by the network through inverse optimization, and analysis of the network's robustness against adversarial noise. As a sanity check for the computed influence values, we demonstrate our results on pruning of neural networks based on the computed influence information. These representations allow the user to better understand the network so that the acceptability of these deep models for time-series analysis can be enhanced. This is extremely important in domains, such as finance, industry 4.0, self-driving cars, health care, and counter-terrorism, where reasons for reaching a particular prediction are equally important as the prediction itself. We assess the proposed framework for interpretability with a set of desirable properties essential for any method in this direction.

Hearing loss is one of the most common conditions affecting older adults worldwide. Frequent complaints from the users of modern hearing aids include poor speech intelligibility in noisy environments and high cost, among other issues. However, the signal processing and audiological research needed to address these problems has long been hampered by proprietary development systems, underpowered embedded processors, and the difficulty of performing tests in real-world acoustical environments. To facilitate existing research in hearing healthcare and enable new investigations beyond what is currently possible, we have developed a modern, open-source hearing research platform, Open Speech Platform (OSP). This paper presents the system design of the complete OSP wearable platform, from hardware through firmware and software to user applications. The platform provides a complete suite of basic and advanced hearing aid features which can be adapted by researchers. It serves web apps directly from a hotspot on the wearable hardware, enabling users and researchers to control the system in real time. In addition, it can simultaneously acquire high-quality electroencephalography (EEG) or other electrophysiological signals closely synchronized to the audio. All of these features are provided in a wearable form factor with enough battery life for hours of operation in the field.