REV Journal on Electronics and Communications
REV Journal on Electronics and Communications (REV-JEC) ISSN: 1859-378X REV-JEC is a peer-reviewed quarterly research journal established by the Radio and Electronics Association of Vietnam (REV), an official professional body recognized by the Vietnamese Government in 1988 that brings together professors, researchers, engineers, and technologists working in electronics, control systems, measurements, signal and image processing, telecommunications, and networks. The journal is sponsored by REV, the IEEE Communications Society Vietnam Chapter, and the IEEE SSCS Vietnam Chapter. It serves as a leading-edge forum for researchers and professionals to contribute and disseminate innovative research ideas and results in electronics and communications. Scope: electronics, integrated circuits and solid state, control systems, communications, networks, signal and image processing, and coding. Recognition: Each paper published in REV-JEC receives the maximum rating of 1.0 from the Vietnamese State Council for Professorship, and the journal is working toward Scopus indexing. The journal reflects REV's broader mission to strengthen Vietnam's research community and connect Vietnamese researchers with international colleagues, a goal also pursued through its annual International Conference on Advanced Technologies in Communications (ATC), held in collaboration with IEEE ComSoc and indexed in IEEE Xplore since 2008.
Intelligent AutoEncoder-Based Modulation: Optimizing Transmission Performance over Non-Ideal Wireless Channels
This paper presents an End-to-End wireless transceiver architecture based on deep AutoEncoder (AE) networks that jointly optimizes the transmitter and receiver as a single differentiable system, replacing the conventional cascade of independently designed signal processing blocks. The channel model incorporates three concurrent non-ideal impairments: nonlinear distortion from the power amplifier (PA) characterized by the Rapp model, progressive carrier frequency offset (CFO), and flat Rayleigh fading. Through the training process and testing scenarios across three progressively evolving architectures, namely single-symbol constellation shaping, multi-symbol blind CFO compensation, and implicit neural forward error correction (Neural FEC), the obtained results confirm that the AE is capable of autonomously learning PAresilient signal constellations, performing blind CFO estimation without pilot signals, and unifying the modulation and channel coding processes into a single optimal system representation. Monte Carlo BER simulations show that the proposed architecture achieves 3–5 dB SNR gain over conventional 16-QAM with ZF equalization, provides 2–3 dB gain relative to ideally CFO-compensated 16-QAM, and the Neural FEC configuration successfully performs the channel coding function, exhibiting effective error correction performance.
De Tien Lai, Nghia Xuan Pham, Trung Duc Tran
Enhancing FMCW Radar-Based Human Activity Recognition using DI-ResNet
This paper proposes a method to enhance the quality of Human Activity Recognition (HAR) based on Frequency Modulated Continuous Wave (FMCW) radar. The method utilizes a Dual Input-ResNet (DI-ResNet) model to address the limitations associated with relying solely on isolated range or micro-Doppler (m-D) features for activity classification. Specifically, two parallel ResNet-18 backbones are employed to extract deep semantic features from two distinct data domains: the Range-Time (RT) spectrogram and the Doppler-Time (DT) spectrogram. These features are subsequently fused via a concatenation layer to synthesize global context, thereby generating a more comprehensive representation of the performed activities. Experimental results demonstrate that the proposed method achieves superior recognition performance compared to single-stream baseline networks. Notably, the proposed model effectively mitigates confusion among activities exhibiting kinematic similarity, improving the Recall metric for the “SitDown” activity by 42.1% compared to traditional single-input methods. Furthermore, the accuracy for complex hand gestures such as "Drink" significantly increased by 19.8%, substantially minimizing the high misclassification rate associated with "Grab." Finally, the model achieves near-ideal reliability for safety-critical activities, attaining a recognition accuracy of 99.5% in fall detection, thereby confirming its potential for practical deployment.
Binh Ngoc Nguyen, Nghia Minh Pham, Cuong Huu Thieu
Efficient Multimodal Feature Refinement via Adaptive RGB-IR Interaction for Robust Drone Detection and Classification
The rapid proliferation of unmanned aerial vehicles (UAVs) has intensified the need for robust surveillance systems capable of distinguishing drones from biological entities like birds in unpredictable environments. While multispectral vision provides a resilient alternative to uni-modal sensors under adverse weather and lighting, existing architectures often struggle with cross-modal feature alignment and noise-induced spatial distortions. This paper proposes Multispectral Attention Context and Receptive-field Network (MACR-Net), an ultra-lightweight multimodal framework designed for high-precision drone detection. MACR-Net introduces a Global-Local Cross-Scale Interaction (GLCI) module to capture multi-scale semantic context and a Multimodal Spatial Cross-Perception (MSCP) mechanism to adaptively fuse RGB-IR streams while preserving target-specific thermal and structural signatures. Furthermore, we design an improved hybrid neck integrating Coordinate-Aware Attention (CAA) and Receptive Field Deformable (RFD) modules to anchor precise spatial coordinates and mitigate geometric distortions. Experimental results on the benchmark Multimodal Drone Detection Dataset demonstrate that MACR-Net outperforms state-of-the-art models, achieving a peak mAP_50 of 91.13% and a significant mAP_50-95 of 65.77%. Remarkably, the architecture maintains an extremely compact footprint with only 2.77M parameters and 0.77 GFLOPs, establishing an optimal balance between superior detection robustness and real-time feasibility for resource-constrained edge deployment.
Van-Phuc Hoang, Thien Huynh-The, Thanh-Dat Tran, Xuan Tinh Hoang
Fast Automatic Shoot Score Approach Combining Optical Flow And Mobilenetv2 Classification
This paper presents a fast automatic shooting scoring system designed for fixed-camera setup in real shooting range, where the paper target may undergo slight physical movement caused by wind, recoil-induced stand vibration. The proposed method combines an initial fixed homography with continuously updated dense optical flow for drift-free global alignment, residual optical flow for sub-pixel frame-to-frame registration, adaptive background modeling via motion-compensated subtraction for bullet hole candidate generation, and a highly efficient MobileNetV2 classifier for robust false-positive rejection. By maintaining precise registration to a static high-resolution Score Image template and leveraging the lightweight yet powerful quantized MobileNetV2 network, the system achieves over 98.5\% scoring accuracy at more than 10 fps entirely on CPU even when the target exhibits light movement and under changing natural lighting. Extensive experiments on indoor and outdoor live-fire datasets confirm robustness and real-time performance compared to traditional per-frame feature-matching methods and heavyweight deep learning approaches.
Performance and Memory Trade-offs in SM4 Implementation on Embedded ARM Cortex-M Microcontrollers
The SM4 block cipher has been widely adopted in security applications across embedded systems, particularly as part of China’s national cryptographic standards. However, its practical deployment on resource-constrained microcontrollers remains challenging due to limited processing power and memory. This paper investigates the trade-offs between performance and memory usage in various SM4 software implementations on ARM Cortex-M microcontrollers. We evaluate and compare three implementation strategies: (1) S-box lookup tables stored in Flash and SRAM, (2) T-table optimization that combines substitution and transformation operations, and (3) direct computation of the S-box using Galois Field (GF) logic. Each implementation is benchmarked on a 32-bit STM32 microcontroller to measure encryption latency, SRAM and Flash memory usage, and code complexity. The results reveal that the T-table implementation in SRAM provides the best performance with the lowest encryption latency, albeit at the cost of high SRAM consumption. Conversely, the GF logic implementation minimizes memory usage but suffers from the slowest execution time. This study provides important insights for selecting the most suitable SM4 implementation strategy for embedded systems with varying resource constraints and real-time requirements.
Hoang-Gia Vu, Khanh-Tuong Tran, Dinh-Tuan Nguyen, Tuan-Khang Nguyen, Khanh-Nghia Truong, Hoai-Luan Pham
Factors Limiting the Frequency of RTD Oscillators
The availability of compact, coherent, room-temperature sources with high output power is critical for numerous applications in the terahertz (THz) frequency range. Resonant-tunneling-diode (RTD) oscillators represent one of the most promising technologies for these requirements. This paper analyzes the factors limiting the maximum oscillation frequencies of RTD oscillators, specifically focusing on contact parasitics, spreading resistance, and antenna losses. By mitigating the effects of these parasitic elements, we optimize the antenna parameters—such as the slot antenna length and width—and the RTD mesa area to maximize oscillation frequencies for a given RTD wafer.
Low-complexity chroma down/up sampling conversion using decimation/interpolation FIR filter and FPGA IP-core design
This paper proposes a low-complexity solution for chroma downsampling and upsampling, applicable to video compression. The proposed method uses decimation and interpolation FIR filters, which are known to be effective in signal processing. This approach enhances the quality of chroma recovery and minimizes computational complexity, making it suitable for hardware platforms with limited resources. An experimental design was implemented on an FPGA using a parameterized IP core that supports YCbCr and YUV color spaces, enabling flexible conversion between 4:4:4, 4:2:2, and 4:2:0 formats. This implementation contributes to improved performance, enhanced flexibility, and reduced computational complexity—an important requirement for modern video applications. The IP core was successfully verified using the Xilinx Vitis Model Composer tool, with a focus on visually inspecting the output and comparing it against reference models developed in the MATLAB/Simulink environment.
Nghia Van Tran, Hieu Trung Dang, Dung Viet Pham
CoDeX-Net: A Coordinate-Gated and Deformable Cross-Scale Network for Efficient Waveform Classification in Integrated Radar-Communication Systems
Accurate waveform classification in hybrid radar-communication environments remains challenging, particularly under low-SNR conditions where structured interference and noise severely distort time-frequency signatures. Existing lightweight CNN models often lack the capacity to capture axis-dependent patterns, whereas conventional vision backbones are computationally prohibitive for edge-level deployment and fail to exploit the physical structure of spectrogram data. To address these limitations, this work proposes CoDeX-Net, a compact dual-stream architecture composed of two complementary modules: (i) PAUG, which performs intra-resolution refinement through coordinate-aware spatial gating and channel-adaptive modulation, and (ii) CSDM, a cross-scale deformable mixer that aggregates coarse-resolution context via offset-guided sampling and soft branch routing. Together, these modules enhance both local discriminability and long-range spectral coherence while maintaining extremely low complexity. Extensive experiments on twelve radar and communication waveforms demonstrate that CoDeX-Net achieves 91.01% average accuracy, outperforming state-of-the-art CNN and lightweight radio frequency (RF) classifiers despite operating with only 51K parameters and 0.564 ms inference latency. The results confirm that task-aligned architectural design provides substantive benefits over repurposed vision models and enables practical deployment in real-time embedded RF systems.
Thien Huynh-The, Minh-Thanh Le, Son Ngoc Truong, Ngoc-Ha Truong, Tan Do-Duy, Hoc Phan, Son Ngoc Pham, Thanh-Dat Tran
Joint Caching and Hovering Minimized Service Time for Cooperative Video Streaming in UAV-assisted VANETs
The rapid growth of advanced applications and services (A\&Ss), particularly video streaming, imposes stringent latency and quality-of-experience requirements on vehicular ad hoc networks (VANETs). Although edge caching at roadside units (RSUs) can reduce service time, its effectiveness is limited in highly dynamic environments characterized by uneven vehicle user (VU) distributions, constrained caching resources, and sparse roadside infrastructure. To address these challenges, this paper proposes a caching and hovering (CAHO) optimization design for cooperative video streaming in VANETs assisted by unmanned aerial vehicles (UAVs). In the proposed CAHO design, VU distributions are modeled as independently thinned one-dimensional homogeneous Poisson point processes, and RSU-to-VU and UAV-to-VU communication models together with video popularity characteristics are incorporated. Based on this modeling, the CAHO problem jointly optimizes video caching at RSUs and UAVs as well as UAV hovering positions to minimize the service time under limited storage resources. The CAHO optimization problem is efficiently solved using genetic algorithms combined with a penalty function and a divide-and-conquer strategy. Simulation results demonstrate that the proposed CAHO scheme outperforms benchmark schemes under various system scenarios in terms of service time and storage resource utilization, highlighting the effectiveness and practical potential for video streaming A\&Ss in UAV-assisted VANETs.
Quynh-Anh Nguyen, Nguyen-Son Vo, Van-Ca Phan, Dac-Binh Ha, Minh-Phung Bui, Dinh-Long Nguyen
A Design of Variable-Cutoff-Frequency CMOS High-Pass Filter using Low-Bias Current Control
This paper presents a variable-cutoff-frequency CMOS high-pass filter, which is designed for low-power analog front-end applications. The filter employs a weak-inversion MOSFET network to synthesize a GΩ-range equivalent resistance, enabling low-frequency operation without relying on large on-chip resistors. The cutoff frequency is adjusted by a low-bias current control scheme consisting of four selectable 300 pA branches, which tune the effective equivalent resistance while maintaining minimal power consumption. A comparator utilizes a folded cascode stage for common-mode stabilization and accurate bias control. Implemented in a 180 nm CMOS technology, the proposed prototype occupies 0.0045 mm2, consumes 0.749 µW, and provides five programmable high-pass corner frequencies ranging from 50 Hz to 100 kHz. The aforementioned features make the proposed architecture suitable for compact, energy-efficient front-end systems requiring reliable and tunable high-pass filtering.
ProtoKNN: A Hybrid ProtoNets-KNN for Few-Shot Cyberattack Detection
Modern network security systems have faced significant challenges from novel attacks with extreme data scarcity, known as few-shot learning problem (FSL). Meta-learning, particularly Prototypical Networks (ProtoNets), has emerged as a promising solution to this problem. However, ProtoNets rely on Euclidean distance to a single prototype, assuming isotropic and spherical class distributions. We argue that network traffic is too diverse for simple clusters; its complex feature distributions cause ``centroid misalignment'', where a single center cannot accurately represent the attack. To address this, we propose a Hybrid ProtoKNN method. By integrating a local KNN metric with the global prototypical objective, we relax the spherical constraint and effectively recover misaligned outliers. We evaluate our approach on the NSL-KDD and CIC-IDS2017 datasets. Experimental results in various few-shot scenarios demonstrate that our model significantly improves detection performance on rare and complex attack categories, such as U2R, R2L, and Heartbleed, compared to standard meta-learning methods.
Le Hoan Hoang, Manh Tuan Nguyen, Van Loi Cao
Partial Distance Correlation-Based Motion Pattern Detection in Pangasius Fish
In computer vision, behavioral recognition of aquatic organisms plays an important role, particularly for Pangasius catfish, a fish species commonly cultured in Vietnam. This study presents a method for detecting Pangasius motion patterns comprising 38 catfish videos with a total of 236,133 extracted frames, from which 4,593 motion windows are extracted and classified into six behavioral categories (Cruising, Burst–Coast, Escape, Schooling, Milling, and Swarming) based on Partial Distance Correlation (PDC) integrated with video processing techniques and feature extraction methods. Experimental results show that Distance Correlation (dCor) on raw data yields high correlation values (0.826–0.989) but with substantial scatter. PDC with heading angle control maintains elevated values (0.804–0.979) with tighter residual clustering. When denoising is combined with heading angle control, pdCor achieves optimal efficacy (0.852–0.973). Compared with dCor, pdCor provides consistent improvements, especially for complex behaviors (Escape: 5.1%; Swarm: 3.2%). The combined strategy detects 40% of patterns better and 60% similarly, indicating pdCor does not reduce performance for simple behaviors but substantially improves detection for nonlinear, high noise patterns.
Kieu Thuy Thi Phan, Thang Cong Pham, Hiep Xuan Huynh
UIT-MLReceipts: A Multilingual Benchmark for Detecting and Recognizing Key Information in Receipts
The 4.0 industrial evolution has paved the way for development potential and revolution in Vietnam. In this movement, digitization appears to be necessary to transform numerous traditional economic sectors. It will provide valuable digital data for many automation applications and decision-making processes. Particularly in the retail industry, data has long played a vital factor. Hence, digitizing documents such as receipts can help businesses in management and enterprise development. Nevertheless, the digital transformation process is still slow because of the shortage of cleaned datasets for this type of document. This paper introduces a new dataset named UIT-MLReceipts for extracting key information in receipts. The task includes two sub-tasks: Receipt Text Detection (RTD) and Receipt Text Recognition (RTR). We thoroughly evaluate current state-of-the-art Receipt Text Detection using Faster R-CNN, YOLOv3, YOLOF, and Faster R-CNN with Precise RoI-Pooling on our dataset. To evaluate the performance of Receipt Text Recognition, we experiment with two text recognition baselines: RobustScanner and SATRN. Experimental results indicate that the Faster R-CNN with Precise RoI-Pooling outperforms the competitors and achieves the best mean Average Precision (mAP) score at 51.6% in the Receipt Text Detection task. With the Receipt Text Recognition task, results show that SATRN performs better.
Design of a Configurable 4-Channel Analog Front-End for EEG Signal Acquisition on 180nm CMOS Process
In this work, a 4-channel Analog Front-End (AFE) circuit has been proposed for EEG signal recording. For EEG recording systems, the AFE may handle a wide range of sensor inputs with high input impedance, adjustable gain, low noise, and wide bandwidth. The buffer or current-to-voltage converter block (BCV), which can be set to operate as a buffer or a current-to-voltage converter circuit, is positioned between the electrode and the main amplifier stages of the AFE to achieve high input impedance and work with sensor signal types. A chopper capacitively-coupled instrumentation amplifier (CCIA) is positioned after the BCV as the main amplifier stage of the AFE to reduce input-referred noise and balance the impedance of the overall AFE system. A programmable gain amplifier (PGA) is the third stage of the AFE that allows the overall gain of the AFE to be adjusted. The suggested AFE operates in a wide frequency range of 0.5 Hz to 2 kHz with a high input impedance bigger than 2TΩ, and it is constructed and simulated using a 180nm CMOS process. With the lowest 100-dB CMRR and low input-referred noise of 1.8 µVrms, the AFE can achieve low noise efficiency. EEG signals can be acquired with this AFE system, which is very useful for detecting epilepsy and seizures.
Duc Hung Le, The-Hung Pham, Cong-Kha Pham
Developing Uplink Power Optimization and ARS Selection Algorithm for Multi-ARS Small Cell Communication System
Small Cell (SC) models and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are both promising advancements in the development of upcoming wireless networks that contribute significantly to improving the overall service quality. In this work, we rely on the Multi-ARS Cell-Free (CF) model, where a large number of ARS coordinated by the ground base station (GBS) and cooperate to serve a large number of users within the same frequency and time resources, to develop the uplink of a multi-ARS SC system, in which each user is served by only one ARS. The time division duplex (TDD) mechanism is used for communication protocol, and the Minimum Mean Square Error (MMSE) method is implemented to estimate the uplink channel. We derive an closed-form expression for uplink user throughput. In addition, we introduce the ARS selection method based on channel conditions and propose the Bisection algorithm to optimize uplink power. The system performance is evaluated by the cumulative distribution function (CDF) of user throughput according to different parameters, such as changing the number of ARS, the number of users, the number of antennas, and the length of pilot sequences with/without power optimization. The results reveal that the ARS selection method is effectively resolved to reduce complexity and improve the practicality of the proposed system, and the power optimization problem for better throughput is non-optimization.
Hoai Thi Bich Be, Duc Anh Bui, Hiep Thanh Pham, Phuong Thu Nguyen
An Ultra-high Quality Factor Terahertz Photonic Crystal Cavity
High quality factor Terahertz (THz) cavities are highly desired for many THz applications. This paper presents an ultra-high quality factor terahertz planar photonic crystal cavity at 300 GHz range. Two approaches are employed to reduce the losses in the cavity increasing the quality factor of the cavity. Firstly, short embedded photonic crystal waveguides are employed to reduce the in – plane loss. Secondly, a novel way of hole displacement is adopted for four edged holes of the L3 – type photonic crystal cavity to decrease the radiation loss. An ultra – high quality factor of 65000 at a resonant frequency of 315.3 GHz was achieved for the designed cavity. This result could enable promising applications such as THz sensing.
Mai Van Ta, Anh Thi Nguyen, Huong Thi Thu Tran, Hung Tuan Nguyen, Linh Thuy Nguyen, Hoang Huy Nguyen, Manh Duy Luong
Deep Learning Based Cooperative MIMO Systems for Wireless Body Area Networks
Wireless Body Area Network (WBAN) is widely applied in various fields, including healthcare, sports, wellness, and assistive technologies, by offering the benefits of convenience, reliability, low latency, privacy, and customization. However, the propagation characteristics of the WBAN channel can impact the reliability of transmission, which is particularly crucial in healthcare systems. To address this issue, this article presents a novel approach using deep learning-based cooperative Multiple-Input Multiple-Output (MIMO) systems that leverage the autoencoder (AE) technique. In our proposed approach, we utilize the AE-based cooperative MIMO systems with two different techniques: Amplify-and-Forward (AE-AF) and Decode-and-Forward (AE-DF). The AE-AF scheme operates without needing training parameters at the relay node, whereas the AE-DF scheme necessitates training parameters at the relay node. Both schemes aim to overcome challenges such as multipath propagation phenomena, thereby enhancing the performance of on-body communication systems. Additionally, we introduce two combinators, Minimum Mean Square Error (MMSE) scheme and Radio Transformation Network (RTN), to effectively mitigate co-channel interference (CCI) in the received signal streams and improve the bit error rate performance of the AE-AF and AE-DF systems. We assess the performance of these systems in scenarios with and without direct links. Simulation results demonstrate significant performance improvements compared to baseline cooperative MIMO systems using MMSE combining, namely AF-MMSE and DF-MMSE systems. Notably, the proposed systems employing RTN combination, including both direct and relay paths, achieve a 7.5 dB gain over the baseline when all nodes are equipped with two transceiver antennas.
Tam Thi Thanh Bui, Nam Xuan Tran, Anh Huy Phan
A Design of Similar High-gain and Dual-band Frequency/Polarization Reconfigurable Antenna for ISM Band Applications
This paper proposes a frequency/polarization reconfigurable antenna (RA) incorporating Frequency Selective Surface (FSS) to achieve dual-band and similar high-gain characteristics. The proposed RA-FSS design using 4 PIN Diodes can produce reconfigurability between circular polarization (CP) at 1.8 GHz and linear polarization (LP) at 2.45 GHz. The fabricated prototype shows good CP performance at 1.8 GHz while the measured peak broadside gains are about 7.2 dBi at 1.8 GHz and 8.5 dBi at 2.45 GHz when PIN diode ON and OFF, respectively.
Cong Danh Bui, Minh Thuan Doan, Thi Thanh Kieu Nguyen, Truong Khang Nguyen
Deep Reinforcement Learning-based Bitrate Adaptations in Dynamic Adaptive Streaming over HTTP
Dynamic adaptive streaming over HTTP (DASH) has been a superior video streaming technology in recent years. Bitrate adaptation function at video player plays a vital role in guaranteeing a high quality-of-experience for the users. This work evaluates the performance of several advanced deep reinforcement learning algorithms, i.e., deep Q-learning, actor-critic, and proximal policy optimization, applied in bitrate adaptations and compares them with other rate adaptation methods with real-trace datasets.
Long Minh Luu, Nghia Trung Nguyen, Phuong Luu Vo, Tuan-Anh Le
Security for Multi-hop Communication of Two-tier Wireless Networks with Different Trust Degrees
Many effective strategies for enhancing network performance have been put forth for wireless communications’ physical-layer security. Up until now, wireless communications security and privacy have been optimized based on a set assumption on the reliability or network tiers of certain wireless nodes. Eavesdroppers, unreliable relays, and trustworthy cooperative nodes are just a few examples of the various sorts of nodes that are frequently categorized. When working or sharing information for one another, wireless nodes in various networks may not always have perfect trust in one another. Modern wireless networks’ security and privacy may be enhanced in large part by optimizing the network based on trust levels. To determine the path with the shortest total transmission time between the source and the destination while still ensuring that the private messages are not routed through the untrusted network tier, we put forth a novel approach. To examine the effects of the transmit SNR, node density, and the percentage of the illegitimate nodes on various network performance components, simulation results are provided.
Chan Thai, Vo Nguyen Quoc Bao, Uyen Han Thuy Thai
A 4-Term Exponential-Quadratic Approximation for Gaussian Qor Error Functions Accurate to1.65×10−4
Integrals on [0, ∞) where the integrand is of the form Qn(a√x) p(x), where Q is the Gaussian Q function, p(·) a Gamma PDF, n a positive integer and a > 0; or of the form erfn(ax + b) xr exp(-c2x2 + 2dx), where erf(x) is the error function, with integers r ≥ 0, n > 0, arise in performance modelling of communication and machine learning systems. Such integrals cannot be evaluated analytically in general, but they are reducible to a set of key integrals whose integrand is erfn(ax + b) N(x; m, s) where N() is a Gaussian PDF with mean m and variance s. Seeking an efficient and accurate evaluation method, we develop a new 4-term exponential quadratic approximator (EQA) for the error function that includes both linear and quadratic terms in its exponents. The EQA minimises a sum-of-squares cost function with two “spline-type” constraints, i.e., constraints on the function value and its first derivative. This constrained optimisation problem is reduced to an unconstrained one by inverting a 4-D linear system, then solved by gradient descent. The resulting approximator has a maximum absolute error of 1.65 × 10-4 on the real line, and outperforms many other exponential sum approximators for erf(x) on x ∈ [0, 1.5] and for Q(x) on x ∈ [0, 2]. Moreover, due to its functional form, the EQA leads to an analytical formula for the set of key integrals, which, in the n = 1 case, is accurate to 3 to 4 significant figures while being orders of magnitude more efficient than Monte Carlo integration. The EQA can equally be used to obtain closed forms for the average symbol error probability of various modulation schemes on Rayleigh fading channels
Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images
Liver vessel segmentation in contrast-enhanced CT (CECT) images has a significant role in the planning stage for liver cancer treatment, such as radiofrequency ablation (RFA). Lowering the radiation dose in CECT imaging to reduce radiation risk to the patient degrades the quality of the image and potentially affects the liver vessel segmentation. In recent years, the convolutional neural network (CNN) has shown significant achievement in medical image analysis, including segmentation and denoising tasks. This paper presents a study on a new framework consisting of three well-known denoising techniques, including vessel enhancing diffusion (VED), RED-CNN, and MAP-NN, along with the state-of-theart segmentation method (nnU-Net) to segment the liver vessels in CECT images. We quantitatively evaluate the impacts of denoising techniques on the vessel segmentation on multi-level simulated low-dose CECT images of the liver. The performances of the liver vessel segmentation method combined with the denoising techniques are evaluated using Dice score, sensitivity metric, and processing time. In addition, the effect of denoising on the surface roughness of the segmented liver vessel is also investigated. The experiments show that the image denoising techniques improve the quality of liver vessel segmentation on high noisy CECT images while also reducing the segmentation accuracy on low-noise-level CECT images.
Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System
Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSOd. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSOd for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSOd and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealing in terms of the service disruption and computation time.
PGN-LM Model and Forcing-Seq2Seq Model: Multiple Automatic Models of Title Generation for Natural Text using Deep Learning
In the current era, the amount of information from the Internet in general and the electronic press in particular has increased rapidly and has extremely useful information value in all aspects of life, many popular users have posted several high-quality writings as casual blogs, notes or reviews. Some of them are even selected by editors to be published in professional venues. However, the original posts often come without titles, which are needed to be manually added by the editing teams. This task would be done automatically, with the recent advancement of AI techniques, especially deep learning. Even though auto-title can be considered as a specific case of text summarization, this job poses some major different requirements. Basically, a title is generally short but it needs to capture major content while still maintaining the writing style of the original document. To fulfill those constraints, we introduce PGN-LM Model, an architecture evolved from the Pointer Generator Network, with the ability to solve Out-of-Vocabulary problems that traditional Seq2Seq models cannot handle, and at the same time combined with language modeling techniques. In addition, we also introduce a model called Forcing-Seq2Seq Model, an enhanced Seq2Seq architecture, in which the classical TF-IDF scores are incorporated with Named Entity Recognition method to identify the major keywords of the original texts. To enforce the appearance of those keywords in the generated titles, the specific Teacher Forcing mechanism combined with the language model technique are employed. We have tested our approaches with real datasets and obtained promising initial results, on both metrics of machine and human perspectives.
To Thanh Nhan, Nguyen Thi Hiep Thuan, Quan Thanh Tho
Real-Time Face Detection and Human Tracking System on FPGA Cyclone-V
Face detection in image sequence (real-time video stream) has been an active research area in the computer vision field in recent years due to its potential applications such as surveillance cameras, human computer interfaces, smart rooms, intelligent robots and biomedical image analysis. Face detection is a process that determines whether an image has a face or not. In this paper, an embedded system for detecting and tracking human faces in real-time video stream implemented on FPGA DE10-Nano is proposed. The system can be divided into two parts: data streaming, data processing. Experimental results show that the system is capable of accurately detecting faces of up to 5 different people at a distance of up to 1.5 meters from the camera, coexisting in the same frame in resolution of 320 × 240 pixels with a detection speed of only several hundred milliseconds prove the feasibility of the system. A comparison with similar existing projects will be discussed for evaluation and conclusion as well.
Huu Luong Nguyen, Minh Son Nguyen, Tri Nhut Do
Brainstorm website layout ideas usually start with everyone giving out their mockups, and all the team members will discuss to finalize the layout of the user interface. Once a vision of that mockup is accepted, it is given to the designer to sketch it digitally on computer software (i.e., Photoshop, Figma, Sketch). When the designer completes, the developer based on the final design to code the UI/UX of the website. As we can see, the process requires three stages, which can be time-consuming. Therefore, if anyone has an idea for the professional website layout, they can visualize it by drawing on sketches. However, it can be impossible for them to make a usable website without designers and website developers. Due to that reason, our primary goal in this paper is to help individuals transform their hand-drawn sketch images into a website that can be deployed. To achieve that goal, we present two approaches: classical computer vision techniques and the other using a deep learning model to detect the sketch and execute the conversion. Furthermore, our evaluation shows that deep learning is the most promising direction. Still, classical techniques also improve the model’s input data by applying it in the pre-processing image.
Lai Nguyen Ha My, Huỳnh Ngọc Thiên, Nguyễn Đức Dung
Combining Content and Social Features in a Deep Learning Approach to Vietnamese Email Prioritization
The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.
Ha Thanh Nguyen, Quan Dinh Dang, Anh Quang Tran
An FPGA-based Convolution IP Core for Deep Neural Networks Acceleration
The development of machine learning has made a revolution in various applications such as object detection, image/video recognition, and semantic segmentation. Neural networks, a class of machine learning, play a crucial role in this process because of their remarkable improvement over traditional algorithms. However, neural networks are now going deeper and cost a significant amount of computation operations. Therefore they usually work ineffectively in edge devices that have limited resources and low performance. In this paper, we research a solution to accelerate the neural network inference phase using FPGA-based platforms. We analyze neural network models, their mathematical operations, and the inference phase in various platforms. We also profile the characteristics that affect the performance of neural network inference. Based on the analysis, we propose an architecture to accelerate the convolution operation used in most neural networks and takes up most of the computations in networks in terms of parallelism, data reuse, and memory management. We conduct different experiments to validate the FPGA-based convolution core architecture as well as to compare performance. Experimental results show that the core is platform-independent. The core outperforms a quad-core ARM processor functioning at 1.2 GHz and a 6-core Intel CPU with speed-ups of up to 15.69× and 2.78×, respectively
A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction
Lithium-ion batteries (LIBs) have recently been used widely in moving devices. Understand status of the batteries can help to predict the failure and improve the effectiveness of using them. There are some lithium-ion information that define the battery health over time. These are state-of-charge (SOC), state-of-health (SOH), and remaining-useful-life (RUL). Normally, a LIB is working under charging and discharging cycles continuously. In this paper, we will focus on the data dependency of different time-slots in a cycle and in a sequence of cycles to retrieve RUL. We leverage multi-channel inputs such as temperature, voltage, current and the nature of peaks cross the cycles to improve our prediction. Comparing to existing methods, the experiments show that we can improve from 0.040 to 0.033 (reduce 17.5%) in RMSE loss, which is significant.
Thien Pham, Loi Truong, Anh Bui, Dang Minh Nguyen, Dat Nguyen, Akhil Garg, Liang Gao, Quan Thanh Tho
Enhancing Security and Robustness for SDN-Enabled Cloud Networks
Software-Defined Networking is an emerging network architecture which promises to solve the limitations associated with current cloud computing systems based on traditional network. The main idea behind SDN is to separate control plane from networking devices, thereby providing a centralized control layer integrable to cloud-based infrastructure. The integration of SDN and Cloud Computing brings an immense benefits to network deployment and management, however, this model still faces many critical challenges with regards to availability, scalability and security. In this study, we present a security and robustness SDN-Enabled Cloud model using OpenStack and OpenDaylight. In particular, we design and implement a security clustering-based SDN Controller for monitoring and managing cloud networking, and a hardware platform to accelerate packet processing in virtual switches. We evaluate our proposed model on a practical cloud testbed consisting of several physical and virtual nodes. The experiment results show that the SDN controller cluster significantly improve robustness for the network even in case of being attacked by abnormal network traffic; while the hardware-accelerated switches can be operated in highperformance and well-adapted to the cloud environment.