The blockchain-based federated learning framework has garnered widespread attention to ensure data privacy and trustworthy computing in IoT devices. Improving the accuracy and scalability are paramount for the increasing demands of IoT tasks. However, existing solutions, such as SIFL and ChainFL, utilize traditional single-chain architecture and fixed models, which exhibit limitations in terms of model generalizability and scalability. To overcome the above problems, this paper proposesFedShard, an FL framework that integrates a layered dual-track incentive mechanism and a privacy knowledge distillation module.When implementingFedShard, we address two technical challenges: (1) to ensure efficient training among the numerous clients in the sharding architecture, we propose a layered dual-track incentive mechanism that provides both long- and short-term rewards; and (2) to enhance the framework’s convergence and privacy in sharding-based FL, we design a knowledge distillation module that incorporates local differential privacy. Furthermore, we propose a bucket-based hash ring to manage clients, enabling the framework to adapt to dynamic network environments. To validate the framework’s generalizability and scalability, we implementFedShardon a 48-core high-performance server using Fabric to conduct both on-chain and off-chain experiments. Our comprehensive experiments, comparingFedShardwith SIFL, PEFL, and ChainFL, reveal that our solution outperforms the state-of-the-art methods by achieving a notable 29% increase in accuracy and a 22% improvement in throughput.

Blockchain-based Federated Learning (BCFL) has attracted considerable attention in the intelligent IoT domain for its privacy-preserving and decentralized characteristics. Depending on their applicable scenarios, BCFL frameworks are categorized into two types: synchronous and asynchronous. However, synchronous BCFL struggles with low efficiency in heterogeneous IoT environments, while asynchronous BCFL suffers from slow convergence speed. In additional, Both BCFL incur significant resource consumption from blockchain consensus mechanisms which is unrelated to federated learning tasks, leading to resource wastage and poor scalability, making them unsuitable for large-scale IoT networks. To address these challenges, we propose CoCFL, a novel BCFL framework utilizing multi-chain collaboration. CoCFL introduces two lightweight sub-chains: PoCFL-CChain and PC-CChain, based on different FL strategy. PoCFL-CChain uses a synchronous FL strategy for learning devices with similar performance to generate high-accuracy models, while PC-CChain adopts an asynchronous strategy for heterogeneous devices, which can improving training efficiency. CoCFL assigns devices to suitable sub-chains based on their performance to carry out FL tasks and aggregates the sub-chain models into a global model. This multi-chain collaboration strategy enhances model accuracy and convergence speed and significantly improves the scalability of BCFL. In additional, the consensus mechanisms in CoCFL sub-chains not only maintain the blockchain ledger but also handle FL-related tasks such as detecting poisoning attacks, assigning roles, and distributing incentives. This design not only improving the efficiency of BCFL, but also enhances learning security and ensuring fair incentives. Experiments show that CoCFL improves learning accuracy by 6% and efficiency by 18% over existing BCFL frameworks. It also demonstrates excellent scalability, with time consumption liner decreasing as sub-chains increase, and can withstand up to 40% of poisoning attacks while ensuring fair incentives.

Induction logging through casing (ILTC) is of significant importance to the evaluation of residual oils. Traditional induction logging fails to work in cased wells due to the shielding effect of metallic casings. To date, the feasibility of ILTC remains an open question. In this article, we propose an ILTC based on the detection of lateral waves that carry the formation information. Based on the analytical equations derived to describe the hybrid modes of total magnetic fields that can be detected inside cased wells, numerical analyses aiming to describe the behavior of lateral waves were carried out in both frequency and time domains. The obtained results comply with full-wave simulations and existing scaled experiments. Our results indicate that if an ILTC tool can be properly configured to work in a region dominated by lateral waves, and the casing effect and the primary field of the lateral waves can be effectively compensated, it is feasible to implement an ILTC to detect formation conductivities ranging from 0.01 to 5 S/m.

Hidden electronics possess the risk of both security threat and privacy intrusion. We present a wireless hidden electronic recognition system, through electronic components unique mmWave nonlinear responses to identify the threats. We then evaluate E-Eye's performance and robustness with a controlled experiment and a field study using iconic devices and score the system with metrics. Results prove that E-Eye is an accurate and robust hidden electronic recognition system.

Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification.

It is a challenging task for a continuous-wave Doppler radar sensor (DRS) to linearly separate independent motions without any prior information. In this paper, a low-IF single-input multiple-output (SIMO) DRS system is designed and implemented for motion separation. The SIMO DRS system detects the signals scattered off multiple objects, and a two-step blind motion separation approach is proposed to separate motions from the DRS output. Experiments show that the SIMO DRS and the proposed motion separation approach can successfully separate combinations of triangular, sinusoidal, and random motions, as long as the velocities or initial phases of such motions are different. With the two-step approach sequentially tackling the signal separation and the nonlinear demodulation problems, both small- and large-scale independent motions can be linearly separated. These results imply the potential in practical applications of short-range tracking and localization of moving objects.

In this paper, a noncontact breathing disorder recognition system has been proposed for identifying irregular breathing patterns. The proposed system consists of a Doppler radar-based sensor module and a machine-learning-based breathing disorder recognition module. A custom-designed 2.4-GHz continuous wave digital-IF Doppler radar is utilized as the radar sensor module to accurately capture the time-domain breathing waveform. Then, a recognition module is designed with selected features and optimized classifiers. Four sets of experiments have been carried out to evaluate the proposed system comprehensively. For the laboratorial experiments, the proposed system achieves 94.7% classification accuracy using the linear support vector machine classifier with seven selected features. Results of clinical experiments demonstrate the feasibility of long-term breathing disorder recognition with good accuracy and robustness, and illustrate the potential of the proposed solution for the auxiliary diagnosis of diseases.

While malicious attacks on electronic devices (e-devices) have become commonplace, the use of e-devices themselves for malicious attacks has increased (e.g., explosives and eavesdropping). Modern e-devices (e.g., spy cameras, bugs or concealed weapons) can be sealed in parcels/boxes, hidden under clothing or disguised with cardboard to conceal their identities (named as hidden e-devices hereafter), which brings challenges in security screening. Inspection equipment (e.g., X-ray machines) is bulky and expensive. Moreover, screening reliability still rests on human performance, and the throughput in security screening of passengers and luggages is very limited. To this end, we propose to develop a low-cost and practical hidden e-device recognition technique to enable efficient screenings for threats of hidden electronic devices in daily life. First, we investigate and model the characteristics of nonlinear effects, a special passive response of electronic devices under millimeter-wave (mmWave) sensing. Based on this theory and our preliminary experiments, we design and implement, E-Eye, an end-to-end portable hidden electronics recognition system. E-Eye comprises a low-cost (i.e., under $100), portable (i.e., 11.8cm by 4.5cm by 1.8cm) and light-weight (i.e., 45.5g) 24GHz mmWave probe and a smartphone-based e-device recognizer. To validate the E-Eye performance, we conduct experiments with 46 commodity electronic devices under 39 distinct categories. Results show that E-Eye can recognize hidden electronic devices in parcels/boxes with an accuracy of more than 99% and has an equal error rate (EER) approaching 0.44% under a controlled lab setup. Moreover, we evaluate the reliability, robustness and performance variation of E-Eye under various real-world circumstances, and E-Eye can still achieve accuracy over 97%. Intensive evaluation indicates that E-Eye is a promising solution for hidden electronics recognition in daily life.

Continuous authentication is of great importance to maintain the security level of a system throughout the login session. The goal of this work is to investigate a trustworthy, continuous, and non-contact user authentication approach based on a heart-related biometric that works in a daily-life environment. To this end, we present a novel, continuous authentication system, namely Cardiac Scan, based on geometric and non-volitional features of the cardiac motion. Cardiac motion is an automatic heart deformation caused by self-excitement of the cardiac muscle, which is unique to each user and is difficult (if not impossible) to counterfeit. Cardiac Scan features intrinsic liveness detection, unobtrusiveness, cost-effectiveness, and high usability. We prototype a remote, high-resolution cardiac motion sensing system based on the smart DC-coupled continuous-wave radar. Fiducial-based invariant identity descriptors of cardiac motion are extracted after the radar signal demodulation. We conduct a pilot study with 78 subjects to evaluate Cardiac Scan in accuracy, authentication time, permanence, evaluation in complex conditions, and vulnerability. Specifically, Cardiac Scan achieves 98.61% balanced accuracy (BAC) and 4.42% equal error rate (EER) in a real-world setup. We demonstrate that Cardiac Scan is a robust and usable continuous authentication system.

In this brief, we propose three efficient three-input XOR/XNOR circuits as the most significant blocks of digital systems with a new systematic cell design methodology (SCDM) in hybrid-CMOS logic style. SCDM, which is an extension of CDM, plays the essential role in designing efficient circuits. At first, it is deliberately given priority to general design goals in a base structure of circuits. This structure is generated systematically by employing binary decision diagram. After that, concerning high flexibility in design targets, SCDM aims to specific ones in the remaining three steps, which are wise selections of basic cells and amend mechanisms, as well as transistor sizing. In the end, the resultant three-input XOR/XNORs enjoy full-swing and fairly balanced outputs. They perform well with supply voltage scaling, and their critical path contains only two transistors. They also outperform their counterparts exhibiting 27%-77% reduction in average energy-delay product in HSPICE simulation based on TSMC 0.13-μm technology. The symmetric schematic topologies significantly simplify and minimize the layout, as 26%-32% improvement in area is demonstrated.

Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.

Life detection radar can detect human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance away by penetrating nonmetal mediums (brick walls, ruins, etc.). However, interference is often caused by respiratory movements of the radar's operator when detecting vital signs of another human target. The detection accuracy can be significantly influenced by this kind of interference. In this letter, an experimental setup with a dual-frequency continuous-wave life detection radar is investigated. The system operates with different frequencies of 5.75 and 35 GHz. An adaptive filtering method is applied to suppress the interference caused by the operator's respiratory movements. Experimental results show that this method can effectively suppress respiratory interference and improve detection accuracy.

Noncontact techniques for detecting vital signs have attracted great interest due to the benefits shown in medical monitoring and military applications. A rapid remote evaluation on physiological signal frequencies is needed in search and rescue operations as well as intensive care. However, the presence of respiration harmonics causes aliasing problems to heart-rate estimation, especially when the data volume is limited. By taking advantage of the simple pattern of physiological signals, we propose a stepwise atomic norm minimization method (StANM) to accurately assess the respiration and heartbeat frequencies with a limited data volume. First, the respiration frequency is estimated by the conventional atomic norm minimization. Then the frequencies of respiration harmonics are generated based on the inherent relationship between the fundamental tone and the harmonics. Finally, with the pre-estimated frequencies, we locate the heartbeat frequency by solving a modified atomic norm minimization problem. Simulations and experiments show that the proposed method can accurately estimate physiological frequencies from 6.5-second-long raw data with a 4-Hz sampling rate.

Plenty of evidence has implied that animals like pigeons and sea turtles utilize geomagnetic information for long-distance migration and homing. In this paper, we conduct an ab initio investigation to imitate a migratory animal's geomagnetic homing across a long distance over the surface of the Earth. Based on a simple assumption that such animals have a natural ability to sense and compare the included angle between the vector geomagnetic field (GF) and a specific geographic direction such as the due north, we show that a long-distance geomagnetic navigation (GN) can be accomplished without any assistance of prestored geomagnetic and geographic information, as long as the navigation is inside a vast area where such a spatial angle can uniquely determine a geographic location. Without any requirement of measuring the strength of the GF, this GN will not be visibly affected by the recurrent fluctuation of the GF that severely hampers traditional GN. By introducing the extended-Kalman-filter algorithm, it is able to find a shortcut in the geomagnetic space, overcoming interferences of strong Gaussian and local-area geomagnetic anomaly. We believe that the proposed approach will find a wide range of potential applications in global, local, and indoor navigations based on magnetic field in the future.

On-chip temperature and stress sensors are important for runtime system management techniques tackling thermomechanical reliability issues in 3-D integrated circuits (ICs). However, traditional temperature and stress sensor designs require large calibration overhead to improve accuracy, which incurs significant cost for massive production. To address the challenge, in this paper, we propose a novel temperature-stress cosensor design for 3-D ICs. By exploring the inherent reciprocity of temperature and stress mechanisms, it achieves runtime self-calibration such that no dedicated calibration effort is needed. Simulation results show that the cosensor achieves 0.26°C and 0.43 MPa accuracy on average in temperature and stress measurements, respectively, when evaluated in [60 °C}, 140 ° C]. In addition, the accuracy of self-calibrated sensors remains within 1.1 °C and 2.3 MPa when there exists up to 5% measurement noise, which shows that the self-calibration process is relatively insensitive to various noises.

This paper presents an all-CMOS temperature sensor front-end operating at low supply voltage. The front-end includes a reference voltage generator and a proportional-to-absolute-temperature (PTAT) voltage generator. In order to minimize the errors due to various mismatches, error correction techniques including gain boosting, dynamic element matching, dynamic offset cancellation and clock boosting have been investigated. Detailed analysis and simulation have been presented. Furthermore, experimental results with 0.5 μm implementation demonstrated that the lowest supply voltage is 1.1 V for the reference voltage generator and 1 V for the PTAT voltage generator over the temperature range of -55°C to 125°C with acceptable performance for a CMOS-based temperature sensor front-end. The effectiveness of the error correction techniques are also demonstrated experimentally.

This paper presents transistor-level design of a continuous-time (CT) reconfigurable ΔΣ modulator in a 1.2 V 65 nm CMOS process. Both architectural- and circuit-level power-optimization techniques, such as flexible loop order and quantizer bit, switchable OTA unit cells, and folding flash ADC, are utilized to achieve power efficiency over all bandwidths. As gate leakage current in 65 nm technology becomes prominent, a DAC biasing scheme that is robust to gate leakage current is employed. Simulation results show that the modulator achieves signal-to-noise-and-distortion-ratio (SNDR) of 73.3/76.5/77.4/84.4 dB for 20/10/3/0.5 MHz bandwidth (BW) with power consumption of 23.9/20.7/9.49/7.22 mW, respectively. The respective figure of merit (FOM) equals 0.16/0.19/0.26/0.53 pJ/conv.

This paper describes the use of moderate inversion for selected MOS transistors in a two-stage CMOS amplifier to achieve high voltage gain, low thermal noise, and fast settling time at minimum power consumption. A detailed circuit analysis, implemented in a MATLAB design tool, is presented to find the optimal inversion level for MOS transistors. The analysis and design tool are illustrated for a fully differential, two-stage, 0.5-μm CMOS amplifier having voltage gain >; 80 dB, input-referred thermal noise voltage1/2, gain bandwidth of 100 MHz, phase margin of 58°, and 0.1% settling time <; 15 ns for load capacitances of 6 pF and a supply voltage of 2.5 V. SPICE simulation results confirm that these specifications are achieved at a minimum supply current of 2.42 mA by operating first- and second-stage input transistors in moderate inversion compared to 3.82 mA for operation in strong inversion, resulting in a 37% decrease in power consumption.

A regulated linear dual-tuning differential ring oscillator has been designed and fabricated in UMC 0.13 μm CMOS process for ultra wide band (UWB) applications. The integrated voltage regulator has a robust performance with high power supply rejection ratio to improve phase noise performance at the presence of power supply ripple. The proposed dual-tuning technique enables a wide tuning range covering the entire UWB spectrum and a fast-tuning capability in the fine-tuning range. Measurement results demonstrate that the proposed oscillator has a large tuning range, high tuning linearity and acceptable phase noise, which is attractive for various UWB applications.

This paper presents a CMOS subbandgap reference circuit operating at a low supply voltage. To support low-voltage operation, the subbandgap reference circuit uses nMOS transistors operating in weak inversion and a bulk-driven amplifier. Measurements of a chip fabricated in AMI 0.5 μm CMOS technology show that the subbandgap reference can operate with a 1 V supply voltage to produce a reference voltage of 337 mV with a temperature coefficient of 9.9 ppm/°C and a voltage coefficient of 290 ppm/V over the temperature range of 10 °C to 100°C. Measurements of a chip fabricated in UMC 0.13 μm CMOS technology indicate the capability of operation with a 0.4 V supply voltage.