Perception Layer Research Articles

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

AbstractThe technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.

Location privacy protection of nodes in the perception layer of the Internet of things based on Radio Frequency Identification location aware algorithm

AbstractTo ensure the security of node location privacy in the perception layer of the Internet of Things, this study proposes a location privacy protection method based on RFID location awareness algorithm. This method first introduces an adaptive multi tree anti‐collision algorithm based on collision trees when perceiving and locating the location information of nodes in the perception layer of the Internet of Things, to prevent signal collisions between multiple readers and writers. Then, based on the obtained node location results, a IoT perception layer node location privacy protection scheme based on virtual ring strategy is used to randomly initiate directed co hop routing using the virtual nodes generated after random walking, avoiding the visible area of the source node location, and transmitting data to the aggregation node in the form of the shortest routing path to protect the privacy and security of the node location. Finally, experiments are carried out to prove the progressiveness of the proposed method. The experimental results show that this method can prolong the security period of the source and aggregation nodes in the perception layer of the Internet of Things, protect the privacy and security of node positions, and has great application value.

Ethnic Return Migrants in Korea and Japan: A Comparative Study of Nationalism and Citizenship

Abstract: This study explores the complicated nature of the citizenship of ethnic return migrants from Japan and Korea, Nikkeijin and Joseonjok. It aims to contribute to the scholarship by demonstrating that racism and nationalism are layered concepts by examining the reasons why Nikkeijin and Joseonjok enjoy preferential treatment to some extent but at the same time become targets of discrimination in Korea and Japan. I draw on Christian Joppke's position that citizenship is composed not only of legal status but also of social and identity elements and utilize two relatively novel concepts that shed new light on the complex nature of Nikkeijin and Joseonjok citizenship. One is Takeyuki Tsuda's concept of ethnoracism, which helps capture the type of racism aimed at groups that are not racially distinct but whose ostensible background differences are essentialized as inferior. The other is Gil-Soo Han's nouveau-riche nationalism, which argues that migrants are evaluated according to the global and developmental status of their country of origin. This comparative analysis helps demonstrate how the lives of Joseonjok and Nikkeijin are shaped by multiple layers of nationalism, racism, and status perceptions in their respective countries of residence.

Что такое фикшен? Фреймы в мультсериале «Подозрительная сова»

Explore the intricate relationship between reality and fiction in a thought-provoking examination of how we perceive and interact with fictional forms. Delve into the fascinating complexities of human experience through cinema, theater, and video games, and witness how they reflect and imitate our own reality. Understand the sociological underpinnings as illustrated by the American sociologist of everyday life Erving Goffman and embodied in the Suspicious Owl animated series. This exploration unravels the layers of perception and interpretation, demonstrating how our engagement with fiction influences our understanding of the real world. Whether you’re a fan of literature, film, or simply intrigued by the mechanics of the human mind, this perspective on the interplay between the real and unreal offers a compelling lens through which to view our daily lives. It’s a journey into the essence of our very being, a reflection of how we see ourselves, and a challenge to our assumptions about what is truly real.

An Optimization-Based Feature Selection and Hybrid Spiking VGG 16 for Intrusion Detection in the CPS Perception Layer

An Optimization-Based Feature Selection and Hybrid Spiking VGG 16 for Intrusion Detection in the CPS Perception Layer

Analysis of Intelligent Living for Elderly in Smart Aging

This paper conducts an in-depth investigation into smart home systems and their developmental trajectory. It systematically dissects the constituent elements of smart homes, encompassing the perception layer, transmission layer, platform layer, and application layer. The paper analyzes the intelligent living forms and types for elderly individuals at home, including environmentally proactive monitoring and types of intelligent living such as life assistance, health and safety, and leisure and entertainment. The research explores interaction modes in smart home systems, incorporating mechanical interaction, voice interaction, and screen interaction. It underscores the significance of screen interaction as a primary control method in the intelligent living of elderly individuals at home. This paper provides profound theoretical foundations and practical guidance for the intelligent living of elderly individuals at home.

INCEPTION TO SENSOR AND IoT TECHNOLOGY

The Internet of Things (IoT) is the recent research topic. The term “Internet of Things" (IoT) is a term that was conceived in 1999 by Kevin Ashton. The Internet of Things (IoT) is a network of physical objects or "things" that are embedded with devices, software, sensors, and networking in order to provide more value. By sharing data with the manufacturer, and by providing service operator or other devices that are linked, every single element is different. It is distinguishable due to its embedded computing system, but able to work together in the current internet. The IoT is being implemented in every hook and corner of industry and personal life of people. Governments are taking note and examining IoT from different perspectives. One aspect is how IoT system can help with performance, analytics and other tasks. Intelligence and decision making are the most important aspects. It combines various sensors and objects to interact directly with one another without human interference. This research paper focuses on the state of the art of IoT architecture. It has four significant layers, the Perception Layer, Network Layer, Middle Layer and Application Layer. The sensors can provide information on emerging health-care issues. Sensors have recently been considered as one of the most rapidly developing fields in physics, electronics, and biotechnology, and it is the one that has benefited the most from advances in individual microelectronics, optical, and computer sciences technologies.

ALICA: A Multi-S-Box Lightweight Cryptographic Algorithm Based on Generalized Feistel Structure

With the development of science and technology, IoT devices have already become ubiquitous in the public eye. Through the perception layer, the collected data are displayed or transmitted to the server backend for analysis. Due to the increasing integration of IoT devices into people’s daily lives, privacy issues, such as data leaks, have received more attention. Most sensor nodes, such as temperature and pressure sensors in marine environments, have low computing power, storage capacity, and significant underlying heterogeneity, making it challenging to implement a standardized data security protection solution. Data security in the nodes is seriously challenged as a result. It is of great significance to design a lightweight block cipher for the Internet of Things (IoT) environment, which can ensure the security of node information. A new lightweight block cipher algorithm called ALICA is proposed in this paper, which is well-suited for the low computing power and heterogeneous device environment at the lower layers. A generalized Feistel structure and a linear structure with XOR and shift operations are used to achieve ease of software implementation. Two different S-boxes are used to create the nonlinear structure of the password, thereby enhancing the robust security of the cipher. In addition, the cipher adopts a design approach that prioritizes software efficiency while also considering hardware implementation efficiency. This makes it more suitable for low computing power, storage capacity, and resource-limited IoT sensor nodes at the lower layers.

Poster Session I: Binocular contrast integration: Cortical and behavioral signals reflect different computations.

Although binocular contrast perception under dichoptic viewing conditions has been extensively characterized behaviorally, little is known about how the signals from each eye are combined in cortex. Here we compared simultaneously-collected behavioral and EEG measures of dichoptic contrast perception. Observers (n=16) dichoptically viewed a 2-cpd grating flickering at 7.5 Hz and rotating slowly (1º/s). The contrast of the grating shown to each eye modulated sinusoidally over time at independent rates (1/6 and 1/8 Hz). We recorded EEG activity while observers positioned a joystick lever to report perceived contrast as it changed over time. A multiband filter was used to isolate the SSVEP responses to 7.5 Hz signals from electrode Oz (occipital pole), and the standard deviation of this signal provided a measure of neural response amplitude as a function of contrast. A simple model was fit to VEP and behavioral responses, [(L^m+R^m)/2]^(1/m)], which essentially characterized whether responses were better fit by a mean (m≈1) or a max (m>1) model. VEP responses (m = 1.0; MSE = 0.013) were well fit by a mean model, suggesting the EEG signal may have been driven by the input layers of V1. In contrast, behavioral responses (m = 8.8; MSE = 0.011) were well fit by a model that was heavily shifted towards a max model, suggesting a significant non-linear transformation of the contrast signal between the input layers of V1 and conscious perception.

Marine pasture monitoring system based on edge computing

With the continuous optimization and upgrading of Marine development, it is particularly important to build Marine pastures that play a pivotal role in the Marine economy. This study discusses the problems and key technologies faced by the construction of Marine pastures. It uses the underwater Internet of Things technology to propose a smart ocean pasture solution based on edge computing and designs a three-level and six-layer system architecture from top to bottom. Taking the target object of the perception layer, fish, for example, is processed and analyzed from the environmental elements and biological characteristics. The watercolor, species, and quantity are monitored. This realizes the purpose of systematic monitoring and early warning of Marine pasture and provides a technical reference for realizing intelligent ocean engineering with high performance and low power consumption.

A novel routing optimization strategy based on reinforcement learning in perception layer networks

Wireless sensor networks have become incredibly popular due to the Internet of Things’ (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for low-power and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm’s effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

Multi-chain blockchain based secure data-sharing framework for industrial IoTs smart devices in petroleum industry

Industry 4.0 efficiently enhances the manufacturing process in many aspects such as increasing system performance, productivity, cost reduction, and building large-scale systems with enable autonomous operation. Numerous heterogeneous devices and sensors are connecting to the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional schemes adopted centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and latency making them unsuitable for resource-constrained IoTs devices. In our work, we proposed a decentralized, low-power consumption, fast, scalable, secure, privacy-preserving, and trusting architecture-based private blockchain network/smart contract and interplanetary file system for the petroleum industry in Iraq. Our scheme improves blockchain performance by adopting the incremental aggregator subsector commitment instated of Merkle tree and a multi-chain proof of rapid authentication as consensus algorithms, which are specially designed for industrial IoTs applications. It provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability, traceability, and information backtracking. In addition to addressing the blockchain storage limitation and eliminating third-party dependencies by adopting an interplanetary file system (IPFS). Also provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm with internet friendly ARM Cortex-M33 Microcontroller as clear in the experimental results. Moreover, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the petroleum industry as an industrial application area, and provides an efficient framework that resists common cyber-security attacks by achieving data integrity, availability, and confidentiality.

Studying Imbalanced Learning for Anomaly-Based Intelligent IDS for Mission-Critical Internet of Things

Training-anomaly-based, machine-learning-based, intrusion detection systems (AMiDS) for use in critical Internet of Things (CioT) systems and military Internet of Things (MioT) environments may involve synthetic data or publicly simulated data due to data restrictions, data scarcity, or both. However, synthetic data can be unrealistic and potentially biased, and simulated data are invariably static, unrealistic, and prone to obsolescence. Building an AMiDS logical model to predict the deviation from normal behavior in MioT and CioT devices operating at the sensing or perception layer due to adversarial attacks often requires the model to be trained using current and realistic data. Unfortunately, while real-time data are realistic and relevant, they are largely imbalanced. Imbalanced data have a skewed class distribution and low-similarity index, thus hindering the model’s ability to recognize important features in the dataset and make accurate predictions. Data-driven learning using data sampling, resampling, and generative methods can lessen the adverse impact of a data imbalance on the AMiDS model’s performance and prediction accuracy. Generative methods enable passive adversarial learning. This paper investigates several data sampling, resampling, and generative methods. It examines their impacts on the performance and prediction accuracy of AMiDS models trained using imbalanced data drawn from the UNSW_2018_IoT_Botnet dataset, a publicly available IoT dataset from the IEEEDataPort. Furthermore, it evaluates the performance and predictability of these models when trained using data transformation methods, such as normalization and one-hot encoding, to cover a skewed distribution, data sampling and resampling methods to address data imbalances, and generative methods to train the models to increase the model’s robustness to recognize new but similar attacks. In this initial study, we focus on CioT systems and train PCA-based and oSVM-based AMiDS models constructed using low-complexity PCA and one-class SVM (oSVM) ML algorithms to fit an imbalanced ground truth IoT dataset. Overall, we consider the rare event prediction case where the minority class distribution is disproportionately low compared to the majority class distribution. We plan to use transfer learning in future studies to generalize our initial findings to the MioT environment. We focus on CioT systems and MioT environments instead of traditional or non-critical IoT environments due to the stringent low energy, the minimal response time constraints, and the variety of low-power, situational-aware (or both) things operating at the sensing or perception layer in a highly complex and open environment.

Construction of the flipped classroom teaching mode of British and American literature in the context of big data and IoT

With the advancement of cultural globalization, China has increased its support for the study of British and American literature. However, due to the great differences in the forms of expression and thinking logic between British and American literature and Chinese local literature, it is difficult for Chinese students to learn British and American literature. In order to solve the dilemma of Chinese students learning English and American literature, the team constructed a smart classroom model with flipped classroom as the core based on big data and Internet of Things technology. The smart classroom model is constructed according to the four-layer architecture of the Internet of Things, which is divided into perception layer, network layer, platform layer and application layer. The flipped classroom is in the application layer. The flipped classroom is designed according to the three processes before, during and after class, which has certain theoretical significance. In order to verify the effectiveness of this model, a university in H city was selected for research. Through the control experiment, it was concluded that within a certain error range, the flipped classroom teaching mode can significantly improve the teaching effect of British and American literature compared with the traditional teaching mode. Finally, based on the problems of English and American literature learning, we further deepen the model of flipped classroom. In the pre-course pre-study stage, teachers should play their organizational role and enhance students’ pre-study effect through interesting pre-study methods. Teachers should also pay attention to students’ pre-study status and make timely adjustments. In the classroom, teachers need to find students’ questions in time and answer common questions in a unified manner and answer individual questions in private, in an effort to improve classroom efficiency and promote the process of internalizing students’ knowledge. At the end of the flipped classroom, the classroom should also summarize the lesson, recognize the strengths and weaknesses of the lesson implementation based on the feedback from the students, and clarify the room for improvement of the lesson. At the same time, teachers should also reflect on the flipped classroom model and comprehensively assess its strengths and weaknesses to ensure that this model can be applied to its full value.

Research and Application of Anti-electric Shock Communication Terminal Based on Internet of Things Technology

Leakage protection cannot effectively identify the voltage and current reception signals of electric shock grounding points that may pose a danger to personal safety through analysis of electric shock parameters, which is inherently inadequate. When a communication electric shock safety accident occurs, the accident site can be visually displayed in the safety prevention and control center, and an alarm is sent out. Meanwhile, relevant control instructions are issued to cut off the accident communication phase line to prevent secondary injury. In this paper, artificial intelligence and Internet of Things technology are applied to the communication system. The tag sensor is used in the monitoring system and is also used as the perception layer. The electric shock receiving signal obtained under the control circuit structure of the Internet of Things is determined by means of S transformation. The physical characteristics of wave frequency sensitivity are analyzed. Then, the external characteristics of biological shock are extracted with the help of the working frequency of each band of wavelet multi-resolution analysis, so that the electric shock model can deal with the hazards that cannot be identified by the traditional perception layer. The deep neural network is optimized to improve the recognition probability. Experiments verify that the recognition rate of the biological electric shock is as high as 99%, which further improves the stability of the leakage protection device and provides a guarantee for the normal operation of the communication system.

BIoTS-Path: Certification Transmission of Supply Chains Based on Blockchain–Internet of Things Architectures by Validating the Information Path

A food traceability system (FTS) can record information about processes along a production chain to determine their safety and quality. Under the Internet of Things (IoT) concept, the communication technologies that support FTSs act as platforms for mass access to information with limited security. However, the integrity of the collected data is not immune to security attacks. This paper proposes a point-to-point information transmission path with no edges or access boundaries (no intermediaries) to transmit data with integrity. This route is possible thanks to the architectural articulation of a hardware device (sensor BIoTS) at the perception layer, with the Blockchain architecture at the application layer. This pairing makes an ecosystem with the ability to trace and certify in parallel the products, the supply chain processes, and the data recorded in it possible. The design of the security testing ecosystem is based on the theoretical and technical principles of cybersecurity. It is executed through mathematical models that define the probability of attacks’ success against the transmitted data’s integrity. The security tests performed allow for establishing that this BIoTS information transmission route is unlikely to suffer from transmission vulnerabilities and that it is not prone to security attacks against integrity. This work paves the way toward fully integrating Blockchain technology in dedicated IoT architectures.

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm’s confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

Multipath Interference Analysis for Low-Power RFID-Sensor Under Metal Medium Environment

Mechanism analysis of the RFID-Sensor communication interference can effectively improve the information collection quality of the perception layer. In our study, multipath Interference analysis of low-power RFID-Sensor under metal medium environment was considered. Firstly, the dual-antenna RFID-Sensor architecture was developed based on low power strategy, the multipath loss model and interference theory under metal medium environment was discussed. Secondly, by full consideration of electromagnetic interference energy loss factors, the time-frequency domain characteristics of electromagnetic signals, such as direct radiation, refraction, diffraction, scattering, absorption and polarization, were analyzed. Thirdly, the normalized electric field strength between the low-power RFID-Sensor with different distance was obtained through simulation/experiment scenario. The distance between the low-power RFID-Sensor and transmitting antennas is represented by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> , when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> is from 1 m to 5 m, the minimum working distance between the RFID-Sensor antenna and the metal medium surface changed from 6.9 mm to 7.0 mm at the frequency of 915 MHz. This paper was conducive for expanding the functions of the perception layer system of the IoT and enhancing the application potential of existing RFID and sensor networks.

Distributed cooperative reinforcement learning for multi‐agent system with collision avoidance

AbstractIn this work, we present an optimal cooperative control scheme for a multi‐agent system in an unknown dynamic obstacle environment, based on an improved distributed cooperative reinforcement learning (RL) strategy with a three‐layer collaborative mechanism. The three collaborative layers are collaborative perception layer, collaborative control layer, and collaborative evaluation layer. The incorporation of collaborative perception expands the perception range of a single agent, and improves the early warning ability of the agents for the obstacles. Neural networks (NNs) are employed to approximate the cost function and the optimal controller of each agent, where the NN weight matrices are collaboratively optimized to achieve global optimal performance. The distinction of the proposed control strategy is that cooperation of the agents is embodied not only in the input of NNs (in a collaborative perception layer) but also in their weight updating procedure (in the collaborative evaluation and collaborative control layers). Comparative simulations are carried out to demonstrate the effectiveness and performance of the proposed RL‐based cooperative control scheme.

Research on Fault Diagnosis System of IOT for Oil Well Pump Based on Machine Learning

Abstract In order to realize automatic prediction and processing of remote fault diagnosis of oil well pumps distributed in different regions by crude oil production enterprises, a fault diagnosis system for oil well pumps based on machine learning was researched and designed. This fault diagnosis system is composed of three layers (perception layer, network layer and control application layer) Internet of Things structure. The function and characteristics of each layer are analyzed in this paper, and the hardware composition and control principle of sensor nodes and aggregation nodes of the measurement and control system are discussed and gives the node microcontroller program design flow chart and the main module content of the IoT central computer software design. This paper focuses on the principle of machine learning for fault diagnosis and prediction, and deeply explains the algorithm steps of using LSTM for fault diagnosis of oil well pumps. The enterprise application experiment results show that, compared with the traditional manual well patrol fault diagnosis method, this system has the advantages of convenient operation and maintenance, low labor intensity, high timeliness and accuracy of fault diagnosis, which can better reduce equipment maintenance costs for enterprises.