Miracle-dz/UniFAR-FTUs huggingface dataset:
A Silicon-Based Photosensor With Photosensitive Cavity Structure

This study proposes a novel photodiode with photosensitive cavity structure, realized by simple KOH isotropic etching and ion implantation technology. The main design concept is using the photosensitive surface of the cavity to capture the light rays of the sensing area, so as to further increase sensing performance. A TracePro simulation shows that the light capture effect of a single cavity structure is enhanced as the etch depth increases due to increase in surface sensing area. However, since the etch depth is limited by the wafer thickness, this research proposes to increase the surface area of the photodiode by using an array of micro-cavity units to improve the sensing efficiency while reducing the needed etch depth. The experimental results show that under the same light-receiving conditions, the sensitivity of the photodiode with a single cavity and a multi-cavity is higher than that of the general planar design, by about 20% and 16%. In addition, at a luminance of 0 nt to <inline-formula> <tex-math notation="LaTeX">$80\times 10^{3}$ </tex-math></inline-formula> nt, the linear adaptation rate (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2}$ </tex-math></inline-formula>) of the linear fitted curve is larger than 0.99.
Activity-Aware Data Rate Tuning in Wireless Body Area Networks

This work proposes an Activity-Aware Data Rate Tuning (A2D) scheme for Wireless Body Area Network (WBAN), while considering the criticality of the physiological sensed data. We consider different physical activities of the patients and thereafter, compute their health criticality. Further, on the basis of the health criticality value, the data rate of these physiological sensors are tuned. Depending on the physical activity of a patient, the value sensed by the physiological sensors may change. Consequently, when a healthy person runs, a particular sensor value may be significantly high, even if it is normal, however, the same data reading may be critical for a person who is sitting or standing. Thus, a WBAN is required to be activity-aware in order to measure the correct criticality values. We implemented in a real hardware platform system to show the effectiveness of the proposed scheme. Experimental results show that the proposed scheme is capable of tuning the data rate of different physiological sensors, based on human activity and critical conditions, while ensuring more than 90% of packet delivery ratio in intra-BAN communication and 93% in inter-BAN communication.
Investigation of Checkerboard Pattern for EM Wave Absorber using Broadside SRR

This paper investigates electromagnetics (EM) wave absorber with modified checkerboard surface pattern configured with broadside SRR. There are three proposed absorbers which all were designed on FR-4 substrate with 1.2mm thickness. Each of these absorbers has a single resonant frequency at 8.41 GHz, 9.75 GHz, and 10.65 GHz for absorber A, B, and C, respectively. The three proposed absorbers are used to construct a checkerboard absorber surface pattern with several variations. From the simulation result, we found that a combination of two absorbers with a checkerboard pattern can create dual resonant frequencies. The combination of A and B absorber has a dual resonant frequency at 8.91 GHz and 9.78 GHz of resonant frequencies, the combination of A and C absorber has a dual resonant frequency at 8.9 GHz and 11.08 GHz of resonant frequencies, while the combination of B and C absorber has 9.79 GHz and 10.98 GHz of resonant frequencies. By combining A, B, and C with the proposed combination, the simulation result shows that it can achieve triple resonant frequency at 8.89 GHz, 9.77 GHz, and 10.96 GHz while achieving a minimum return loss of -29.6 dB.
Cooperative Positioning in Vehicular Networks using Angle of Arrival Estimation through mmWave

In this paper, we present a novel cooperative positioning (CP) technique to improve localization accuracy in vehicular environments. The proposed method uses position information obtained from the Global Positioning System (GPS) and Angle-of-Arrivals (AoAs) between the nodes estimated using Millimeter wave (mmWave) V2X communication and MUSIC algorithm. We propose two methods to improve the positioning accuracy: pairwise cooperation and angular Multi-dimensional scaling (AMDS). Simulation results show that the proposed methods outperform other CP methods in the literature.
Mobility Prediction at Points of Interest Using Many-to-one Recurrent Neural Network

With the population of mobile phones, the telecom company has a user’s cellular phone signals and its movement trajectory. So, the company can learn about the user’s activity habits and predict where the user may go next to meet the need of network resource and service management. In this paper, we propose a mobility prediction framework with Many-to-one Recurrent Neural Network (RNN). First, we extract the place that the user frequently visits (i.e., Points of Interest (POI)) from the user’s mobility data through our proposed POI mining method, i.e., acceleration clustering. Then, we propose an adaptive mapping method to map the user’s trajectory to a series of POI. Afterward, we use the RNN with Long Short-Term Memory to learn the user’s POI series. Finally, we evaluate the prediction performance of the proposed scheme on two different real datasets. The performance shows that the prediction accuracy of our scheme outperforms previous works.
Deep Learning for Geometrically-Consistent Angular Power Spread Function Estimation in Massive MIMO

In spatial channel models used in multi-antenna wireless communications, the propagation from a single-antenna transmitter (e.g. a user) to an M-antenna receiver (e.g. a Base Station) occurs through scattering clusters located in the far field of the receiving array. The angular power spread function (APSF) of the corresponding M-dim channel vector describes the angular density of the received signal power at the array. In many applications, such as channel sounding and Uplink Downlink covariance transformation in FDD systems, estimating the APSF is required either implicitly or explicitly. However, the existing literature on the subject has mainly focused on channel covariance estimation from a set of noisy pilot observations. It is also assumed that the APSF consists only of discrete components corresponding to Line-of-Sight (LoS) paths and specular scattering. It turns out that while covariance estimation is a well-posed problem, APSF estimation is a much harder task and is in general ill-posed. The reason is that the propagation environment can also include diffuse scattering elements, resulting in continuous APSF components. Therefore, the APSF is a function belonging to the infinite-dimensional space of nonnegative measures over the angle domain. In this paper, we show that under a geometrically-consistent, group-sparse structure on the APSF, which is prevalent in massive MIMO channels, one is able to estimate the APSF properly. We propose an algorithm based on deep neural networks (DNNs) that learns this structure and yields precise APSF estimates, even when the number of available pilot observations is relatively small. We empirically show that our proposed method outperforms the state-of-the-art method in various performance metrics.
Robust Beamforming for Multi-User MISO Full-Duplex SWIPT System Under Non-Linear Energy Harvesting Model

In this article, we investigate a multi-user multiple-input-single-output full-duplex system with simultaneous wireless information and power transfer under the non-linear energy harvesting model, where channel state information is imperfect. Since the channel estimation error is considered in our proposed system, the self-interference cannot be canceled perfectly. For such a system, a robust optimization problem that maximizes the minimum average total harvested power by jointly optimizing the uplink and downlink beamforming matrices is formulated. As the problem is non-linear and non-convex under channel estimation errors, a novel Newton method based iterative optimization algorithm is proposed to solve it efficiently. In each iteration, the semidefinite relaxation technique and S-Procedure method are applied to obtain the optimal beamforming vectors, which are proved to be optimal solutions with rank-one. Simulation results reveal that the proposed robust design can achieve better system performance than the non-robust scheme, both of which are based on the non-linear energy harvesting model.
Efficient Handover Algorithm in 5G Networks using Deep Learning

In 5G networks, microcells are densely deployed for the spatial reuse to cooperate with the traditional macrocells, and thus a moving user equipment (UE) usually experiences a more irregular change of the signal-to-interference-plus-noise ratio (SINR) and is more likely to disconnect to the serving cells when proceeding a handover. Hence, efficient handover algorithms in 4G networks no longer perform well in 5G networks. In addition, deep learning is a common method able to deliver highly accurate classification results for classification problems. In this paper, we make the first attempt to consider the SINR change of a UE in the handover problem in 5G networks, and to reduce the handover problem to a classification problem and solve the classification problem using a deep neural network (DNN). Simulations show that the proposed algorithm has a good performance in terms of the radio link failure rate and the ping-pong rate in 5G networks, as compared with the state-of-the-art methods.
Resource Allocation based on Graph Neural Networks in Vehicular Communications

In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a node in the graph. We apply a graph neural network (GNN) to learn the low-dimensional feature of each node based on the graph information. According to the learned feature, multi-agent reinforcement learning (RL) is used to make spectrum allocation. Deep Q-network is utilized to learn to optimize the sum capacity of the V2X network. Simulation results show that the proposed allocation scheme can achieve near-optimal performance.
Blind Timing Synchronization for DCO-OFDM VLC Systems

In this paper, we propose a blind direct current bias (DCB) based timing synchronization and a blind null subcarrier (NS) based timing synchronization methods for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communications (VLC) systems. This is the first work to investigate blind timing synchronization for DCO-OFDM VLC systems, achieving high bandwidth efficiency, unlike the previous works which require a number of pilots. The two blind approaches are robust against the limited bandwidth of light emitting diode (LED), as the timing synchronization is conducted in frequency domain to mitigate the effect of inter-symbol-interference (ISI) caused by LED limited bandwidth, rather than being performed in time domain as the previous works that are vulnerable to the ISI. The DC bias is utilized by the proposed DCB based approach to perform blind timing synchronization, and the null subcarrier is used by the proposed NS based approach. Simulation results show that the proposed blind DCB and NS timing synchronization approaches significantly outperform the state-of-the-art methods in terms of the probability of false detection and bit error rate (BER), and yield BER performance close to ideal case with perfect synchronization, zero forcing (ZF) equalization and perfect channel state information (CSI).
Distributed Multi-agent Cooperative Resource Sharing Algorithm in Fog Networks

Compared with traditional cloud computing technology, fog computing provides lower latency services in the next generation mobile networks. However, the imbalance of workloads and computing resources among Fog Nodes (FNs) restricts the further promotion of the network service quality. In this paper, considering the resource sharing of FNs by cooperation where FNs are as agents, we propose a distributed multi-agent cooperative resource sharing (DMCRS) algorithm to minimize the average service latency of the fog network. Firstly, the multi-agent resource sharing problem of the fog network is formulated as a convex optimization problem and the convexity is proved. Then the coalitional graph game framework is applied to achieve the cooperation among agents and the coalition graph Nash equilibrium is proved. To reduce the complexity, the global optimization problem is decomposed into distributed local optimization sub-problems in the DMCRS algorithm and these sub-problems are transformed into root-finding problems of polynomial equations. Simulation results demonstrate that the DMCRS algorithm can balance the workloads of different FNs, reduce the average service latency remarkably and the convergence result of the algorithm is close to the global optimal solution. In addition, the time complexity of the DMCRS algorithm is significantly lower than existing algorithms.
A Scalable Stateful Approach for Virtual Security Functions Orchestration

Previous works suggested different approaches to implementing service chaining. Their goal is to enhance the performance of the middleboxes and satisfy the expectations of the cloud providers and users. To meet these expectations, the delay factor, i.e., flow through the low-cost paths, as well as the best node processing factor, are considered. Achieving these two goals simultaneously turns the middlebox optimal placement into an NP-hard problem. Therefore, when the problem size is large, it is infeasible to obtain an optimal solution at a reasonable time. One of the important issues which has not been considered in the previous works is stateful optimal placement when receiving a new request. Due to resource constraints as well as financial costs for the customers, it is not possible to create functions for all requests. Therefore, not only it is possible to integrate the same network functions between new flows, but it will also be examined between new on-demand network functions as well as existing ones. Our proposed approach not only reduces the creation of network functions that can be cost-effective for the customer but also because of the migration of previous network functions (integration with on-demand network functions) to optimize new requests, overall, it will optimize the entire network cost over time. We formulated the problem as 0-1 programming problem. The results of this article are based on a fat-tree data center. To show that our stateful solution is scalable in large networks, we use network zoning and topology partitioning heuristics. Our simulations show that we were able to scale our placement model to a network with 54K nodes and 1.5M edges.
Performance of MPPT-Based Minimum Phase Bipolar Converter for Photovoltaic Systems

The change in environmental conditions and dynamic loading distort the smooth and stable MPPT operation. Conventionally, boost and boost-derived converters are used for MPPT operations, which inherently display right half plane zero (RHPZ). The presence of RHPZ causes a transient undershoot and overshoot in the output voltages. A tradeoff between bandwidth and stability is another issue with such converters. In order to address these issues, a minimum-phase bipolar converter (MPBC) is proposed to facilitate the MPPT operation. The MPBC is derived by combining the Ćuk and SEPIC converters with some circuit modifications in both of them. These circuit modifications eliminate RHPZ from the proposed MPBC. Due to the minimum-phase behavior, the proposed MPBC can achieve high bandwidth with improved phase margin, which is required for the fast and stable MPPT operation. MPBC can operate at the high load current and provides wide operating ranges for the MPPT operation due to its buck and boost capabilities. The transient analysis of the proposed MPBC is carried out to compare its performance with the conventional bipolar converter. A 600-W scaled-down laboratory prototype is developed and implemented using Texas Instrument TMS320F28335 processor to validate the performance of the MPBC for photovoltaic systems.
Utility Function based Power Control Scheme in Femtocell Network

In dense heterogeneous networks, each femtocell base station transmits signals at the maximum power in order to meet the needs of its users. This will inevitably cause interference to macro base station users and other femtocell base station users. Therefore, it is necessary to control the transmission power to reduce interference and improve the quality of service. In this paper, a distributed utility function based power control scheme is adopted to reduce the transmit power of femtocell base station. The transmit power of femtocell base station can be adjusted independently to maximize the corresponding utility function. The simulation results show that the scheme can not only improve the system throughput, but also improve the performance of femtocell users. At the same time, femtocell users can meet the SINR lower limit requirements, and the overall system performance is improved.
Hybrid-Mode Multiple Access for UAV-BS Assisted Communications with UL-DL Rate Balancing

In this paper, we propose an unmanned aerial vehicle (UAV) base station (BS) assisted communication system for a special event (e.g., a football game) with heterogeneous traffic demands by all users and the uplink (UL)-downlink (DL) rate balancing requirement. With respect to UAV’s high mobility, we propose a hybrid mode multiple access (HMMA) strategy where both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques are utilized to meet heterogeneous traffic demands. Specifically, NOMA is utilized to achieve high average data rate, and OMA helps to meet the instantaneous rate demands of users. The proposed HMMA strategy has a high degree of freedom and provides superior minimum average rate across all users and a higher user fairness than the previous work with OMA only or NOMA only, where the instantaneous rate demands of users may not always be guaranteed during UAV’s flight time due to dynamic channel changes, the inter-user interference and successive interference cancellation (SIC) error propagation. Furthermore, we investigate joint UL-DL optimization for a UAV assisted wireless system. Based on the channel reciprocity of the air-ground channels, an alternative algorithm is proposed to conduct joint UL-DL optimization of bandwidth assignment and UAV trajectory to accommodate heterogeneous rate demands across users and achieve quasi-balanced average rates in UL and DL.
LoRaWAN Intelligent Outdoor Smart Street Lighting

Nowadays, The roads have increased the number of streetlights for the roads vehicles/pedestrians, which raises investment and energy. Observations made to obtain most of the road lights are always active at night, even when there are no vehicles or pedestrians on the road. The problems that occur are the waste of energy sources that are used in streetlights. This research designs the concept of intelligent traffic flow based (LED) for energy optimization, maximum efficiency. This concept uses intelligent light architecture using the LoRaWAN Mesh network. The application of this concept offers system reliability, reduces costs, and makes user satisfaction. The results of this study are demonstrated by experimenting with comparing conventional LED lights. The proposed system, resulting in 33% to 62% energy savings depending on when the usage process in streetlights. Smart lighting LEDs with LoRaWAN provide a remote-control mechanism that can be dynamically adjusted based on environmental conditions, distance, and automatic motion.
Deep Convolution Neural Network Implementation for Emotion Recognition System

In this paper, we present a facial recognition system based on deep learning model. The proposed work is implemented on the embedded system named Raspberry Pi 4. For this, the “Xception convolutional neural network” model is chosen to achieve our emotion system recognition. The registered facial images are taken as input into the classifiers in the embedded system which classifies them into seven facial expressions. For the conduction of the experiment “Fer 2013” data set is used. The proposed model gives an accuracy of 94 % in Graphics Processing Unit” (GPU). After his implementation on the embedded system, according to his limitation against GPU performances, The accuracy is 89% on Raspberry Pi 4. Comparing to other recent works
Design and Analysis of a Linear Digital Variable Stiffness Actuator

In this paper, a novel variable stiffness mechanism (VSM) based on specially designed S-springs made from shape memory alloy (SMA) is developed. Based on the stiffness model, by changing the state combination of the SMA S-springs with different thicknesses, the actuator’s stiffness can be discretely adjusted online. The stiffness adjustment range of the actuator can be conveniently extended almost from 0 N/m to infinity by changing the preset spring angle manually offline without a complex mechanical mechanism or replacing any parts. A linear digital variable stiffness actuator (LDVSA) is also designed with the VSM. Some tests of the SMA S-springs, as well as the actuator’s stiffness under different configurations, comparisons with other actuators and its trajectory tracking capacity are conducted to verify its design. Then, a dynamic model of the actuator is established and its bandwidth is analysed. Based on this, explosive experiments are designed and performed to explore the application potential of the actuator. Experimental results are provided to illustrate the explosive capacity and energy efficiency of the proposed design.
StyleGAN-LSRO Method for Person Re-Identification

In this study, the StyleGAN-LSRO method has been developed for person re-identification (re-ID) tasks. This method applies the style-based generative adversarial network (StyleGAN) to generate new synthetic images from existing person re-ID datasets and the label smoothing regularization for outliers (LSRO) algorithm to process those newly produced unlabeled images by assigning them a uniform label distribution along with the definition of a loss function for the training process. A baseline model based on a convolutional neural network (CNN) was developed to learn the discriminative features to recognize a person’s identity. The developed method has been tested on three datasets. These datasets are Market-1501, DukeMTMC-reID, and MSMT17. The experimental results show that the StyleGAN model achieved a Fréchet inception distance score of 12.67 and structural similarity score of 0.387, outperforming all the previous generative methods and demonstrating that the images generated by StyleGAN are of superior quality. Adding these StyleGAN-generated data significantly improves the person re-ID accuracy. The StyleGAN-LSRO person re-ID method achieved 98.5% rank-1 accuracy and 91.8% mean average precision (mAP) on Market-1501, 87.0% rank-1 accuracy and 83.8% mAP on DukeMTMC-reID, and 81.5% rank-1 accuracy and 60.9% mAP on MSMT17, respectively. These results show that the StyleGAN-LSRO method significantly outperforms most of the state-of-the-art person re-ID methods. The success rate for person re-ID increases when the images used are of high resolution and square matrix form. In other cases, the success rate decreases.