Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of ANN, LSTM, and Various Supervised Machine Learning Algorithms on Human Activity Recognition

Plenty of healthcare applications (fitness tracking, sleep pattern monitoring, amount of calories burned, etc.), remote monitoring applications (patient, child or older people), security, and soft biometrics applications require automatic and precise activity recognition. So, this paper demonstrates the best approach among various approaches used for activity classification using the tri-axial gyroscope and accelerometer sensors, which are already integrated into smartphones. Some unique and distinguishable features are extracted using the acquired signals from these sensors. For activity recognition purpose, we have evaluated the performance of the Artificial Neural Network (ANN), Long short term memory (LSTM), and various supervised machine learning algorithms on a human activity recognition data set, UCI HAR dataset (obtained from the inertial sensors of a smartphone). Comparative results obtained on UCI HAR dataset shows that the maximum accuracy achieved so far is 98.57%. Therefore, we have analyzed the effectiveness of various methods using our feature extraction process and observed that 99.9% of accuracy can be achieved with the Ensemble classification method with bagging tree.
Machine Learning Based Approach for Predicting the Impact of Time of Day on Traffic Accidents

In India, there is a dearth of information about machine learning (ML) based crash prediction that uses crash data from police crash databases. This study introduces a novel approach utilizing artificial neural network (ANN) to develop models that predict the impact of time of day on road traffic accidents (RTAs) based on Kokrajhar police data. By resourcing a comprehensive crash database spanning from 2020 to 2022, this study encompasses multiple ANN models via different combinations of activation functions and optimizers ensuring enhanced prediction accuracy. The best fit prediction model is found to be produced by the ANN model using the adam optimizer and softmax activation function. Additionally, it was found that the variable "p.m. peak" is the most sensitive factor impacting the crash outcome (fatal/non fatal). Finally, the Apriori algorithm was used to identify the factors that have the greatest impact on each classification of the time of day for fatal traffic accidents. Determining these causal risk variables will facilitate the development of focused mitigation strategies to improve road safety standards.
Image Compression Technique Using Contour Coding and Wavelet Transform in Digital Image Processing

Photo compression is a vital thing in virtual photograph processing. Contour coding and wavelet transforms are exceptional techniques that may be used for image compression. Contour coding is a space-filling curve technique that encodes the picture by tracing the contours of areas inside the photograph. It makes use of a smooth space-filling curve that visits areas in a more efficient way than raster scanning. The method takes into account the similarities between nearby pixels and reduces the file length with the aid of using predictive coding. Wavelet remodel is a mathematical tool that decomposes photos into unique frequency components. The wavelet rework is based on analyzing the variations between pixels by taking the differences in their spatial correlations. This technique is superb because it can represent a photograph using fewer coefficients than other compression techniques, resulting in smaller record sizes. Universally, these two photograph compression techniques are powerful for digital image processing. They could enable faster transmission of pix and decrease storage.
Enhancing Online Intrusion Detection Systems via Attack Clustering

Improving Intrusion detection systems (IDS) is pivotal for securing networks from various elusive attacks, including DDoS, packet injection, and unauthorized access. While many IDS systems are successful in fending off attacks, they often suffer from either low accuracy or significant latency. This paper proposes an IDS framework that balances higher accuracy with computational complexity, focusing on decreasing dimensionality by utilizing clustering methods, along with Ant Colony Optimization, and Rough k-means algorithms. Additionally, the paper enhances existing feature selection algorithms through multi-objective optimization, eliminating superfluous features that do not contribute significantly to intrusion detection and reducing problem dimensions, which increases speed. The combination of rough clustering and optimization techniques leads to a desirable accuracy of 87% in low dimensions. The proposed framework is evaluated using two benchmark datasets, NSL-KDD and ISCX 2012, and compared to two leading approaches, namely ICA-BP and GA-BP, where our model surpassed their accuracy levels.
Analyzing the Effect of Edge Computing on Real-time Data Processing and Latency Reduction

Edge computing platforms offer multiple advantages to information technology, like lower latency, increased connectivity, longer battery duration, and so on. Despite their many advantages, edge computing platforms have some flaws that could severely impair the functioning of specific programs. Indeed, present edge computing platforms do not guarantee latency reduction for real-time data processing and suffer from data overload concerns. To address the above difficulties, this research offers Household Edge Computation (HEC), a novel 3-level edge computation design that delivers real-time data storing and processing near consumers. This 3-level design includes a Residential, a Central, Edge Server, all of which are common in typical edge computation designs. The Residence Server is positioned close to the consumers, allowing for latency reduction for real-time data processing that can be handled by the servers; this also helps minimize the quantity of data that may be handled in the Edge Server and the centralized Cloud. This research uses the Edge-Cloud-Sim simulated system to show the viability of the proposed design. The research findings suggest that this solution may assist in attaining extremely low latency and eliminate overloading difficulties.
Effects of 2D and 3D Max Pooling on LSTMFeature Extraction

LSTM is a valuable approach in the context of land cover change detection due to its ability to capture temporal dependencies and long-term patterns in sequential data. Land cover change detection involves analyzing satellite images or time series data to identify and classify changes in land surface features over time. By time step analysis of images, LSTM can capture even small gradual changes in the area. The recurrent nature of LSTM allows it to understand the relationship between neighboring pixels which could also help to fix missing or noise pixel data if any. Lastly, its temporal feature extraction capability helps it to better distinguish between classes. The success of any machine learning model depends upon feature extraction and its ability to generalize the data well. This research focuses on the MaxPooling operation used in the convolution layer of a convolutional neural network. The various advantages of MaxPooling are extensively studied by implementing and comparing the performance of 2D MaxPooling and 3D MaxPooling in an LSTM network. As a result, this can be concluded that MaxPooling not only helps in dimensionality reduction and size invariance issues but also helps in better local feature extraction, and translational invariance, and is robust to noise variations.
Real-Time Pig Vocal Analysis for Early Respiratory Infection Study

Pig farming has steadily advanced to rank among the top agricultural efforts. Their health and nutrition determine financial success in pig farming for weaning and pork production. Respiratory diseases are a significant cause of economic losses and animal welfare concerns in the pig industry. The ability to quickly intervene, improve treatment outcomes, lessen disease effects, and support long-term pig production depends on the early detection and diagnosis of respiratory infections in pigs of industrial farms. Vocalizations made by sick pigs may be an indication of respiratory illness. The proposed work uses the best performing MFE features trained model deployed to Arduino Nano, for real-time classification of pig vocals. The vocals are preprocessed using three signal processing algorithms: MFE, MFCC, and spectrogram. This research uses an Arduino Nano 33 BLE sensing development board and a mobile phone. The Edge Impulse studio receives the pig’s cough samples from Arduino, and the Mel cepstral features are extracted from the pig's sound signals and fed to neural network classifiers for training the CNN model. The model that used the MFE features extracted from pig vocals generated the best results, with a maximum accuracy of 99%.
3D spatial condition sharing system based on sensor fusion of LiDAR and depth camera

With the recent spread of COVID-19, online communication methods such as web conferencing are attracting attention. However, the existing methods have a problem that it is difficult to get the same realistic experience as in real world. Existing studies propose online communication methods using avatars in a virtual reality space, but cannot completely solve the problem because the avatar’s appearance is not the same as in the real world and it is difficult for the avatars to reflect the user’s real-world behavior. Especially, when an elderly people living alone communicates with family members living far away, it is ideal to generate a three-dimensional virtual communication space that is close to the real world and to provide an experience as if the users are living in the same space. In order to observe the three-dimensional shape of the structures in the real world, a 3D LiDAR (Light Detection and Ranging) and a depth camera are often utilized. However, these devices have problems that the LiDAR can observe a wide area of the real world but cannot acquire color information, and that the depth camera can acquire both depth and color information but can only accurately observe the limited area. This study proposes a combination of the advantages of the 3D LiDAR and the depth camera to be able to generate a realistic communication space. In the proposed system, the 3D LiDAR is utilized to observe the shape of the structures in the room and both location and behavior of the people living in the room as point cloud data. In addition, the depth camera obtains accurate shape and color of the 3D structures in the room, and the color information is reflected to the point cloud data by applying the machine learning technologies that extract only targeted people. Therefore, our study can solve the existing problem by participating the constructed 3D space through a HMD (Head Mounted Display), the family members living far away from each other can get the realistic experience as if they were living together.
A Target Tracking Algorithm Based on Channel Attention and Spatial Attention

Due to issues such as target occlusion and complex background interference, the accuracy and stability of target tracking still need to be improved. To address these issues, this paper proposes a target tracking algorithm based on channel attention and spatial attention. This algorithm applies channel attention and spatial attention mechanisms to the field of target tracking. Among them, the channel attention mechanism enhances the ability to extract target features by focusing on important channels in the input feature map; The spatial attention mechanism achieves accurate estimation of target position by focusing on important areas in the feature map, enhances the expression ability of target features by utilizing channel attention mechanism, and suppresses interference from background features by utilizing spatial attention mechanism. Through experimental verification on the got-10k dataset, the algorithm proposed in this paper has higher tracking accuracy and stability in complex scenarios.
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation

We analyze and improve a recent strategy to train a quantile regression model by minimizing an expected pinball loss over all quantiles. Through an asymptotic convergence analysis, we show that minimizing the expected pinball loss can be more eﬃcient at estimating single quantiles than training with the standard pinball loss for that quantile, an insight that generalizes the known deﬁciencies of the sample quantile in the unconditioned setting. Then, to guarantee a legitimate inverse CDF, we propose using ﬂexible deep lattice networks with a monotonicity constraint on the quantile input to guarantee non-crossing quantiles, and show lattice models can be regularized to the same location-scale family. Our analysis and experiments on simulated and real datasets show that the proposed method produces state-of-the-art legitimate inverse CDF estimates that are likely to be as good or better for speciﬁc target quantiles.
Joint Sensing and Computation Offloading for Wireless Powered Mobile Edge Computing System

Mobile-edge computing (MEC) and wireless power transfer (WPT) are promising paradigms in the Internet of Things (IoT) era to alleviate limitations of energy and computation capacity of wireless devices (WDs). In this paper, we consider a wireless-powered MEC in which one hybrid access point (HAP) with a stable energy supply and computation capacity coordinates with a set of WDs with WPT and sensing capabilities. In the considered model, WDs first harvest RF energy transmitted by HAP and then sense data with stored energy for multiple sensing tasks. With MEC, WDs can execute tasks locally or offload data to HAP. To minimize the average latency, we formulate a joint sensing and processing optimization problem in a wireless powered-MEC system under energy and successful sensing probability constraints. This NP-hard problem can be decoupled into three subproblems. Then, we adopt an alternating algorithm to iteratively solve the WPT time, sensing time, and offloading policy subproblems. Simulation results demonstrate the effectiveness of our proposed joint optimization algorithm.
Commissioning Federated Reinforcement Learning to Envision Network Security Strategies

Federated Learning (FL) has been used in various cyber security tasks to train ML models in a decentralized and privacy-preserving manner. This has been coupled with the application of reinforcement learning in various simulated environments for effective training in defense and intrusion response scenarios for computer networks. In this work, we presented a FL - based method in which reinforcement learning agents are trained in a customized environment inspired by real-world schemes, to defend computer networks in a privacy-preserving manner. The results show that federated reinforcement learning based agents outperform the reinforcement learning agent.
A 3D Multi-channel Amplifier Module with Fuzz Button in LTCC

3-dimensional vertical interconnection structure has become the key technology of microwave integrated package which is significant for miniaturization. Fuzz button is an elastic electrical connector which can achieve solderless vertical interconnection. In this paper, a 3D multi-channel amplifier module with fuzz button based on LTCC technology is proposed. What’s more, Wilkinson power divider is designed, and two cascaded power dividers are placed on different layers to realize miniaturization. Also, fuzz buttons and vertical transition structure from strip lines to fuzz button are designed, and fuzz buttons are used as input and output port. The test results show that in 7.8~8.2GHz, the $S_{21}$ is greater than 29 dB, the standing wave of the input port is less than 1.55 and of the output is less than 1.3.
P4-based In-Network RL inference for Efficient Flow-Level Bandwidth Allocation

Software-defined networking (SDN) has notably improved networks by providing Machine Learning-Powered programming capabilities at the control plane (CP), making it easier to dynamically manage the network resources according to varying traffic conditions. However, the geographically remote location of the CP from the data plane (DP) leads to significant round-trip delays in the order of milliseconds, which can ad-versely impact the performance of delay-sensitive and real-time traffic. To address this issue, this paper proposes a novel in-network reinforcement learning (RL) inference framework that extends programming capability from the CP to the DP for fine-grained control of network resources to meet the Quality of Service (QoS) demands of real-time applications. The in-network RL inference is achieved by adopting a match-action table mapping strategy in the DP and validating it through programming protocol-independent packet processors (P4). A P4 meter extern is utilized to allocate bandwidth to individual traffic flows based on their QoS requirements. Our proposed strategy achieves in-network RL inference at the line rate with negligible processing overhead while reducing packet loss rate and jitter by up to 92% and 57%, respectively, compared to the CP-based approach. Additionally, we evaluate the performance of our proposed bandwidth allocation framework using a state-of-the-art deep-deterministic policy gradient (DDPG)-based RL agent with a heuristic priority experience replay (hPER) technique. Our proposed DDPG agent achieves a faster convergence rate, higher reward, superior training stability, and up to 56 % reduction in operational cost compared to two alternative agents,
Intelligent Urban Expressway Managing Architecture Using LoRaWAN and Edge Computing

With the rapid growth of smart cities, Intelligent Transportation Systems (ITS) are playing an increasingly important role. However, with the rapid expansion of city boundaries, ITS faces scalability and energy consumption challenges. The wide range and the massive number of nodes have become significant issues for network technology in ITS. This study proposes an urban expressway managing architecture using LoRaWAN and edge computing. The traffic network is divided into different sections. A LoRa WAN network is established for monitoring and controlling each section by exploiting its low-power, long-range characteristics. Moreover, an edge computing-based traffic state encoder model has been proposed to handle the large amount of data generated by the massive number of nodes. The architecture's procedure allocates tasks to LoRaWAN devices by exploiting their different computing capabilities. Simulation results on real maps of both Abu Dhabi and Beijing demonstrate the high performance and scalability of the architecture. Numerical results also show that the encoder model can effectively reduce network packet size by extracting data features.
Optimizing Waveform Power Allocation in Cognitive DFRC Systems: An Individual User AoI Preference-Based Approach

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.
EEG Channel Analysis for Energy Data Visualizations Using GAF Encoding and Deep Learning

Electroencephalography (EEG) is a technique used to assess brain activity during cognitive tasks. EEG signals are generated by a variety of sensors, and due to their inherent complexity, analyzing EEGs can be difficult to retrieve task-related information. To improve the performance of EEG applications, selecting useful EEG channels and minimizing irrelevant channel information can improve the quality of analysis and results. This study focuses on the classification performance evaluation of EEG channels. A unique EEG dataset containing EEG signals is used in response to energy data visualization stimuli to capture users' emotions. To analyze valence emotion in data visualizations, EEG signals are transformed into Gramian Angular Fields (GAF) images, and Deep Learning (DL) features retrieved from GAFs using the Convolutional Neural Network (CNN) -Long-Short Term Memory (LSTM) model. To evaluate valence emotion, a boosting classifier is utilized using a DL feature set reduced by a Principal Component Analysis (PCA) approach. The results demonstrate the success of the technique employed to identify the best channels. The F3 channel has the highest accuracy of 99.76%, while the Fp2 channel has a comparable accuracy of 99.52%. Overall, the suggested technique shows promising outcomes in identifying valence emotion for energy data representations.
Channel Selective Relation Network for Efficient Few-shot Facial Expression Recognition

Few-shot learning-based facial expression recognition (FER) aims to achieve maximum efficiency from a few numbers of data. Therefore, it is significant to utilize the given training dataset efficiently. However, the existing few-shot FERs are too dependent on the datasets they trained, so it is challenging to generalize the FER performance. To address the problem, we propose a Channel Selective Relation Network with channel selection module and spatial data construction to train optimal features. Our method helps the network to prevent irrelevant information and focus on essential information by comparing the original sample features with the averaged feature. Furthermore, our network efficiently learns dominant facial expression features in local patches, such as the eyes and lips. Compared to the current state-of-the-art method, the average performances on RAFDB, FER2013, SFEW, and AFEW datasets are improved by 3.5%, 4.44%, 5.58%, and 2.31% in accuracy, respectively.
Video Compression by Chroma Prediction Using Semantic Communications

Conventional video coding is evolving to meet unprecedented consumer device requirements, but the statistical signal processing based approach may find limitations in handling new media contents. Deep neural network and semantic communication based video compression systems show potential to be used as video encoders and decoders, but reaching the rate distortion performance of state-of-the-art conventional video coding systems remains to be achieved. A novel video compression system by predicting the chroma components of video using the semantically encoded luma component and reference intra-coded frames is proposed and tested against high-efficiency video coding for bit rate comparison and rate-distortion performance evaluation. The proposed system demonstrated 18% to 30% saving of bit rate for high and medium motion videos without significant reductions of rate distortion, the saving increasing with a larger group of picture sizes, but low motion videos only demonstrated negligible savings.