Fractional Frequency Reuse (FFR), which was introduced by 3GPP is considered the powerful technique to improve user performance. However, implementation of FFR is a challenge due to strong dependence between base stations (BSs) in terms of resource allocations. This paper studies a modified and flexible FFR scheme that allows all BSs works independently. The analytical and simulation results prove that the modified FFR scheme outperforms the conventional FFR.

In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWGω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.

This paper introduces a deep reinforcement learning approach to solve the virtual network function scheduling problem in dynamic scenarios. We formulate an integer linear programming model for the problem in static scenarios. In dynamic scenarios, we define the state, action, and reward to form the learning approach. The learning agents are applied with the asynchronous advantage actor-critic algorithm. We assign a master agent and several worker agents to each network function virtualization node in the problem. The worker agents work in parallel to help the master agent make decision. We compare the introduced approach with existing approaches by applying them in simulated environments. The existing approaches include three greedy approaches, a simulated annealing approach, and an integer linear programming approach. The numerical results show that the introduced deep reinforcement learning approach improves the performance by 6-27% in our examined cases.

In this paper, we investigate the Access Point (AP) selection problem in Cell-Free Massive multiple-input multiple-output (MIMO) system. Firstly, we add a connecting coefficient to the uplink data transmission model. Then, the problem of AP selection is formulated as a discrete combinatorial optimization problem which can be dealt with by the particle swarm algorithm. However, when the number of optimization variables is large, the search efficiency of the traditional particle swarm algorithm will be significantly reduced. Then, we propose an ‘user-centric’ cooperative coevolution scheme which includes the proposed probability-based particle evolution strategy and random-sampling-based particle evaluation mechanism to deal with the search efficiency problem. Simulation results show that proposed algorithm has better performance than other existing algorithms.

This paper describes an in-depth analysis of crosstalk in a high-bandwidth 3D-stacked memory using a multi-hop inductive coupling interface and proposes two countermeasures. This work analyzes the crosstalk among seven stacked chips using a 3D electromagnetic (EM) simulator. The detailed analysis reveals two main crosstalk sources: concentric coils and adjacent coils. To suppress these crosstalks, this paper proposes two corresponding countermeasures: shorted coils and 8-shaped coils. The combination of these coils improves area efficiency by a factor of 4 in simulation. The proposed methods enable an area-efficient inductive coupling interface for high-bandwidth stacked memory.

The issue of a low minority class identification rate caused by data imbalance in anomaly detection tasks is addressed by the proposal of a GAN-SR-based intrusion detection model for industrial control systems. First, to correct the imbalance of minority classes in the dataset, a generative adversarial network (GAN) processes the dataset to reconstruct new minority class training samples accordingly. Second, high-dimensional feature extraction is completed using stacked asymmetric depth self-encoder to address the issues of low reconstruction error and lengthy training times. After that, a random forest (RF) decision tree is built, and intrusion detection is carried out using the features that SNDAE retrieved. According to experimental validation on the UNSW-NB15, SWaT and Gas Pipeline datasets, the GAN-SR model outperforms SNDAE-SVM and SNDAE-KNN in terms of detection performance and stability.

This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).

We have developed and evaluated a prototype micro-pump for a new form of medication that is driven by a chemical reaction. The chemical reaction between citric acid and sodium bicarbonate produces carbon dioxide, the pressure of which pushes the medication out. This micropump is smaller in size than conventional diaphragm-type micropumps and is suitable for swallowing.

The application of time-series prediction is very extensive, and it is an important problem across many fields, such as stock prediction, sales prediction, and loan prediction and so on, which play a great value in production and life. It requires that the model can effectively capture the long-term feature dependence between the output and input. Recent studies show that Transformer can improve the prediction ability of time-series. However, Transformer has some problems that make it unable to be directly applied to time-series prediction, such as: (1) Local agnosticism: Self-attention in Transformer is not sensitive to short-term feature dependence, which leads to model anomalies in time-series; (2) Memory bottleneck: The spatial complexity of regular transformation increases twice with the sequence length, making direct modeling of long time-series infeasible. In order to solve these problems, this paper designs an efficient model for long time-series prediction. It is a double pyramid bidirectional feature fusion mechanism network with parallel Temporal Convolution Network (TCN) and FastFormer. This network structure can combine the time series fine-grained information captured by the Temporal Convolution Network with the global interactive information captured by FastFormer, it can well handle the time series prediction problem.

Our group has developed terahertz(THz)-waves emitting devices utilizing single crystals of high temperature superconductor Bi2Sr2CaCu2O8+δ (Bi2212). The working principle of the device is based on the AC Josephson effect which is originated in the intrinsic Josephson junctions (IJJs) constructed in Bi2212 single crystals. In principle, based on the superconducting gap of the compound and the AC Josephson effect, the emission frequency range from 0.1 to 15 THz can be generated by simply adjusting bias voltages to the IJJs. In order to improve the device performances, we have performed continuous improvement to the device structures. In this paper, we present our recent approaches to high performance Bi2212 THz-waves emitters. Firstly, approaches to the reduction of self Joule heating of the devices is described. In virtue of improved device structures using Bi2212 crystal chips, the device characteristics, such as the radiation frequency and the output power, become better than previous structures. Secondly, developments of THz-waves emitting devices using IJJs-mesas coupled with external structures are explained. The results clearly indicate that the external structures are very useful not only to obtain desired radiation frequencies higher than 1 THz but also to control radiation frequency characteristics. Finally, approaches to further understanding of the spontaneous synchronization of IJJs is presented. The device characteristics obtained through the approaches would play important roles in future developments of THz-waves emitting devices by use of Bi2212 single crystals.

Uncovered muck truck detection aims to detect the muck truck and distinguish whether it is covered or not by dust-proof net to trace the source of pollution. Unlike traditional detection problem, recalling all uncovered trucks is more important than accurate locating for pollution traceability. When two objects are very close in an image, the occluded object may not be recalled because the non-maximum suppression (NMS) algorithm can remove the overlapped proposal. To address this issue, we propose a Location First NMS method to match the ground truth boxes and predicted boxes by position rather than class identifier (ID) in the training stage. Firstly, a box matching method is introduced to re-assign the predicted box ID using the closest ground truth one, which can avoid object missing when the IoU of two proposals is greater than the threshold. Secondly, we design a loss function to adapt the proposed algorithm. Thirdly, a uncovered muck truck detection system is designed using the method in a real scene. Experiment results show the effectiveness of the proposed method.

In this paper, we present a novel discriminative dictionary learning (DDL) method for image classification. The local structural relationship between samples is first built by the Laplacian eigenmaps (LE), and then integrated into the basic DDL frame to suppress inter-class ambiguity in the feature space. Moreover, in order to improve the discriminative ability of the dictionary, the category label information of training samples is formulated into the objective function of dictionary learning by considering the discriminative promotion term. Thus, the data points of original samples are transformed into a new feature space, in which the points from different categories are expected to be far apart. The test results based on the real dataset indicate the effectiveness of this method.

Gaze following is the task of estimating where an observer is looking inside a scene. Both the observer and scene information must be learned to determine the gaze directions and gaze points. Recently, many existing works have only focused on scenes or observers. In contrast, revealed frameworks for gaze following are limited. In this paper, a gaze following method using a hybrid transformer is proposed. Based on the conventional method (GazeFollow), we conduct three developments. First, a hybrid transformer is applied for learning head images and gaze positions. Second, the pinball loss function is utilized to control the gaze point error. Finally, a novel ReLU layer with the reborn mechanism (reborn ReLU) is conducted to replace traditional ReLU layers in different network stages. To test the performance of our developments, we train our developed framework with the DL Gaze dataset and evaluate the model on our collected set. Through our experimental results, it can be proven that our framework can achieve outperformance over our referred methods.

Underwater acoustic channels (UWA) are usually sparse, which can be exploited for adaptive equalization to improve the system performance. For the shallow UWA channels, based on the proportional minimum symbol error rate (PMSER) criterion, the adaptive equalization framework requires the sparsity selection. Since the sparsity of the L0 norm is stronger than that of the L1, we choose it to achieve better convergence. However, because the L0 norm leads to NP-hard problems, it is difficult to find an efficient solution. In order to solve this problem, we choose the Gaussian function to approximate the L0 norm. Simulation results show that the proposed scheme obtains better performance than the L1 based counterpart.

We propose a non-photorealistic rendering method to automatically generate reaction-diffusion-pattern-like images from photographic images. The proposed method uses smoothing filter with a circular window, and changes the size of the circular window depending on the position in photographic images. By partially changing the size of the circular window, the size of reaction-diffusion patterns can be changed partially. To verify the effectiveness of the proposed method, experiments were conducted to apply the proposed method to various photographic images.

Thin films of Teflon AF 1600 were prepared by an electron-assisted (e-assist) deposition method. IR analysis revealed that the e-assist deposition generates small amount of polar groups such as carboxylic acid in the molecular structure of the deposited films. The polar groups contributed to increase intermolecular interaction and led to remarkable improvement in the adhesion strength and robustness of the films especially when a bias voltage was applied to the substrate in the course of e-assist deposition. The vapor-deposited Teflon AF films had refractive indices of 1.35 to 1.38, and were effective for antireflection coatings. The use of e-assist deposition slightly increased the refractive index as a trade-off for the improvement of film robustness.

In this study, vertically aligned carbon nanotubes (VA-CNTs) were grown from filler-added LB films with accumulated AlFe2O4 nanoparticles and palmitic acid (C16) as the filler molecule after different hydrogen reduction temperatures of 500°C and 750°C, and the grown VA-CNTs were compared and evaluated. As a result, VA-CNTs were approximately doubled in length after 500°C hydrogen reduction compared to 750°C hydrogen reduction when AlFe2O4 NPs were used. On the other hand, when the catalyst area ratio was decreased by using palmitic acid, i.e., the distance between CNTs was increased, VA-CNTs rapidly shortened after 500°C hydrogen reduction, and VA-CNTs were no longer obtained even in the range where VA-CNTs were obtained in 750°C hydrogen reduction. The inner and outer diameters of VA-CNTs decreased with decreasing catalyst area ratio at 750°C hydrogen reduction and tended to increase at 500°C hydrogen reduction. The morphology of the catalyst nanoparticles after CVD was observed to change significantly depending on the hydrogen reduction temperature and catalyst area ratio. These observations indicate that the state of the catalyst nanoparticles immediately before the CNT growth process greatly affects the physical properties of the CNTs.

An electroactive supercoiled polymer artificial muscle, which is made from a conductive sewing thread using self-coiling caused by inserting a twist with a hanged appropriate weight, is 1/4-1/3 of the thread in length. Therefore, it is necessary to move the weight vertically about two or three times as long as the desired electroactive supercoiled polymer artificial muscle, resulting in a large vertical dimension of the fabrication equipment. This study has attempted to solve this problem by using constant-load springs that enable horizontal table-top fabrication equipment. It has been also demonstrated that inserting a twist into the bundled threads results in a strong electroactive supercoiled polymer artificial muscle.

An enzymatic biofuel cell (BFC) that uses lactic acid in human sweat as fuel to generate electricity is an attractive power source for wearable devices. A BFC capable of generating electricity with human sweat has been developed. It comprised a flexible tattoo seal type battery with silver oxide vapor deposited on a flexible material and conductive carbon nanotubes printed on it. The anode and cathode in this battery were arranged in a plane (planar type). This work proposes a thin laminated enzymatic BFC by inserting a cellulose nanofiber (CNF) sheet between two electrodes to absorb human sweat (stack-type). Optimization of the anode and changing the arrangement of electrodes from planar to stack type improved the output and battery life. The stack type is 43.20μW / cm2 at 180mV, which is 1.25 times the maximum power density of the planar type.

Studies on intrinsic Josephson junctions (IJJs) of cuprate superconductors are reviewed. A system consisting of a few IJJs provides phenomena to test the Josephson phase dynamics and its interaction between adjacent IJJs within a nanometer scale, which is unique to cuprate superconductors. Quasiparticle density of states, which provides direct information on the Cooper-pair formation, is also revealed in the system. In contrast, Josephson plasma emission, which is an electromagnetic wave radiation in the sub-terahertz frequency range from an IJJ stack, arises from the synchronous phase dynamics of hundreds of IJJs coupled globally. This review summarizes a wide range of physical phenomena in IJJ systems having capacitive and inductive couplings with different nanometer and micrometer length scales, respectively.