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Computationally intensive Inference tasks of Deep neural networks have enforced revolution of new accelerator architecture to reduce power consumption as well as latency. The key figure of merit in hardware inference accelerators is the number of multiply-and-accumulation operations per watt (MACs/W), where, the state-of-the-arts MACs/W remains several hundreds Giga-MACs/W. We propose a Tera-MACS/W neural hardware inference Accelerator (TMA) with 8-bit activations and scalable integer weights less than 1-byte. The architectures main feature is configurable neural processing element for matrix-vector operations. The proposed neural processing element has Multiplier-less Massive Parallel Processor to work without any multiplications, which makes it attractive for energy efficient high-performance neural network applications. We benchmark our systems latency, power, and performance using Alexnet trained on ImageNet. Finally, we compared our accelerators throughput and power consumption to the prior works. The proposed accelerator outperforms the state of the art in terms of energy and area achieving 2.3 TMACS/W@1.0 V, 65 nm CMOS technology.

The purpose of this research is to design a simple and intuitive path tracker to minimise the distance error of a car-like delivery service robot using only a position sensor, i.e. global positioning system in outdoor environments. The authors proposed not only a single-point preview path tracker (SPP), but also a curvature-based preview control using a quadratic curve fitting technique. This method can provide flexibility in preview distance control even at a constant speed, and reduces the burden of an actuator for maintenance. Furthermore, the performance of the proposed adaptive SPP is considerably improved compared to that when using a fixed preview distance.

We propose an artificial deep neural network- (ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-the-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle’s behavior resemble a real vehicle.

We propose a noninvasive method for determining Langer’s tension lines and tracking two-dimensional (2D) deformation of human skin utilizing spatially resolved diffuse reflectance (SRDR) of infrared (IR) light measurement in vivo. The anisotropic light propagation pattern of the skin was measured at several locations on the human body; then, the principal axis (i.e., eigenvector) of the radial plot of equi-distant intensity lines was obtained to determine the orientation of the scattering-induced anisotropic reflectance of IR light. The results indicate the proposed method is able to determine preferential orientation of filamentous fiber with a reasonable degree of prediction accuracy over the entire body. We validated the proposed method by comparing the detected orientations of fiber with a previous scattering model-based light propagation anisotropy analysis method. We measured temperature dependence and tensile stress-induced changes of the spatially resolved reflectance of IR light on the skin. The surface temperature affects the light transmittance almost isotopically on the propagation patterns. The main contribution of this work is tracking the direction of tension causing 2D deformation of human skin without visible markers. The proposed method is applicable to sensing facial gestures or skin movements induced by human motion of skeletal or facial muscles.

The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

The recent growth and adoption of Unmanned Aerial Vehicles (UAVs) is due to its low development cost, high aerial mobility, advanced battery technology, rotors, gyroscopes, GPS, cameras, sensors and a wide range of applications. The UAV offers new possibilities for business in civil and non-civil applications such as parcel delivery, aerial mapping, agriculture, wildlife conservation, surveillance, search and rescue, etc. However, considering the exponential growth in various types of aerial vehicles, it is clear that advanced air traffic management services are urgently needed to control and handle the ever increasing air traffics. In this paper, we first present the two major low altitude UAV traffic management system in the USA and Europe and compare them thoroughly. We present the global vision and advancement of UAV traffic management system, and then discuss the existing research trends. Understanding the complexity of future air transportation system, we discuss the necessary elements required for urban air traffic management system. We provide key strategic elements for an advanced urban traffic management through connectivity, open source, safety enhancement and automation to maximize its impacts on the innovative future air traffic and mobility system. In the end, we discuss the challenging security issues in urban traffic management system.

We propose a novel type of near-infrared (NIR) based sensory system for the detection of skin deformation induced by facial expressions. The proposed system utilizes the diffusion property which was called spatially resolved diffuse reflectance of NIR light in human skin as clues of detecting skin deformations. The sensor was mainly composed of NIR laser diode and a camera as a light source and receiver. To refine the information from captured IR raw images, we exploited principal component analysis based preprocessing procedure. By producing sensor data collecting experiment on several participants, we've verified the facial deformation tracking performance of the proposed sensory system.

We propose a single inductor multiple output (SIMO) auto-buck-boost DC&ndash, DC converter with error-driven randomized control (EDRC). The conventional controls in a SIMO DC&ndash, DC converter supply power to outputs that have been selected in a sequential order. Furthermore, they control the inductor current levels at either edge of a switching period in a steady state to be at the same level to alleviate cross-regulation. However, this limits the flexibility of the converter to respond to changes in load requirements. A sequential selection of light loads results in these loads being selected more often than a load demand, degrading the efficiency for light loads. In addition, limited flexibility leads to delayed responses. This paper introduces an auto-buck-boost topology that selects outputs based on output errors, and instantaneously adjusts the inductor current level. Moreover, we propose a technique for allowing any output to avoid selection when all outputs are fully supplied. The proposed EDRC scheme achieves improvements in efficiency in regards to light loads, cross-regulation, and output driving capability.

Intelligent vehicle (IV) is an internet-enabled vehicle, commonly referred to as a self-driving car, which enables vehicles-to-everything communications. This communication environment is not secure and has several vulnerabilities. The major issues in IV communication are trustworthiness, accuracy, and security of received and broadcasted data in the communication channel. In this article, we introduce blockchain technology to build trust and reliability in peer-to-peer networks with topologies similar to IV communication. Further, we propose a blockchain-technology-enabled IV communication use case. Blockchain technology is used to build a secure, trusted environment for IV communication. This trusted environment provides a secure, distributed, and decentralized mechanism for communication between IVs, without sharing their personal information in the intelligent transportation system. Our proposed method comprises of a local dynamic blockchain (LDB) and main blockchain, enabled with a secure and unique crypto ID called intelligent vehicle trust point (IVTP). The IVTP ensures trustworthiness among vehicles. Vehicles use and verify the IVTP with the LDB to communicate with other vehicles. For evaluation, we simulated our proposed blockchain technology-based IV communication in a common intersection deadlock use case. The performance of the traditional blockchain is evaluated with emphasis on real-time traffic scenarios. We also introduce LDB branching, along with a branching and un-branching algorithm for automating the branching process for IV communication.

Developing a user interface (UI) suitable for headset environments is one of the challenges in the field of augmented reality (AR) technologies. This study proposes a hands-free UI for an AR headset that exploits facial gestures of the wearer to recognize user intentions. The facial gestures of the headset wearer are detected by a custom-designed sensor that detects skin deformation based on infrared diffusion characteristics of human skin. We designed a deep neural network classifier to determine the user&rsquo, s intended gestures from skin-deformation data, which are exploited as user input commands for the proposed UI system. The proposed classifier is composed of a spatiotemporal autoencoder and deep embedded clustering algorithm, trained in an unsupervised manner. The UI device was embedded in a commercial AR headset, and several experiments were performed on the online sensor data to verify operation of the device. We achieved implementation of a hands-free UI for an AR headset with average accuracy of 95.4% user-command recognition, as determined through tests by participants.

With the rapid evolution in wireless communications and autonomous vehicles, intelligent and autonomous vehicles will be launched soon. Vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and road information in real-time in vehicular networks. V2X has evolved by integrating cellular 5G and New Radio (NR) access technology in V2X communications (i.e., 5G NR V2X); it can fulfill the ever-evolving vehicular application, communication, and service demands of connected vehicles, such as ultra-low latency, ultra-high bandwidth, ultra-high reliability, and security. However, with the increasing number of intelligent and autonomous vehicles and their safety requirements, there is a backlash in deployment and management because of scalability, poor security and less flexibility. Multi-access Edge Computing (MEC) plays a significant role in bringing cloud services closer to vehicular nodes, which reduces the scalability and flexibility issues. In addition, blockchain has evolved as an effective technology enabler to solve several security, privacy, and networking issues faced by the current 5G-based MEC systems in vehicular networks. Blockchain can be integrated as a strong security mechanism for securing and managing 5G V2X along with MEC. In this survey, we discuss, in detail, state-of-the-art V2X, its evolution based on cellular 5G technology and non-cellular 802.11bd. We investigate the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. We present the issues and challenges in existing edge computing and 5G V2X and, then, we shed some light on future research directions in these integrated and emerging technologies.

Many significant research achievements in the past few decades have demonstrated that convolutional neural networks (CNNs) could be capable of steering wheel control which is the basic and essential maneuver of autonomous vehicles. We propose an end-to-end steering controller with CNN-based closed-loop feedback for autonomous vehicles that improves driving performance compared to traditional CNN-based approaches. This paper demonstrates that the proposed neural network, DAVE-2SKY, is able to learn to inference steering wheel angles for the lateral control of self-driving vehicles through initial supervised pre-training and subsequent reinforced closed-loop post-training with images from a camera mounted on the vehicle. We used the PreScan simulator and Caffe deep learning framework for training under diversified circumstances in a software in the loop (SIL) simulation environment. We used DRIVE TM PX2 computer to implement a self-driving car for an experimental validation of the proposed end-to-end controller. The performance of the proposed system has been investigated under simulations and on-road tests as well. This work shows that the CNN-based end-to-end controller performs robust steering control even under partially observable road conditions, which indicates the possibility of fully self-driving vehicles controlled by CNN-based end-to-end steering controllers.

The Intelligent vehicle is experiencing revolutionary growth in research and industry, but it still suffers from a lot of security vulnerabilities. Traditional security methods are incapable of providing secure IV, mainly in terms of communication. In IV communication, major issues are trust and data accuracy of received and broadcasted reliable data in the communication channel. Blockchain technology works for the cryptocurrency, Bitcoin which has been recently used to build trust and reliability in peer-to-peer networks with similar topologies to IV Communication world. IV to IV, communicate in a decentralized manner within communication networks. In this paper, we have proposed, Trust Bit (TB) for IV communication among IVs using Blockchain technology. Our proposed trust bit provides surety for each IVs broadcasted data, to be secure and reliable in every particular networks. Our Trust Bit is a symbol of trustworthiness of vehicles behavior, and vehicles legal and illegal action. Our proposal also includes a reward system, which can exchange some TB among IVs, during successful communication. For the data management of this trust bit, we have used blockchain technology in the vehicular cloud, which can store all Trust bit details and can be accessed by IV anywhere and anytime. Our proposal provides secure and reliable information. We evaluate our proposal with the help of IV communication on intersection use case which analyzes a variety of trustworthiness between IVs during communication.

Internet of Things (IoT) is now in its initial stage but very soon, it is going to influence almost every day-to-day items we use. The more it will be included in our lifestyle, more will be the threat of it being misused. There is an urgent need to make IoT devices secure from getting cracked. Very soon IoT is going to expand the area for the cyber-attacks on homes and businesses by transforming objects that were used to be offline into online systems. Existing security technologies are just not enough to deal with this problem. Blockchain has emerged as the possible solution for creating more secure IoT systems in the time to come. In this paper, first an overview of the blockchain technology and its implementation has been explained; then we have discussed the infrastructure of IoT which is based on Blockchain network and at last a model has been provided for the security of internet of things using blockchain.

The intelligent vehicle communication network is prone to cyberthreats, which are difficult to solve using traditional centralized security approaches. Blockchain is an immutable peer-to-peer distributed database containing cryptographically secured information. Blockchain shows successful use cases in financial applications, smart contact, protecting digital copyright of media contents. It extends to all industries including the secure IoT devices, embedded systems, etc. The superior feature of blockchain is its decentralized, immutable, auditable database that secures transactions by protecting privacy. In this chapter, we contemplate the environment of the intelligent vehicle communication network and issues regarding methods of building a blockchain-based trust network among intelligent vehicles. We present the use cases of blockchain in intelligent vehicles in the phase of ongoing research or that under development from automotive industries and academic institutes. We also deliberate the challenging issue of blockchain for intelligent vehicles.

We proposed a hand-free Natural User Interface (NUI) for VR Head-Mounted-Display (HMD) with infra-red (IR) based sensor tracking facial gestures. We have realized NUI based on the real-time recognition of user intuitions for VR HMD without any additional control devices except for a built-in Gyroscope and IR couplers with readout circuitry integrated in the foam interface of an HMD. We implemented seven control commands affordable for 3D interactions with virtual objects. The experimental data show that the proposed system provides a convenient and efficient 2D/3D user interface for both manipulating objects and controlling commands while wearing a VR HMD.

The Intelligent vehicle (IV) is experiencing revolutionary growth in research and industry, but it still suffers from many security vulnerabilities. Traditio nal security methods are incapable to provide secure IV communication. The major issues in IV communication are trust, data accuracy and reliability of communicatio n data in the communication channel. Blockchain technology works for the crypto currency, Bit-coin, which is recently used to build trust and reliability in peer-to-peer networks having similar topologies as IV Communication. In this paper, we introduce an Intelligent Vehicle-Trust Point (IV-TP) mechanism for IV communication among IVs using Blockchain technology. The IVs communicated data provides security and reliability using our proposed IV-TP. This IV-TP mechanism introduced in the paper provides trustworthiness for vehicles behavior, and vehicles legal and illegalaction. The introduced reward-based system is an exchange of some IV-TP among IVs, during successful communication.

An intelligent vehicle (Self-driving) is an emerging research area. Presently, many industries, research organizations and universities are working on intelligent vehicles. Some researchers are working on security issues of intelligent vehicles (IV). Most of the researchers focus on static security (static security cannot adapt to the changes in the environment). Since the environment of IV is dynamically changing, the approach of static security is not feasible to provide reconcile cyber-security in IV. Reconciling systems evolve in a very uncertain environment and try to fulfill a business objective by sensing the environment, analyzing it and taking the best decision according to its requirements. In this paper, we discuss reconcile security requirements engineering to provide cyber-security in IV. To fulfill our objective, we use the goal modeling approach of RE, the Knowledge Acquisition in Automated Specification (KAOS) Model approach, and designed it using Objectiver tool to find the reconciling requirements. Self-reconciliation in requirement engineering is acquired by five building blocks such as Monitor, Analyze, Plan, Execute and Knowledge i.e. [MAPE-K] loop architecture, which provides the reconciling capabilities to our proposed reconciling requirements for cyber-security in IV.

We propose a real-time near-optimal motion and trajectory planner using an interior-point method (IPM) based simultaneous dynamic optimization approach applicable to automatic parallel parking. Partitioning an automatic parking zone into two areas with and without inequality constraints drastically improves the convergence efficiency of the IPM-based simultaneous dynamic optimization of nonlinear programs while retaining the accuracy and stability of simulation for optimization. An optimal ready-to-reverse point (RRP), from where backward moving parking maneuver starts, is provided based on the cost function of the tracking time and additional delayed time of gear shifts caused by multiple maneuvers. The stability and accuracy of the proposed method was verified through simulation using AMPL with an IPOPT solver.

In fully autonomous vehicles, occupants do not need to keep their eyes forward during driving and they are able to sit on any position, from a forward-facing position to a backward-facing. However, traditional passive safety systems such as seat belts, airbags, and air curtains have been developed only for the driver or passengers sitting on forward facing seats. In this paper, we propose a pre-crash safety system for an occupant sitting on a backward facing seat in front-end crashes. The proposed safety system starts to move the seat horizontally and tilt the seatback prior to a front-end collision to reduce crash force which may cause whiplash injury. The simulated results show a remarkable improvement in reduction of crashing force impact on occupant's whiplash injury using Honda Accord models posted on National Highway Traffic Safety Administration (NHTSA) and the hybrid III dummies with MADYMO 7.6 simulator.

We have implemented a mixed reality telepresence platform providing a user experience (UX) of exchanging emotional expressions as well as information among a group of participants. The implemented system provides a platform to experience an immersive live scene through a Head-Mounted Display (HMD) and sensory information to a VR HMD user at a remote place. Moreover, the user at a remote place can share and exchange emotional expressions with other users at another remote location by using 360° cameras, environmental sensors compliant with MPEG-V, and a game cloud server combined with a technique of holographic display. We demonstrated that emotional expressions of an HMD worn participant were shared with a group of other participants in the remote place while watching a sports game on a big screen TV.

Immersive VR, a technology getting attention in recent years, is widely applied to the realm of serious games because it can provide users with both fun and intriguing experiences. This poster proposes a self-training VR serious game for fire evacuation drill with concurrent tele-collaboration among avatars controlled by and synchronized with multiple-users in remote places. We introduce a system architecture of both single user and its extension to multiple-user system. The single user system consists of wearable sensors and 3D VR HMD to synchronize a user's motions to one's own avatar in the virtual environment. We can easily extend the system to the multi-user mode through the Unity game cloud server. The multi-user mode enable players to experience a tele-existence so that they can collaborate in the virtual environment, and they can concurrently navigate while interacting with virtual objects as if they coexist in the same space.

We have proposed and implemented a hologram based tele-existence platform to provide user experience of emotional exchange among a group of participants in both real and virtual environments. The system provides not only immersive live scene to a user wearing VR HMD at a remote place, but also share and exchange emotional expressions with other users at other place by using a 360° camera and cloud server combined with a holographic technique. In the experiments, 6-different basic emotional expressions of user wearing VR HMD were transmitted, and replayed on the hologram display at the place where the group of participants were watching broadcasting of a sport through large screen TV.

We propose an output error-driven random controlled Single Inductor Multiple Output (SIMO) buck/boost DC-DC converter operated in the Continuous Conduction Mode (CCM). Random selection of output switches based on maximum instantaneous output error enables us a novel control scheme which leads to a fast transient response with mitigating the cross-regulation problem of buck-boost converters. A freewheeling period for CCM control is not necessary in the proposed output error-driven control scheme thanks to the instantaneous control of output switches, hence, we can achieve high power conversion efficiency as well as stability of operation. Both simulated and experimental results show the effectiveness of the proposed control scheme. The measured power conversion efficiency of the converter with 3 buck/boost output nodes is approximately 91.0%.

We proposed an IR-based facial expression tracking sensor for Head-Mounted Displays(HMD). The proposed sensor uses lateral propagation characteristics of IR light on human skin to capture the degree of compressed or stretched deformations of facial skin. We derived a semi-empirical equation modeling the lateral propagation characteristics of vertically incident IR light into human skin, with parameters which fit to the measured data. All components of IR emitters and receivers were integrated into the form interface of a commercial VR headset. We verified the functionality of tracking performance from 4-kind of basic facial expressions in the experiment using the implemented prototype headset. As a further work, we will upgrade the performance of proposed sensor to recognize emotional expressions of HMD user by applying machine learning technique. We are aiming to enable immersive human to human(or avatar) communications in the cyberspace.

We present a design and application of NFC (Near Field Communication) transceiver for the using an inductive powered receiver to enable all type of NFC communication modes such as the passive, active, RW, and RFID modes. NFC technology has been ready massively adopted in the automotive applications. We proposed a system architecture of NFC Automotive Applications with Trused Execution Envinonment to enforce protection, confidentiality, integrity and access rights of the resources and data.