In the software testing phase, software reliability growth models (SRGMs) are commonly used to evaluate the reliability of software systems. Traditional SRGMs are restricted by their assumption of a continuous growth pattern for the failure detection rate (FDR) throughout the testing phase. However, the assumption is compromised by Change-Point phenomena, where FDR fluctuations stem from variations in testing personnel or procedural modifications, leading to reduced prediction accuracy and compromised software reliability assessments. Therefore, the objective of this study is to improve software reliability prediction using a novel approach that combines genetic algorithm (GA) and deep learning-based SRGMs to account for the Change-point phenomenon. The proposed approach uses a GA to dynamically combine activation functions from various deep learning-based SRGMs into a new mutated SRGM called MuSRGM. The MuSRGM captures the advantages of both concave and S-shaped SRGMs and is better suited to capture the change-point phenomenon during testing and more accurately reflect actual testing situations. Additionally, failure data is treated as a time series and analyzed using a combination of Long Short-Term Memory (LSTM) and Attention mechanisms. To assess the performance of MuSRGM, we conducted experiments on three distinct failure datasets. The results indicate that MuSRGM outperformed the baseline method, exhibiting low prediction error (MSE) on all three datasets. Furthermore, MuSRGM demonstrated remarkable generalization ability on these datasets, remaining unaffected by uneven data distribution. Therefore, MuSRGM represents a highly promising advanced solution that can provide increased accuracy and applicability for software reliability assessment during the testing phase.

Modern high-level programming languages have a wide variety of grammar and can implement the required functionality in different ways. The authors believe that a large amount of code that implements the same functionality in different ways exists even in open source software where the source code is publicly available, and that by collecting such code, a useful data set can be constructed for various studies in software engineering. In this study, we construct a dataset of pairs of Java methods that have the same functionality but different structures from approximately 314 million lines of source code. To construct this dataset, the authors used an automated test generation technique, EvoSuite. Test cases generated by automated test generation techniques have the property that the test cases always succeed. In constructing the dataset, using this property, test cases generated from two methods were executed against each other to automatically determine whether the behavior of the two methods is the same to some extent. Pairs of methods for which all test cases succeeded in cross-running test cases are manually investigated to be functionally equivalent. This paper also reports the results of an accuracy evaluation of code clone detection tools using the constructed dataset. The purpose of this evaluation is assessing how accurately code clone detection tools could find the functionally equivalent methods, not assessing the accuracy of detecting ordinary clones. The constructed dataset is available at github (https://github.com/YoshikiHigo/FEMPDataset).

A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address this gap, retraining of neural network models triggered by concept drift detection is a practical approach. However, since available compute resources are strictly limited in edge devices, in this paper we propose a fully sequential concept drift detection method in cooperation with an on-device sequential learning technique of neural networks. In this case, both the neural network retraining and the proposed concept drift detection are done only by sequential computation to reduce computation cost and memory utilization. We use three datasets for experiments and compare the proposed approach with existing batch-based detection methods. It is also compared with a DNN-based approach without concept drift detection. The evaluation results of the proposed approach show that the proposed method is capable of detecting each of four concept drift types. The results also show that, while the accuracy is decreased by up to 0.9% compared to the existing batch-based detection methods, it decreases the memory size by 88.9%-96.4% and the execution time by 45.0%-87.6%. As a result, the combination of the neural network retraining and the proposed concept drift detection method is demonstrated on Raspberry Pi Pico that has 264 kB memory.

We explore the maximum total number of edge crossings and the maximum geometric thickness of the Euclidean minimum-weight (k, ℓ)-tight graph on a planar point set P. In this paper, we show that (10/7－ε)|P| and (11/6－ε)|P| are lower bounds for the maximum total number of edge crossings for any ε ＞ 0 in cases (k,ℓ)=(2,3) and (2,2), respectively. We also show that the lower bound for the maximum geometric thickness is 3 for both cases. In the proofs, we apply the method of arranging isomorphic units regularly. While the method is developed for the proof in case (k,ℓ)=(2,3), it also works for different ℓ.

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 recent years, measuring biomagnetic fields in the Earth’s field by differential measurements of scalar-mode OPMs have been actively attempted. In this study, the sensitivity of the scalar-mode OPMs under the geomagnetic environment in the laboratory was studied by numerical simulation. Although the noise level of the scalar-mode OPM in the laboratory environment was calculated to be 104 pT/$\sqrt{\mathrm{Hz}}$, the noise levels using the first-order and the second-order differential configurations were found to be 529 fT/cm/$\sqrt{\mathrm{Hz}}$ and 17.2 fT/cm2/$\sqrt{\mathrm{Hz}}$, respectively. This result indicated that scalar-mode OPMs can measure very weak magnetic fields such as MEG without high-performance magnetic shield roomns. We also studied the operating conditions by varying repetition frequency and temperature. We found that scalar-mode OPMs have an upper limit of repetition frequency and temperature, and that the repetition frequency should be set below 4 kHz and the temperature should be set below 120°C.

A 0.13mJ/prediction with 68.6% accuracy wired-logic deep neural network (DNN) processor is developed in a single 16-nm field-programmable gate array (FPGA) chip. Compared with conventional von-Neumann architecture DNN processors, the energy efficiency is greatly improved by eliminating DRAM/BRAM access. A technical challenge for conventional wired-logic processors is the large amount of hardware resources required for implementing large-scale neural networks. To implement a large-scale convolutional neural network (CNN) into a single FPGA chip, two technologies are introduced: (1) a sparse neural network known as a non-linear neural network (NNN), and (2) a newly developed raster-scan wired-logic architecture. Furthermore, a novel high-level synthesis (HLS) technique for wired-logic processor is proposed. The proposed HLS technique enables the automatic generation of two key components: (1) Verilog-hardware description language (HDL) code for a raster-scan-based wired-logic processor and (2) test bench code for conducting equivalence checking. The automated process significantly mitigates the time and effort required for implementation and debugging. Compared with the state-of-the-art FPGA-based processor, 238 times better energy efficiency is achieved with only a slight decrease in accuracy on the CIFAR-100 task. In addition, 7 times better energy efficiency is achieved compared with the state-of-the-art network-optimized application-specific integrated circuit (ASIC).

The subject must be motionless for conventional radar-based non-contact vital signs measurements. Additionally, the measurement range is limited by the design of the radar module itself. Although the accuracy of measurements has been improving, the prospects for their application could have been faster to develop. This paper proposed a novel radar-based adaptive tracking method for measuring the heart rate of the moving monitored person. The radar module is fixed on a circular plate and driven by stepping motors to rotate it. In order to protect the user’s privacy, the method uses radar signal processing to detect the subject’s position to control a stepping motor that adjusts the radar’s measurement range. The results of the fixed-route experiments revealed that when the subject was moving at a speed of 0.5 m/s, the mean values of RMSE for heart rate measurements were all below 2.85 beat per minute (bpm), and when moving at a speed of 1 m/s, they were all below 4.05 bpm. When subjects walked at random routes and speeds, the RMSE of the measurements were all below 6.85 bpm, with a mean value of 4.35 bpm. The average RR interval time of the reconstructed heartbeat signal was highly correlated with the electrocardiography (ECG) data, with a correlation coefficient of 0.9905. In addition, this study not only evaluated the potential effect of arm swing (more normal walking motion) on heart rate measurement but also demonstrated the ability of the proposed method to measure heart rate in a multiple-people scenario.

Handover is an important property of cellular communication that enables the user to move from one cell to another without losing the connection. It is a very crucial process for the quality of the user’s experience because it may interrupt data transmission. Therefore, good handover management is very important in the current and future cellular systems. Several techniques have been employed to improve the handover performance, usually to increase the probability of a successful handover. One of the techniques is predictive handover which predicts the target cell using some methods other than the traditional measurement-based algorithm, including using machine learning. Several studies have been conducted in the implementation of predictive handover, most of them by modifying the internal algorithm of existing network elements, such as the base station. We implemented a predictive handover algorithm using an intelligent node outside the existing network elements to minimize the modification of the network and to create modularity in the system. Using a recently standardized Open Radio Access Network (O-RAN) Near Realtime Radio Intelligent Controller (Near-RT RIC), we created a modular application that can improve the handover performance by determining the target cell using machine learning techniques. In our previous research, we modified The Near-RT RIC original software that is using vector autoregression to determine the target cell by predicting the throughput of each neighboring cell. We also modified the method using a Multi-Layer Perceptron (MLP) neural network. In this paper, we redesigned the neural network using Long Short-Term Memory (LSTM) that can better handle time series data. We proved that our proposed LSTM-based machine learning algorithms used in Near-RT RIC can improve the handover performance compared to the traditional measurement-based algorithm.

With maturation of 5G technology in recent years, multimedia services such as live video streaming and online games on the Internet have flourished. These multimedia services frequently require low latency, which pose a significant challenge to compute the high latency requirements multimedia tasks. Mobile edge computing (MEC), is considered a key technology solution to address the above challenges. It offloads computation-intensive tasks to edge servers by sinking mobile nodes, which reduces task execution latency and relieves computing pressure on multimedia devices. In order to use MEC paradigm reasonably and efficiently, resource allocation has become a new challenge. In this paper, we focus on the multimedia tasks which need to be uploaded and processed in the network. We set the optimization problem with the goal of minimizing the latency and energy consumption required to perform tasks in multimedia devices. To solve the complex and non-convex problem, we formulate the optimization problem as a distributed deep reinforcement learning (DRL) problem and propose a federated Dueling deep Q-network (DDQN) based multimedia task offloading and resource allocation algorithm (FDRL-DDQN). In the algorithm, DRL is trained on the local device, while federated learning (FL) is responsible for aggregating and updating the parameters from the trained local models. Further, in order to solve the not identically and independently distributed (non-IID) data problem of multimedia devices, we develop a method for selecting participating federated devices. The simulation results show that the FDRL-DDQN algorithm can reduce the total cost by 31.3% compared to the DQN algorithm when the task data is 1000 kbit, and the maximum reduction can be 35.3% compared to the traditional baseline algorithm.

Despite the appealing advantages of reconfigurable intelligent surfaces (RIS) aided mmWave communications, there remain practical issues that need to be addressed before the large-scale deployment of RISs in future wireless networks. In this study, we jointly consider the non-neglectable practical issues in a multi-RIS-aided mmWave system, which can significantly affect the secrecy performance, including the high computational complexity, imperfect channel state information (CSI), and finite resolution of phase shifters. To solve this non-convex challenging stochastic optimization problem, we propose a robust and low-complexity algorithm to maximize the achievable secrete rate. Specially, by combining the benefits of fractional programming and the stochastic successive convex approximation techniques, we transform the joint optimization problem into some convex ones and solve them sub-optimally. The theoretical analysis and simulation results demonstrate that the proposed algorithms could mitigate the joint negative effects of practical issues and yielded a tradeoff between secure performance and complexity/overhead outperforming non-robust benchmarks, which increases the robustness and flexibility of multiple RIS deployments in future wireless networks.

In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.

In this study, we propose a novel dataset distillation method based on parameter pruning. The proposed method can synthesize more robust distilled datasets and improve distillation performance by pruning difficult-to-match parameters during the distillation process. Experimental results on two benchmark datasets show the superiority of the proposed method.

Cooperative Communication is one of the most effective techniques to improve the desired signal quality of the typical user. This paper studies an indoor cellular network system that deploys the Reconfigurable Intelligent Surfaces (RIS) at the position of BSs to enable the cooperative features. To evaluate the network performance, the coverage probability expression of the typical user in the indoor wireless environment with presence of walls and effects of Rayleigh fading is derived. The analytical results shows that the RIS-assisted system outperforms the regular one in terms of coverage probability.

Direct-current biased optical orthogonal frequency division multiplexing (DCO-OFDM) converts bipolar OFDM signals into unipolar non-negative signals by introducing a high DC bias, which satisfies the requirement that the signal transmitted by intensity modulated/direct detection (IM/DD) must be positive. However, the high DC bias results in low power efficiency of DCO-OFDM. An adaptively biased optical OFDM was proposed, which could be designed with different biases according to the signal amplitude to improve power efficiency in this letter. The adaptive bias does not need to be taken off deliberately at the receiver, and the interference caused by the adaptive bias will only be placed on the reserved subcarriers, which will not affect the effective information. Moreover, the proposed OFDM uses Hartley transform instead of Fourier transform used in conventional optical OFDM, which makes this OFDM have low computational complexity and high spectral efficiency. The simulation results show that the normalized optical bit energy to noise power ratio (Eb(opt)/N0) required by the proposed OFDM at the bit error rate (BER) of 10-3 is, on average, 7.5 dB and 3.4 dB lower than that of DCO-OFDM and superimposed asymmetrically clipped optical OFDM (ACO-OFDM), respectively.