Simple and quick tests at medical clinics have become increasingly important. Magnetic sensing techniques have been developed to detect biomarkers using magnetic nanoparticles in liquid-phase assays. We developed a biomarker assay that involves using an alternating current (AC) susceptibility measurement system that uses functional magnetic particles and magnetic sensing technology. We also developed compact biomarker measuring equipment to enable quick testing. Our assay is a one-step homogeneous assay that involves simply mixing a sample with a reagent, shortening testing time and simplifying processing. Using our compact measuring equipment, which includes anisotropic magneto resistance (AMR) sensors, we conducted high-sensitivity measurements of extremely small amounts of two biomarkers (C-reactive protein, CRP and α-Fetoprotein, AFP) used for diagnosing arteriosclerosis and malignant tumors. The results indicate that an extremely small amount of CRP and AFP could be detected within 15 min, which demonstrated the possibility of a simple and quick high-sensitivity immunoassay that involves using an AC-susceptibility measurement system.

In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. The magnetization signal is modulated by the lateral vibration of the sample on top of a planar differential detection coil of the flux transformer. A pair of primary and excitation coils are utilized to apply an excitation field parallel to the sensitive axis of the detection coil. Using the high-Tc SQUID magnetometer, the magnetization curve of a commercial MNP sample (Resovist) was measured in a logarithmic scale of the excitation field. The PSO inverse technique is then applied to the magnetization curve to construct the magnetic moment distribution. A multimodal normalized log-normal distribution was used in the minimization of the objective function of the PSO inversion technique, and a modification of the PSO search region is proposed to improve the exploration and exploitation of the PSO particles. As a result, a good agreement on the Resovist magnetic core size was obtained between the proposed technique and the non-negative least square (NNLS) inversion technique. The estimated core sizes of 8.0484 nm and 20.3018 nm agreed well with the values reported in the literature using the commercial low-Tc SQUID magnetometer with the SVD and NNLS inversion techniques. Compared to the NNLS inversion technique, the PSO inversion technique had merits in exploring an optimal core size distribution freely without being regularized by a parameter and facilitating an easy peak position determination owing to the smoothness of the constructed distribution. The combination of the high-Tc SQUID magnetometer and the PSO-based reconstruction technique offers a powerful approach for characterizing the MNP core size distribution, and further improvements can be expected from the recent state-of-the-art optimization algorithm to optimize further the computation time and the best objective function value.

The magnetic field resolution of the tunnel magneto-resistive (TMR) sensors has been improving and it reaches below 1.0 pT/Hz0.5 at low frequency. The real-time measurement of the magnetocardiography (MCG) and the measurement of the magnetoencephalography (MEG) have been demonstrated by developed TMR sensors. Although the MCG and MEG have been applied to diagnosis of diseases, the conventional MCG/MEG system using superconducting quantum interference devices (SQUIDs) cannot measure the signal by touching the body, the body must be fixed, and maintenance costs are huge. The MCG/MEG system with TMR sensors operating at room temperature have the potential to solve these problems. In addition, it has the great advantage that it does not require a special magnetic shielded room. Further developments are expected to progress to maximize these unique features of TMR sensors.

In the field of machine learning security, as one of the attack surfaces especially for edge devices, the application of side-channel analysis such as correlation power/electromagnetic analysis (CPA/CEMA) is expanding. Aiming to evaluate the leakage resistance of neural network (NN) model parameters, i.e. weights and biases, we conducted a feasibility study of CPA/CEMA on floating-point (FP) operations, which are the basic operations of NNs. This paper proposes approaches to recover weights and biases using CPA/CEMA on multiplication and addition operations, respectively. It is essential to take into account the characteristics of the IEEE 754 representation in order to realize the recovery with high precision and efficiency. We show that CPA/CEMA on FP operations requires different approaches than traditional CPA/CEMA on cryptographic implementations such as the AES.

The misalignment of a coupler is a significant issue for capacitive wireless power transfer (WPT). This paper presents a capacitive WPT system specifically designed for underwater drones operating in flowing freshwater environments. The primary design features include a capacitive coupler with an opposite relative position between feeding and receiving points on the coupler electrode, two phase compensation circuits, and a load-independent inverter. A stable and energy-efficient power transmission is achieved by maintaining a 90° phase difference on the coupler electrode in dielectrics with a large unloaded quality factor (Q factor), such as in freshwater. Although a 622-mm coupler electrode is required at 13.56MHz, the phase compensation circuits can reduce to 250mm as one example, which is mountable to small underwater drones. Furthermore, the electricity waste is automatically reduced using the constant-current (CC) output inverter in the event of misalignment where efficiency drops occur. Finally, their functions are simulated and demonstrated at various receiver positions and transfer distances in tap water.

The novel patch element shapes with the interdigital and multi-via structures for mushroom-type metasurface reflectors are proposed for controlling the reflection phases. The interdigital structure provides a wide reflection phase range by changing the depth of the interdigital fingers. In addition, the multi-via structure provides the higher positive reflection phases such as near +180°. The sufficient reflection phase range of 360° and the low polarization dependent properties could be confirmed by the electromagnetic field simulation. The metasurface reflector for the normal incident plane wave was designed. The desired reflection angles and sharp far field patterns of the reflected beams could be confirmed in the simulation results. The prototype reflectors for the experiments should be designed in the same way as the primary reflector design of the reflector antenna. Specifically, the reflector design method based on the ray tracing method using the incident wave phase was proposed for the prototype. The experimental radiation pattern for the reflector antenna composed of the transmitting antenna (TX) and the prototype metasurface reflector was similar to the simulated radiation pattern. The effectiveness of the proposed structures and their design methods could be confirmed by these simulation and experiment results.

We study a new transistor-level side-channel leakage caused by charges trapped in between stacked transistors namely residual electric charges (RECs). Building leakage models is important in designing countermeasures against side-channel attacks (SCAs). The conventional work showed that even a transistor-level leakage is measurable with a local electromagnetic measurement. One example is the current-path leak [1], [2]: an attacker can distinguish the number of transistors in the current path activated during a signal transition. Addressing this issue, Sugawara et al. proposed to use a mirror circuit that has the same number of transistors on its possible current paths. We show that this countermeasure is insufficient by showing a new transistor-level leakage, caused by RECs, not covered in the previous work. RECs can carry the history of the gate's state over multiple clock cycles and changes the gate's electrical behavior. We experimentally verify that RECs cause exploitable side-channel leakage. We also propose a countermeasure against REC leaks and designed advanced encryption standard-128 (AES-128) circuits using IO-masked dual-rail read-only memory with a 180-nm complementary metal-oxide-semiconductor (CMOS) process. We compared the resilience of our AES-128 circuits against EMA attacks with and without our countermeasure and investigated an RECs' effect on physically unclonable functions (PUFs). We further extend RECs to physically unclonable function. We demonstrate that RECs affect the performance of arbiter and ring-oscillator PUFs through experiments using our custom chips fabricated with 180- and 40-nm CMOS processes*.

Electromagnetic scattering in a coaxial cable having two flanges and concentric grooves is studied. The associated Weber-Orr transform is used to represent electromagnetic fields in an infinitely long cavity, and the mode-matching method is used to enforce boundary continuity. S-parameters obtained by our approach are compared with the reference solutions, and the characteristics are discussed when geometric parameters are varied. The results show that the proposed model provides cost effective and accurate solutions to the problem.

Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.

Fast switching speed, low power consumption, and good stability are some of the important properties of spin transfer torque assisted voltage controlled magnetic anisotropy magnetic tunnel junction (STT-assisted VCMA-MTJ) which makes the non-volatile full adder (NV-FA) based on it attractive for Internet of Things. However, the effects of process variations on the performances of STT-assisted VCMA-MTJ and NV-FA will be more and more obvious with the downscaling of STT-assisted VCMA-MTJ and the improvement of chip integration. In this paper, a more accurate electrical model of STT-assisted VCMA-MTJ is established on the basis of the magnetization dynamics and the process variations in film growth process and etching process. In particular, the write voltage is reduced to 0.7 V as the film thickness is reduced to 0.9 nm. The effects of free layer thickness variation (γtf) and oxide layer thickness variation (γtox) on the state switching as well as the effect of tunnel magnetoresistance ratio variation (β) on the sensing margin (SM) are studied in detail. Considering that the above process variations follow Gaussian distribution, Monte Carlo simulation is used to study the effects of the process variations on the writing and output operations of NV-FA. The result shows that the state of STT-assisted VCMA-MTJ can be switched under -0.3%≤γtf≤6% or -23%≤γtox≤0.2%. SM is reduced by 16.0% with β increases from 0 to 30%. The error rates of writing ‘0’ in the NV-FA can be reduced by increasing Vb1 or increasing positive Vb2. The error rates of writing ‘1’ can be reduced by increasing Vb1 or decreasing negative Vb2. The reduction of the output error rates can be realized effectively by increasing the driving voltage (Vdd).

In this paper, the author performed an electromagnetic field simulation of a typical bonding wire structure that connects a chip and a package, and evaluated the signal transmission characteristics (S-parameters). In addition, the inductance per unit length was extracted by comparing with the equivalent circuit of the distributed constant line. It turns out that the distributed constant line model is not sufficient because there are frequencies where chip-package resonance occurs. Below the resonance frequency, the conventional low-frequency approximation model was effective, and it was found that the inductance was about 1nH/mm.

A surrogate-based electromagnetic (EM) optimization using neural networks (NNs) is presented for computationally efficient microwave bandpass filter (BPF) design. This paper first describes the forward problem (EM analysis) and the inverse problems (EM design), and the two fundamental issues in BPF designs. The first issue is that the EM analysis is a time-consuming task, and the second one is that EM design highly depends on the structural optimization performed with the help of EM analysis. To accelerate the optimization design, two surrogate models of forward and inverse models are introduced here, which are built with the NNs. As a result, the inverse model can instantaneously guess initial structural parameters with high accuracy by simply inputting synthesized coupling-matrix elements into the NN. Then, the forward model in conjunction with optimization algorithm enables designers to rapidly find optimal structural parameters from the initial ones. The effectiveness of the surrogate-based EM optimization is verified through the structural designs of a typical fifth-order microstrip BPF with multiple couplings.

In this paper, an experimental method is proposed for extracting the primary and secondary parameters of transmission lines with frequency dispersion. So far, there is no report of these methods being applied to transmission lines with frequency dispersion. This paper provides an experimental evaluation means of transmission lines with frequency dispersion and clarifies the issues when applying the proposed method. In the proposed experimental method, unnecessary components such as connectors are removed by using a simple de-embedding method. The frequency response of the primary and secondary parameters extracted by using the method reproduced all dispersion characteristics of a transmission line with frequency dispersion successfully. It is demonstrated that an accurate RLGC equivalent-circuit model is obtained experimentally, which can be used to quantitatively evaluate the frequency/time responses of shielded-FPC with frequency dispersion and to validate RLGC equivalent-circuit models extracted by using electromagnetic field analysis.

In this study, we develop a numerical method for determining transient energy deposition in biological bodies exposed to electromagnetic (EM) pulses. We use a newly developed frequency-dependent finite-difference time-domain (FD2TD) method, which is combined with the fast inverse Laplace transform (FILT) and Prony method. The FILT and Prony method are utilized to transform the Cole-Cole model of biological media into a sum of multiple Debye relaxation terms. Parameters of Debye terms are then extracted by comparison with the time-domain impulse responses. The extracted parameters are used in an FDTD formulation, which is derived using the auxiliary differential equation method, and transient energy deposition into a biological medium is calculated by the equivalent circuit method. The validity of our proposed method is demonstrated by comparing numerical results and those derived from an analytical method. Finally, transient energy deposition into human heads of TARO and HANAKO models is then calculated using the proposed method and, physical insights into pulse exposures of the human heads are provided.

Magnetic fields are often utilized for position sensing of mobile devices. In typical sensing systems, multiple sensors are used to detect magnetic fields generated by target devices. To determine the positions of the devices, magnetic-field data detected by the sensors must be converted to device-position data. The data conversion is not trivial because it is a nonlinear inverse problem. In this study, we propose a machine-learning approach suitable for data conversion required in the magnetic-field-based position sensing of target devices. In our approach, two different sets of training data are used. One of the training datasets is composed of raw data of magnetic fields to be detected by sensors. The other set is composed of logarithmically represented data of the fields. We can obtain two different predictor functions by learning with these training datasets. Results show that the prediction accuracy of the target position improves when the two different predictor functions are used. Based on our simulation, the error of the target position estimated with the predictor functions is within 10cm in a 2m × 2m × 2m cubic space for 87% of all the cases of the target device states. The computational time required for predicting the positions of the target device is 4ms. As the prediction method is accurate and rapid, it can be utilized for the real-time tracking of moving objects and people.

To realize a stable and efficient wireless power transfer (WPT) system that can be used in any environment, it is necessary to inspect the influence of environmental interference along the power transmission path of the WPT system. In this paper, attempts have been made to reduce the influence of the medium with a dielectric and conductive loss on the WPT system using spiral resonators for resonator-coupled type wireless power transfer (RC-WPT) system. An important element of the RC-WPT system is the resonators because they improve resonant characteristics by changing the shape or combination of spiral resonators to confine the electric field that mainly causes electrical loss in the system as much as possible inside the resonator. We proposed a novel dual-spiral resonator as a candidate and compared the basic characteristics of the RC-WPT system with conventional single-spiral and dual-spiral resonators. The parametric values of the spiral resonators, such as the quality factors and the coupling coefficients between resonators with and without a lossy medium in the power transmission path, were examined. For the lossy mediums, pure water or tap water filled with acryl bases was used. The maximum transmission efficiency of the RC-WPT system was then observed by tuning the matching condition of the system. Following that, the transmission efficiency of the system with and without lossy medium was investigated. These inspections revealed that the performance of the RC-WPT system with the lossy medium using the modified shape spiral resonator, which is the dual-spiral resonator proposed in our laboratory, outperformed the system using the conventional single-spiral resonator.

The growing threat of Hardware Trojans (HT) in the System-on-Chips (SoC) industry has given way to the embedded systems researchers to propose a series of detection methodologies to identify and detect the presence of Trojan circuits or logics inside a host design in the various stages of the chip design and manufacturing process. Many state of the art works propose different techniques for HT detection among which the popular choice remains the Side-Channel Analysis (SCA) based methods that perform differential analysis targeting the difference in consumption of power, change in electromagnetic emanation or the delay in propagation of logic in various paths of the circuit. Even though the effectiveness of these methods are well established, the evaluation is carried out on simplistic models such as AES coprocessors and the analytical approaches used for these methods are limited by some statistical metrics such as direct comparison of EM traces or the T-test coefficients. In this paper, we propose two new detection methodologies based on Machine Learning algorithms. The first method consists in applying the supervised Machine Learning (ML) algorithms on raw EM traces for the classification and detection of HT. It offers a detection rate close to 90% and false negative smaller than 5%. In the second method, we propose an outlier/novelty algorithms based approach. This method combined with the T-test based signal processing technique, when compared with state-of-the-art, offers a better performance with a detection rate close to 100% and a false positive smaller than 1%. In different experiments, the false negative is nearly the same level than the false positive and for that reason the authors only show the false positive value on the results. We have evaluated the performance of our method on a complex target design: RISC-V generic processor. Three HTs with their corresponding sizes: 0.53%, 0.27% and 0.09% of the RISC-V processors are inserted for the experimentation. In this paper we provide elaborative details of our tests and experimental process for reproducibility. The experimental results show that the inserted HTs, though minimalistic, can be successfully detected using our new methodology.

In this paper, an ultra-thin wave absorber using a resistive patch array closely-placed in front of a back-metal is designed. The positively large susceptance is required for the patch array to cancel out the negatively large input susceptance of the short-circuited ultra-thin spacer behind the array. It is found that the array needs the gap of 1mm, sheet resistance of less than 20Ω/sq. and patch width of more than 15mm to obtain the zero input susceptance of the absorber with the 1/30 wavelength spacer. Moreover, these parameters were designed considering the electromagnetic coupling between the array and back-metal, and the square patch array absorbers with the thickness from 1/30 to 1/150 wavelength were designed.

In this letter, a method for the construction of polar codes based on the mutual information approximation (MIA) is proposed for the 4Tb/in2 two-dimensional inter-symbol interference (2D-ISI) channels, such as the bit-patterned magnetic recording (BPMR) and two-dimensional magnetic recording (TDMR). The basic idea is to exploit the MIA between the input and output of a 2D detector to establish a log-likelihood ratio (LLR) distribution model based on the MIA results, which compensates the gap caused by the 2D ISI channel. Consequently, the polar codes obtained by the optimization techniques previously developed for the additive white Gaussian noise (AWGN) channels can also have satisfactory performances over 2D-ISI channels. Simulated results show that the proposed polar codes can outperform the polar codes constructed by the traditional methods over 4Tb/in2 2D-ISI channels.

A non-volatile memory (NVM) employing MTJ has a lot of strong points such as read/write performance, best endurance and operating-voltage compatibility with standard CMOS. However, it consumes a lot of energy when writing the data. This becomes an obstacle when applying to battery-operated mobile devices. To solve this problem, we propose an approach to augment the capability of the precision scaling technique for the write operation in NVM. Precision scaling is an approximate computing technique to reduce the bit width of data (i.e. precision) for energy reduction. When writing image data to NVM with the precision scaling, the write energy and the image quality are changed according to the write time and the target bit range. We propose an energy-efficient approximate storing scheme for non-volatile flip-flops and a magnetic random-access memory (MRAM) that allows us to write the data by optimizing the bit positions to split the data and the write time for each bit range. By using the statistical model, we obtained optimal values for the write time and the targeted bit range under the trade-off between the write energy reduction and image quality degradation. Simulation results have demonstrated that by using these optimal values the write energy can be reduced up to 50% while maintaining the acceptable image quality. We also investigated the relationship between the input images and the output image quality when using this approach in detail. In addition, we evaluated the energy benefits when applying our approach to nine types of image processing including linear filters and edge detectors. Results showed that the write energy is reduced by further 12.5% at the maximum.