In this work, we formalize the concept of differential model robustness (DMR), a new property for ensuring model security in federated learning (FL) systems. For most conventional FL frameworks, all clients receive the same global model. If there exists a Byzantine client who maliciously generates adversarial samples against the global model, the attack will be immediately transferred to all other benign clients. To address the attack transferability concern and improve the DMR of FL systems, we propose the notion of differential model distribution (DMD) where the server distributes different models to different clients. As a concrete instantiation of DMD, we propose the ARMOR framework utilizing differential adversarial training to prevent a corrupted client from launching white-box adversarial attack against other clients, for the local model received by the corrupted client is different from that of benign clients. Through extensive experiments, we demonstrate that ARMOR can significantly reduce both the attack success rate (ASR) and average adversarial transfer rate (AATR) across different FL settings. For instance, for a 35-client FL system, the ASR and AATR can be reduced by as much as 85% and 80% over the MNIST dataset. Full article

In this paper, a fractional frequency division phase-locked loop based on phase interpolation is proposed and implemented using the TSMC 0.11 $\mathsf{\mu }$m CMOS process. Compared with the conventional phase-locked loop, a digital time converter (DTC) module is added to this phase-locked loop, and the DTC module can reduce the fractional spurious by phase interpolation. The circuit and analysis method of this DTC module are given in this paper. Unlike the existing approaches, the proposed DTC is calibration-free, and the error introduced by it is only related to the DAC adopted in the DTC. In addition, the accuracy of the DTC is 8 bits. Finally, this paper verifies the proposed quantization noise reduction technique using a 0.11 $\mathsf{\mu }$m CMOS process. The proposed FNPLL achieves the overall power consumption of 20.3 mW, the noise of $-117$dBc/Hz@1 MHz and $-138$dBc/Hz$@$10 MHz, and the RMS jitter of $0.860$ps. The area of the proposed FDIV is $60\times 245\mathsf{\mu }$m${}^2$, and the power consumption is $1.356$mW. The phase noise of the proposed FNPLL in the fractional division mode is just $2$dB higher than that in the integer division mode. Full article

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method. Full article

Speech emotion recognition (SER) plays a vital role in human–machine interaction. A large number of SER schemes have been anticipated over the last decade. However, the performance of the SER systems is challenging due to the high complexity of the systems, poor feature distinctiveness, and noise. This paper presents the acoustic feature set based on Mel frequency cepstral coefficients (MFCC), linear prediction cepstral coefficients (LPCC), wavelet packet transform (WPT), zero crossing rate (ZCR), spectrum centroid, spectral roll-off, spectral kurtosis, root mean square (RMS), pitch, jitter, and shimmer to improve the feature distinctiveness. Further, a lightweight compact one-dimensional deep convolutional neural network (1-D DCNN) is used to minimize the computational complexity and to represent the long-term dependencies of the speech emotion signal. The overall effectiveness of the proposed SER systems’ performance is evaluated on the Berlin Database of Emotional Speech (EMODB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets. The proposed system gives an overall accuracy of 93.31% and 94.18% for the EMODB and RAVDESS datasets, respectively. The proposed MFCC and 1-D DCNN provide greater accuracy and outpace the traditional SER techniques. Full article

A highly integrated methane-detection system was experimentally established by using a distributed feedback laser diode and hollow-core photonic crystal fiber. The self-developed circuits with a laser diode and essential optical devices were integrated into an instrument that generated a modulated optical signal in a fiber-coupled gas cell that contained the hollow-core photonic crystal fiber. The instrument could also process the return optical signal that contained the gas concentration information. The experiments demonstrated the good performance of the developed system. In the spectrum tests, the center wavelength of the laser diode could be tuned linearly by controlling the laser’s working temperature and driving current. The second harmonic signal could be extracted in order to reflect the gas concentration. According to the Allan deviation method, the low limit of detection of the system was determined to be 29.52 ppm. In addition, a long-term stability test demonstrated that the system has a good stable performance. The proposed system can be further optimized in order to be applied in paddy fields to detect and monitor the methane concentration in a large area by using the optical fibers. Full article

The rise of social networks has greatly contributed to creating information cascades. Overtime, new nodes are added to the cascade network, which means the cascade network is dynamically variable. At the same time, there are often only a few nodes in the cascade network before new nodes join. Therefore, it becomes a key task to predict the diffusion after the dynamic cascade based on the small number of nodes observed in the previous period. However, existing methods are limited for dynamic short cascades and cannot combine temporal information with structural information well, so a new model, MetaCaFormer, based on meta-learning and the Transformer structure, is proposed in this paper for dynamic short cascade prediction. Considering the limited processing capability of traditional graph neural networks for temporal information, we propose a CaFormer model based on the Transformer structure, which inherits the powerful processing capability of Transformer for temporal information, while considering the neighboring nodes, edges and spatial importance of nodes, effectively combining temporal and structural information. At the same time, to improve the prediction ability for short cascades, we also fuse meta-learning so that it can be quickly adapted to short cascade data. In this paper, MetaCaFormer is applied to two publicly available datasets in different scenarios for experiments to demonstrate its effectiveness and generalization ability. The experimental results show that MetaCaFormer outperforms the currently available baseline methods. Full article

In this paper, a novel Monogenic Sobel Directional Pattern (MSDP) using fractional order masks is proposed for extracting features. The MSDP uses fractional-order Sobel masks to identify thin edges along with color and texture-based information thereby increasing performance. Other edge-detection methods can identify only thick edges. There are three modules namely feature extraction, dimension reduction via a novel discriminant analysis method, and classification using a Convolutional Neural Network (CNN). The proposed MSDP is insensitive to the rotation and scaling changes existing in the images. The Bat Algorithm-based Optimization (BAO) is used for the selection of the best parameters of MSDP. The best value is modified by the Pearson Mutation (PM) operator in an effort to aid the algorithm in avoiding local optima and achieving a balance between global and local searches. The proposed work uses CNN for classification and achieves higher classification accuracy for six datasets. Full article

In higher education institutions (HEI), particularly in biology and medical education, the use of 3D animation, virtual reality, and simulation offers great potential in terms of enhancing learning and engaging students. Higher education researchers are still investigating virtual reality’s possibilities and outcomes in various fields. This study focuses on the effects of 3D gamification using an Artificial Intelligence integrated Internet of Medical Things (AIoMT) implemented with virtual reality application for biology and medical students to learn about the human brain. Nowadays, both theoretical and practical education frequently incorporate virtual reality and augmented reality. Virtual tours of the human body’s systems are offered to biology students so that they may comprehend such systems’ functions. This study focuses on the use of 3D animation, virtual reality, and simulation in medical education, with a specific focus on the effects of a 3D gamification app using the Internet of Medical Things (AIoMT) on medical professionals’ passion for learning. This study uses the ARCS model and SEM analysis to examine the impact of virtual reality on students’ motivation and learning. The results show that virtual reality positively impacts motivation and the understanding of the concept-to-execution process through practice and simulation-based training. To assess how well students are learning, what they are analyzing, and how well they can understand the objects of analysis, a 3D-simulation-based and user-feedback-based design has been developed using the proposed research methodology. According to this article’s findings, a smartphone app that uses virtual reality can help medical professionals better understand the concept-to-execution process through practice. VR simulation-based training, as well as Biology teachers or medical colleges, can offer high-definition 3D VR models rather than organs in jars to understand the human anatomy and its functions more experientially and effectively. Full article

Computer memory comprises temporarily or permanently stored data and instructions, which are utilized in electronic digital computers. The opposite of serial access memory is Random Access Memory (RAM), where the memory is accessed immediately for both reading and writing operations. There has been a vast technological improvement, which has led to tremendous information on the amount of complexity that can be designed on a single chip. Small feature sizes, low power requirements, low costs, and great performance have emerged as the essential attributes of any electronic component. Designers have been forced into the sub-micron realm for all these reasons, which places the leakage characteristics front and centre. Many electrical parts, especially digital ones, are made to store data, emphasising the need for memory. The largest factor in the power consumption of SRAM is the leakage current. In this article, a 1 KB memory array was created using CMOS technology and a supply voltage of 0.6 volts employing a 1-bit 6T SRAM cell. We developed this SRAM with a 1-bit, 32- × 1-bit, and 32 × 32 configuration. The array structure was implemented using a 6T SRAM cell with a minimum leakage current of 18.65 pA and an average delay of 19 ns. The array structure was implemented using a 6T SRAM cell with a power consumption of 48.22 μW and 385 μW for read and write operations. The proposed 32 × 32 memory array SRAM performed better than the existing 8T SRAM and 7T SRAM in terms of power consumption for read and write operations. Using the Cadence Virtuoso tool (Version IC6.1.8-64b.500.14) and 22 nm technology, the functionality of a 1 KB SRAM array was verified. Full article

Acquiring biopotentials with fidelity using low-cost circuits is a significant challenge in biomedical instrumentation. In this perspective, our goal is to investigate the characteristics of the widely applied AD8232^®, an analog front-end for biopotential acquisition. We designed and evaluated circuits to acquire the most common biosignals: electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). Our findings show that the circuit is suitable for ECG and EMG instrumentation, although it has limitations for EEG signals, particularly concerning the gain. The entire project of the boards is also a contribution of this work as we intend to corroborate open-source do-it-yourself biomedical instrumentation. Full article

The battery thermal management system plays an important role in the safe operation of a lithium-ion battery system. In this paper, a novel liquid cooling plate with mini-channels is proposed and is improved with disturbance structures. First, an accurate battery heat generation model is established and verified by experiments. The error is less than 4%, indicating the heat generation power is reliable. Then, five designs are proposed first to determine the suitable number of disturbance structures, and plan 3 with five disturbance structures shows a satisfying performance in heat dissipation and flow field. Moreover, four layout plans are proposed, namely uniform, interlaced, thinning, and gradually denser distribution. Results show that plan 5 (uniform) achieves the best performance: the maximum average temperature is 36.33 °C and the maximum average temperature difference is 0.16 °C. At last, the orthogonal experiment and range analysis are adopted to optimize the structure parameters. Results show that the best combination is space between adjacent disturbance structures d1 = 20 mm, length d2 = 5 mm, width d3 = 1.5 mm, and tilt angle β = 60°. Full article

In the era of vigorous development of the Internet of Things (IoT), the IoT has been widely used in people’s daily life. Before the user starts using an IoT product, the developer provides a privacy consent form for the user to fill in. However, the content of the consent form is usually too long for the user to read, and the user neglects the provisions related to privacy use, which often results in personal information being recorded in the database of the product without the user’s knowledge. To protect users’ informed use, we propose a privacy protection standard of the general data protection regulation (GDPR) law applicable to smart-family-related applications and data security with a consensus mechanism. We also propose a unified device data format agreement. Each product can communicate with each other through a smart housekeeper and can collect personal information between its own products and users based on the personal data protection law. Through practice, we demonstrate the feasibility of this open system. In addition, we also collected 70 questionnaires. If the GDPR specification is placed on smart appliances, about 90% of people can accept smart appliances. If smart appliances can be compatible with different brands’ unified standards, about 97% of people can accept smart appliances. Therefore, we recommend the introduction of GDPR specifications for smart home appliances. Full article

This article investigates the possibilities of gas detection using a tapered optical fiber coated with a graphene oxide layer. Measurement is based on changes in light beam propagation depending on the process of gas absorption to the graphene oxide layer. In this paper, we investigated the light change in a double-clad tapered optical fiber in a wide optical range. We present a special platform constructed for the deposition of additional functional materials that enable the preparation of the sensor module. Our results present differences in light transmission for three different kinds of gasses pure nitrogen, pure hydrogen, and a mixture of propane–butane. Measurements were provided in a wide range of 500 nm–1800 nm to find the most sensitive ages for which we are able to detect mentioned absorption and their interaction with light. Obtained results for pure gasses for which the refractive indices are similar to the air show the greatest changes for the visible range 750 nm–850 nm, and for propane–butane, changes are much visible in the whole investigated range. Full article

In this paper, a methodology to assess ground risk with multi-uncertainties is introduced, which is associated with a major unmanned aerial vehicle (UAV) in-flight incident. In the assessment model, random factors are taken into account including uncertainty in the drag force, uncertainty in the UAV velocity, and the random effects of local wind. The probability distribution of impact positions is first estimated by using a second-order drag model with probabilistic assumptions regarding the least well-known parameters. Then, an approach for modeling and estimating the ground risks is presented, in which the ground casualties are set as the safety index. In the multifactor risk estimation model, ground casualty areas covered by the UAVs’ debris are determined. Correspondingly, the probability of fatal injuries to people is derived by addressing the protection effects, impact energy, and energy threshold a person can sustain. Further, four kinds of sheltering effects are defined. Finally, the affected area on the ground is partitioned into six zones, taking into consideration the density and distribution of the local population. Case studies are conducted for fixed-wing and rotary-wing UAVs. Risk levels on the ground are obtained and compared with the widely accepted target safety level of manned aircrafts. Full article

In this paper, we present a non-rigid point cloud matching method based on an invariant structure for face deformation. Our work is guided by the realistic needs of 3D face reconstruction and re-topology, which critically need support for calculating the correspondence between deformable models. Our paper makes three main contributions: First, we propose an approach to normalize the global structure features of expressive faces using texture space properties, which decreases the variation magnitude of facial landmarks. Second, we make a modification to the traditional shape context descriptor to solve the problem of regional cross-mismatch. Third, we collect a dataset with various expressions. Ablation studies and comparative experiments were conducted to investigate the performance of the above work. In face deformable cases, our method achieved 99.89% accuracy on our homemade face dataset, showing superior performance over some other popular algorithms. In this way, it can help modelers to build digital humans more easily based on the estimated correspondence of facial landmarks, saving a lot of manpower and time. Full article