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Univariate data has been the focus of optimal design for generalized linear models. It is common practice to conduct experiments with multiple dependent responses that are modelled using regression techniques. To design the experiment for all these responses, one needs a multivariate distribution supporting a previously chosen data model. The T‐optimal criterion is used to discriminate between two competing models. The designs that are optimal for discrimination between two models may be unsuitable for describe the quality of predictions for test data. G‐optimal criterion overcomes this restriction by minimizing the maximum variance of the predicted values. The design of experiments with dependent bivariate binary data is a topic we discuss in this paper. For dependent bivariate binary data, a technique for deriving TG‐optimal experimental designs using Copula is shown. The new compound TG‐criterion satisfy the dual aim to selecting the true model and minimizing the maximum prediction variance. The equivalence theorem is demonstrated and proved for the new compound criterion. A numerical example is presented to show the properties of the new compound criterion for bivariate binary setting and its value in meeting the dual aims. [ABSTRACT FROM AUTHOR]

In the age of the artificial intelligence of things (AIoT), wearable devices have been extensively developed for smart healthcare. This paper proposes a self‐powered and self‐sensing lower‐limb system (SS‐LS) with negative energy harvesting and motion capture for smart healthcare. The SS‐LS achieves self‐sustainability via a half‐wave electromagnetic generator (HW‐EMG) that recovers negative work from walking with a low cost of harvesting. Additionally, the motion capture function of the system is achieved by the three‐channel triboelectric nanogenerator (TC‐TENG) based on binary code, which can accurately detect the angle and direction of the knee joint rotation. The bench test experiment indicates that the HW‐EMG has an average output power of 11.2 mW, sufficient to power a wireless sensor. The three‐channel voltage signal of TC‐TENG fits well with the binary signal, which can precisely detect the angle and direction of rotation. Furthermore, the SS‐LS demonstrates an identification accuracy of 99.68% and a motion detection accuracy of 99.96% based on an LSTM deep learning model. Demonstrations of Parkinson's disease and fall detection and monitoring of three training modes (sit‐and‐stand, balance, and walking training) are also performed, which exhibit outstanding sensing capabilities. The SS‐LS is highly promising in sports rehabilitation medicine and can contribute to the development of smart healthcare. [ABSTRACT FROM AUTHOR]

This paper presents a low‐power self‐contained overvoltage detector. Compared with conventional detectors that consist of stacked Zener diodes, the proposed structure can trim the threshold with binary codes to accommodate system‐level changes. The proposed structure enables trimming by adding a decoder, a biasing circuit, and switches in parallel with Zener diodes. The newly configured structure forms a standalone detector that can operate independently and be powered from the supply under detection with negligible current addition. The proposed structure is applicable to any high‐voltage process that contains N‐type isolation layer and needs no high‐voltage device. The proposed circuit with three bits adjustment circuit was fabricated in a 0.18 μm Bipolar‐CMOS‐DMOS (BCD) process, the overvoltage threshold can be adjusted from 8 to 38 V with post trim variation of ±2.5 V. The measured quiescent current was around 0 μA at 10 V and 28 μA at 38 V, which was comparable to the current consumption in a conventional structure with 38 V overvoltage threshold. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Based on the relaxation digital‐to‐analog converter (ReDAC) architecture and the capacitor charge/discharge function under discrete time conditions, this paper proposes a new mathematical model for the output voltage of the ReDAC to implement the unconstrained relaxation DAC (Uc‐ReDAC). The system clock calibration is not required. An adaptive probabilistic binary particle swarm optimization (AP‐BPSO) algorithm is proposed for solving the binary coding configuration problem under the Uc‐ReDAC model. Simulation results show that the 8‐bit (10‐bit) Uc‐ReDAC has an average sampling rate of 7.9 MS/s (6.76 MS/s). [ABSTRACT FROM AUTHOR]

Combinatorial t‐designs have nice applications in coding theory, finite geometries, and several engineering areas. A classical method for constructing t‐designs is by the action of a permutation group that is t‐transitive or t‐homogeneous on a point set. This approach produces t‐designs, but may not yield (t+1)‐designs. The objective of this paper is to study how to obtain 3‐designs with 2‐transitive permutation groups. The incidence structure formed by the orbits of a base block under the action of the general affine groups, which are 2‐transitive, is considered. A characterization of such incidence structure to be a 3‐design is presented, and a sufficient condition for the stabilizer of a base block to be trivial is given. With these general results, infinite families of 3‐designs are constructed by employing almost perfect nonlinear functions. Some 3‐designs presented in this paper give rise to self‐dual binary codes or linear codes with optimal or best parameters known. Several conjectures on 3‐designs and binary codes are also presented. [ABSTRACT FROM AUTHOR]

The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis. [ABSTRACT FROM AUTHOR]

Semisupervised classification aims to construct a classifier by making use of both labeled data and unlabeled data. This paper proposes a semisupervised classifier based on a piecewise linear regression model implemented by using a gated linear network. The semisupervised classifier is constructed in two steps. In the first step, instead of estimating the break points of a piecewise linear model directly, a label‐guided autoencoder‐based semisupervised gating mechanism is designed to generate binary gate control signals to realize the partitioning. In the second step, the piecewise linear model is first transformed into linear regression form, and the linear parameters are then optimized globally by a Laplacian regularized least squares (LapRLS) algorithm using a kernel function comprising the gate control signals obtained in the first step. Moreover, the composed kernel function is used as a better similarity function for the graph construction. As a result, we capture data manifold from both labeled and unlabeled data, and the data manifold is ingeniously incorporated into both the kernel and the graph Laplacian in LapRLS. Numerical experiments on various real‐world datasets exhibit the effectiveness of the proposed method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

A single‐element antenna is unfit for application in most wireless systems and an alternative is an array of antenna. The desire to reduce weight and cost of antenna arrays gave rise to sparse arrays. The design of a sparse antenna array requires an optimization process, which is time‐consuming for large arrays. In order to accelerate the optimization process, a method combining the numerical Green's function (NGF) and genetic algorithm (GA) is presented in this paper. In the proposed method, binary coding is applied to describe the status of antenna elements, and GA optimization is performed to sparsify the array subject to constraint on the peak side lobe level (PSLL). The PSLL is calculated efficiently by the NGF. Simulation results are presented to illustrate the advantage of the proposed method. It is shown that the proposed method significantly reduces the optimization time. [ABSTRACT FROM AUTHOR]

Summary: Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, we present examples of codes all of whose weight classes support 1‐designs, with duals whose classes include two that support 2‐designs. We can find these examples in the triply even binary codes of length 48, which have been classified by Betsumiya and Munemasa. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a high dynamic range digital pixel sensor (DPS) is presented. Each pixel receives light illumination and converts the intensity level to a digital code. The analog-to-digital conversion is performed in-pixel. The pixel structure incorporates light to pulse signal converter and compact in-pixel dynamic counter to convert the pulse signal to binary coded data. Different variations of the custom design dynamic counters are analyzed in a 0.18 µm technology, and their application in the DPS is investigated. It is shown that due to the limited clock frequency required in the sensor pixel, the dynamic counter can be incorporated as a pulse to binary code converter. Performance specifications such as power consumption, dynamic range and resolution of the presented structure are investigated using a 100 × 100 pixel sensor. The presented sensor is an effective solution for use in digital field programmable smart image sensors and vision chips since the pixel output is a regular binary code while no global digital counter bus is required. The fill factor of the presented design remains close to that of typical DPSs. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multiple sets of measurements on the same objects obtained from different platforms may reflect partially complementary information of the studied system. The integrative analysis of such data sets not only provides us with the opportunity of a deeper understanding of the studied system but also introduces some new statistical challenges. First, the separation of information that is common across all or some of the data sets and the information that is specific to each data set is problematic. Furthermore, these data sets are often a mix of quantitative and discrete (binary or categorical) data types, while commonly used data fusion methods require all data sets to be quantitative. In this paper, we propose an exponential family simultaneous component analysis (ESCA) model to tackle the potential mixed data types problem of multiple data sets. In addition, a structured sparse pattern of the loading matrix is induced through a nearly unbiased group concave penalty to disentangle the global, local common, and distinct information of the multiple data sets. A Majorization‐Minimization–based algorithm is derived to fit the proposed model. Analytic solutions are derived for updating all the parameters of the model in each iteration, and the algorithm will decrease the objective function in each iteration monotonically. For model selection, a missing value–based cross validation procedure is implemented. The advantages of the proposed method in comparison with other approaches are assessed using comprehensive simulations as well as the analysis of real data from a chronic lymphocytic leukaemia (CLL) study. Here, we propose an exponential family SCA (ESCA) model for the integrative analysis of multiple data sets of mixed data types, such as quantitative, binary, or count, and introduce group concave penalty to induce structured sparsity on the loading matrix to separate common (global and local) and distinct variation in such data sets. The model is applied for the exploratory data analysis of quantitative drug response, gene expression, methylation, and binary mutation data sets measured on the same objects. [ABSTRACT FROM AUTHOR]

The data rate of a Tomlinson-Harashima (TH) precoder is limited by the delay in the feedback path of its infinite impulse response (IIR) filter that cancels the channel's post-cursor intersymbol interference (ISI). One reason for long loop delays is the requirement to perform the ISI subtraction from the data signal in binary arithmetic in order to properly execute the modulo (MOD) operation, which stabilises the IIR filter and limits the transmitter amplitude. The restriction to binary arithmetic is unfortunate, because some filter subcomponents such as, e.g. adder trees are often implemented in a faster data format that has no slow carry propagation [e.g. the carry-save adder (CSA) format]. This paper proposes a novel architecture for TH precoders in which the MOD operation is taken out of the loop, so that the remaining IIR filtering is performed entirely in CSA arithmetic to reduce the feedback delay as no conversion to binary format is required within the loop. With the pipelining technique applied, the filter's feedback delay reduces to the propagation delay of a multiply-accumulate unit operated with CSA arithmetic. The proposed concept was verified in a 14 nm CMOS test chip with multi-level signalling (NRZ, 4-/8-/16-PAM). [ABSTRACT FROM AUTHOR]