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.

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 this paper, belief propagation (BP) multi-input multi-output (MIMO) detection with maximum ratio combining (MRC) and minimum mean square error (MMSE) pre-cancellation is proposed for overload MIMO. The proposed scheme applies MRC before MMSE pre-cancellation. The BP MIMO detection with MMSE pre-cancellation leads to a reduction in diversity gain due to the decreased number of connections between variable nodes and observation nodes in a factor graph. MRC increases the diversity gain and contributes to improve bit error rate (BER) performance. Numerical results obtained through computer simulation show that the BERs of the proposed BP MIMO detection with MRC and MMSE pre-cancellation yields bit error rates (BERs) that are approximately 0.5dB better than those of conventional BP MIMO detection with MMSE pre-cancellation at a BER of 10-3.

Far-field directivity analysis method where the FDTD method is used to calculate the near-field and then calculating far-field from the near-field has been used practically in wide variety of fields. MR/FDTD method is a simulation method derived from the FDTD method and can provide several advantages to the FDTD method. When combined with the far-field analysis, it theoretically can provide several advantages against the conventional method. In this paper, far-field analysis method that uses MR/FDTD method is introduced and its effectiveness is verified against the conventional method through numerical simulations.

We assess how well the recently created MRI reconstruction technique, Multi-Resolution Convolutional Neural Network (MRCNN), performs in the core medical vision field (classification). The primary goal of MRCNN is to identify the best k-space undersampling patterns to accelerate the MRI. In this study, we use the Figshare brain tumor dataset for MRI classification with 3064 T1-weighted contrast-enhanced MRI (CE-MRI) over three categories: meningioma, glioma, and pituitary tumors. We apply MRCNN to the dataset, which is a method to reconstruct high-quality images from under-sampled k-space signals. Next, we employ the pre-trained VGG16 model, which is a Deep Neural Network (DNN) based image classifier to the MRCNN restored MRIs to classify the brain tumors. Our experiments showed that in the case of MRCNN restored data, the proposed brain tumor classifier achieved 92.79% classification accuracy for a 10% sampling rate, which is slightly higher than that of SRCNN, MoDL, and Zero-filling methods have 91.89%, 91.89%, and 90.98% respectively. Note that our classifier was trained using the dataset consisting of the images with full sampling and their labels, which can be regarded as a model of the usual human diagnostician. Hence our results would suggest MRCNN is useful for human diagnosis. In conclusion, MRCNN significantly enhances the accuracy of the brain tumor classification system based on the tumor location using under-sampled k-space signals.

This study proposes a new asynchronous Multi-Rate Multiple-Access Differential Chaos Shift Keying (MRMA-DCSK) scheme, ensuring significant data rates for all users. This scheme assigns a unique chaos sequence with a different length to each user. During the first data transmission period, each user transmits the chaos sequence as the reference signal, followed by multiple data bits by sharing the same reference signal in subsequent periods. The proposed scheme affects the bit-error-rate (BER) performance with the number of users, data rate related parameters (L), and length of chaos signals. The simulation results are verified by the derived analysis and show that the proposed scheme achieves higher data rates (from 1/2 to L/L+1) than a conventional scheme while enhancing bit-error-rate (BER) performance.

At present, the application of different types of memristors in electronics is being deeply studied. Given the nonlinearity characterizing memristors, a circuit with memristors cannot be treated by classical circuit analysis. In this paper, memristor is equivalent to a nonlinear dynamic system composed of linear dynamic system and nonlinear static system by Volterra series. The nonlinear transfer function of memristor is derived. In the complex frequency domain, the n-order complex frequency response of memristor is established by multiple Laplace transform, and the response of MLC parallel circuit is taken as an example to verify. Theoretical analysis shows that the complex frequency domain analysis method of memristor transforms the problem of solving nonlinear circuit in time domain into n times complex frequency domain analysis of linear circuit, which provides an idea for nonlinear dynamic system analysis.

In this paper, we study Hermitian linear complementary dual (abbreviated Hermitian LCD) rank metric codes. A class of Hermitian LCD generalized Gabidulin codes are constructed by qm-self-dual bases of Fq2m over Fq2. Moreover, the exact number of qm-self-dual bases of Fq2m over Fq2 is derived. As a consequence, an upper bound and a lower bound of the number of the constructed Hermitian LCD generalized Gabidulin codes are determined.

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.

We propose a compact magnetic tunnel junction (MTJ) model for circuit simulation by de-facto standard SPICE in this paper. It is implemented by Verilog-A language which makes it easy to simulate MTJs with other standard devices. Based on the switching probability, we smoothly connect the adiabatic precessional model and the thermal activation model by using an interpolation technique based on the cubic spline method. We can predict the switching time after a current is applied. Meanwhile, we use appropriate physical models to describe other MTJ characteristics. Simulation results validate that the model is consistent with experimental data and effective for MTJ/CMOS hybrid circuit simulation.

Reference current used in sense amplifiers is a crucial factor in a single-end read manner for emerging memories. Dummy cell average read scheme uses multiple pairs of dummy cells inside the array to generate an accurate reference current for data sensing. The previous research adopts current mirror sense amplifier (CMSA) which is compatible with the dummy cell average read scheme. However, clamped bit-line sense amplifier (CBLSA) has higher sensing speed and lower power consumption compared with CMSA. Therefore, applying CBLSA to dummy cell average read scheme is expected to enhance the performance. This paper reveals that direct combination of CBLSA and dummy cell average read scheme leads to sense margin degradation. In order to solve this problem, a new array design is proposed to make CBLSA compatible with dummy cell average read scheme. Current mirror structure is employed to prevent CBLSA from being short-circuited directly. The simulation result shows that the minimum sensible tunnel magnetoresistance ratio (TMRR) can be extended from 14.3% down to 1%. The access speed of the proposed sensing scheme is less than 2 ns when TMRR is 70% or larger, which is about twice higher than the previous research. And this circuit design just consumes half of the energy in one read cycle compared with the previous research. In the proposed array architecture, all the dummy cells can be always short-circuited in totally isolated area by low-resistance metal wiring instead of using controlling transistors. This structure is able to contribute to increasing the dummy cell averaging effect. Besides, the array-level simulation validates that the array design is accessible to every data cell. This design is generally applicable to any kinds of resistance-variable emerging memories including STT-MRAM.

We performed a time dependent percolation analysis of the degradation phenomena in ultra-thin CoFeB/MgO/CoFeB magnetic tunneling junctions. The objective was to understand the microscopic degradation physics of coherent tunneling and the thickness limitation of the MgO barrier. We propose two models: a trap assisted tunneling (TAL) model and a filamentary defect assisted leakage (FAL) model. The correlation between resistance drift behavior and barrier lifetime was then calculated and compared with real data based on these models. The relationship between the resistance drift behavior and barrier lifetime was found to be well explained by the TAL model, the random trap formation in the barrier and the percolation path formation which lead to barrier breakdown. Based on the TAL model, the measured TDDB Weibull slope (β) was smaller than the value estimated by the model. By removing the effect of some initial defects in the barrier, an ultra-thin MgO tunneling barrier in MTJ has the potential for a much better lifetime with a better Weibull slope even at 3ML thickness. This method is rather simple but useful to deeply understand the microscopic degradation physics in dielectric films under TDDB stress.

Double modular redundancy (DMR) is to execute operations twice and detect soft error by comparing the operation results. The error is corrected by executing necessary operations again. For the DMR design of conditional processing, a method is proposed which makes the secondary executions of the duplicated operations be dependent on the primary execution of the condition operation, thereby widening the schedule solution space and allowing better results to be derived. The energy minimization with the proposed method is formulated as ILP models and the optimum solution is obtained by using an ILP solver.

When the track density of two-dimensional magnetic recording (TDMR) systems is increased, intertrack interference (ITI) inevitably grows, resulting in the extreme degradation of an overall system performance. In this work, we present coding, writing, and reading techniques which allow TDMR systems with multi-readers to overcome severe ITI. A rate-5/6 two-dimensional (2D) modulation code is adopted to protect middle-track data from ITI based on cross-track data dependence. Since the rate-5/6 2D modulation code greatly improves the reliability of the middle-track, there is a bit-error rate gap between middle-track and sidetracks. Therefore, we propose the different track width writing technique to optimize the reliability of all three data tracks. In addition, we also evaluate the TDMR system performance using an user areal density capability (UADC) as a main key parameter. Here, an areal density capability (ADC) can be measured by finding the bit-error rate of the system with sweeping track and linear densities. The UADC is then obtained by removing redundancy from the ADC. Simulation results show that a system with our proposed techniques gains the UADC of about 4.66% over the conventional TDMR systems.

Energy consumption is one of the important issues in communication networks, and it is expected that network devices such as network interface cards will be turned off to decrease the energy consumption. Moreover, fast failure recovery is an important issue in large-scale communication networks to minimize the impact of failure on data transmission. In order to realize both low energy consumption and fast failure recovery, a method called LE-MRC (Low-Energy based Multiple Routing Configurations) has been proposed. However, LE-MRC can degrade network robustness because some links ports are turned off for reducing the energy consumption. Nevertheless, network robustness is also important for maintaining the performance of data transmission and the network functionality. In this paper, for realizing both low energy consumption and fast failure recovery while maintaining network robustness, we propose Robustness and Low-Energy based Multiple Routing Configurations (RLE-MRC). In RLE-MRC, some links are categorized into unnecessary links, and those links are turned off to lower the energy consumption. In particular, the number of excluded links is determined based on the network robustness. As a result, the energy consumption can be reduced so as not to degrade the network robustness significantly. Simulations are conducted on some network topologies to evaluate the performance of RLE-MRC. We also use ns-3 to evaluate how the performance of data transmission and network robustness are changed by using RLE-MRC. Numerical examples show that the low energy consumption and the fast failure recovery can be achieved while maintaining network robustness by using RLE-MRC.

In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer's disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods.

This paper concerned the research on a memristive chaotic system and the generated random sequence; by constructing a piecewise-linear memristor model, a kind of chaotic system is constructed, and corresponding numerical simulation and dynamical analysis are carried out to show the dynamics of the new memristive chaotic system. Finally the proposed memristive chaotic system is used to generate random sequence for the possible application in encryption field.

As LSI chips integrate more transistors and the operating power supply voltage decreases, LSI chips are becoming more vulnerable to the soft error caused by neutrons induced from cosmic rays. The soft error is detected by comparing the duplicated operation results in double modular redundancy (DMR) and the error is corrected by re-executing necessary operations. In this paper, based on the error recovery scheme of re-executing necessary operations, the minimization of the vote operations for error checking with respect to given resource constraints is considered. An ILP model for the optimal solution to the problem is presented and a heuristic algorithm is proposed to minimize the vote operations.

Intensity modulated radiation therapy (IMRT), which irradiates doses to a target organ, calculates the irradiation dose using the radiation treatment planning system (RTPS). The irradiation quality is ensured by verifying that the dose distribution planned by RTPS is the same as the data measured by two-dimensional (2D) detectors. Since an actual three-dimensional (3D) distribution of irradiated dose spreads complicatedly, it is different from that of RTPS. Therefore, it is preferable to evaluate by using not only RTPS, but also actual irradiation dose distribution. In this paper, in order to perform a dose-volume histogram (DVH) evaluation of the irradiation dose distribution, we propose a method of correcting the dose distribution of RTPS by using sparsely measured radial data from 2D dose detectors. And we perform a DVH evaluation of irradiation dose distribution and we show that the proposed method contributes to high-precision DVH evaluation. The experimental results show that the estimates are in good agreement with the measured data from the 2D detectors and that the peak signal to noise ratio and the structural similarity indexes of the estimates are more accurate than those of RTPS. Therefore, we present the possibility of an evaluation of the actual irradiation dose distribution using measured data in a limited observation direction.

We investigate linear complementary dual (LCD) rank-metric codes in this paper. We construct a class of LCD generalized Gabidulin codes by a self-dual basis of an extension field over the base field. Moreover, a class of LCD MRD codes, which are obtained by Cartesian products of a generalized Gabidulin code, is constructed.