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To integrate third-party intellectual properties (IPs) into a new system-on-chip (SoC) architecture is a big challenge. Therefore, this study first presents a new bus protocol named as integrated bus (IBUS), and more important, a configurable bus wrapper for connecting AXI3-interfaced IPs into IBUS is further proposed, aiming to finding the optimal balance between bus efficiency and resource cost in terms of field-programming gate array slice count, bus transfer latency, and energy consumption. As a case study, the authors implemented three IBUS wrappers for integrating three AXI3-interfaced verification IPs into an IBUS SoC. Experimental results show that their proposed work achieves a higher valid data throughput ( 1.35 × in the block test and 1.52 × in the cipher test) compared with the designs on conventional bridge-based SoC integration, as well as a large reduction in the normalised slice-time-power (18.73% in the block benchmark and 23.45% in the cipher benchmark) when setting the same weights of slice number, data transfer latency, and energy dissipation.

Underground utility pipes especially sewer pipes are prone to develop cracks due to aging, shifting soil, increased traffic, corrosion, and improper installation. A major challenge for utility operators is cost effective periodic condition monitoring of their sewer networks. The existing industry standard pipe condition monitoring system is based on passing a robot mounted closed circuit television (CCTV) camera through the pipe. The CCTV video feed is recorded and monitored by a trained operator who annotates it corresponding to the location of cracks and other structural imperfections. This system is both cost and labor intensive. In recent years, the deep learning-based systems have achieved success in vision based object detection problems. In this project, we have collected pipe crack data from extensive field trials with CCTV based systems in actual sewer networks. The noisy field data is cleaned up and used for training the convolutional neural networks. We test the proposed model with validation data to determine its accuracy and effectiveness. The results indicate that deep learning model can be effectively used to ]detect cracks in underground pipes.

A critical modern-day challenge for utility operators is condition monitoring of underground sewer infrastructure. Existing industry standard for underground sewer line inspection is based on sending a wire-guided robot with a closed circuit television (CCTV) camera through a pipe. A trained operator observes the video feed from the camera, and annotates it to record defects such as cracks, sags, offsets, root infiltrations, grease build up, and lateral protrusions. The success of a CCTV based robot system depends on visual observation and alertness of the operator. There is a likelihood that the operator fatigue and distraction may lead to missed observations. The CCTV based systems are expensive and man-hour intensive. We propose a deep learning based method to make the defect detection process automated without the need for an onsite operator to visually observe the video. The system is based on passing an autonomous camera-mounted robot through the pipe. The recorded video is analyzed using deep learning based algorithms. Our initial focus is to detect presence of cracks in polyvinyl chloride pipes, which are industry standard for sewer installations. We propose a deep learning framework including network architecture to detect presence or absence of a crack in a pipe sample. We also collect empirical data using an autonomous robot during laboratory trials to validate our approach. The data analysis indicates an accuracy of 89.42% in training and 83.3 % in validation. Further data collection and analysis is currently in progress and results will be reported in future.

Dangerous situations such as children are left in vehicles, are dropped off at wrong stops, or take on wrong school buses usually caused by the negligence of drivers. This paper presents a real-time intelligent in-vehicle monitoring system that can count and recognize people as well as alert drivers if such improprieties or potential dangers happen. The system uses HOG-based face detector from Dlib library to obtain face counting function. Face recognition is achieved through two steps, facial feature extraction and face identification. The ResNet is used in facial feature extraction. It transforms an aligned face into a 256-dimensional vector, a Euclidean facial embedding. In face identification, labeled faces will be transformed to facial embeddings first. Then k-nearest neighbor classifier (kNN) is adopted to identify people using such facial embeddings. The simulation on ChokePoint dataset is tested and the average accuracy is 93 percent.

Thread is an IPV6 based networking protocol for Internet-of-Things (IoT) that uses 6LoWPAN, IEEE 802.15.4 wireless at 2450MHz. OpenThread is an open source implementation of Thread Protocol released by Nest Labs that supports the Thread 1.1.1 specifications and currently used on Linux using command-line interface. This research focuses on building a Graphical User Interface (GUI) using TCL/TK for OpenThread. GUI helps researchers to manage and visualize Thread Protocol and its operations easier.

This paper presents an Internet-of-Things (IoT) architecture by integrating a Synology cloud server, edge computing systems, and physical networks. More specifically, we have established a physical network combining two subsystems 1) non-standardized Bluetooth Low Energy (BLE) Mesh network, and 2) a security monitoring system. The BLE Mesh system has one IoT host connected with three BLE devices, enabling to extend the communication distance by using one or two relays. The monitoring system consists of a Passive Infrared Sensor (PIR) and a webcam with multiple solutions for recognizing a human face. Two algorithms, Low Binary Pattern Histogram (LBPH) and Deep Metric Learning (DML), have been implemented and evaluated on different benchmarks. Experimental results show that the DML-based computation can reach 99.38% accuracy with almost 400 ms latency for recognizing a single face in frames of images. The future work will focus on testing the cloud service by integrating a Synology D218+ server, as well as improving the computation speed of facial recognition on pure hardware design on field-programmable gate array (FPGA). The aim of our work is to provide a robust IoT-Edge-Cloud system which can be deployed on the large-scale applications and processes data much faster compared to traditional cloud computing system due to the perks of parallel computing on FPGAs at the network edge.

This paper presents a synthesis of well-known Viola-Jones face detection algorithm on Xilinx software and platform - Vivado and field programmable gate array (FPGA) as Nexys 4 Artix-7 device. Compared with the prior work on the Altera platform proposed in [1], our work reduces the slice count by 1018. And additionally, the power consumption of the implementation is 714 mW, including 15% as the static cost and 85% as the dynamic power dissipation.Furthermore, the design details of the components of the structure, such as generation of integral image, multiple pipelined classifiers, as well as the parallel processing, are discussed in this work, in order to provide a potential improvement for the future work. This paper not only provides successful synthesis of a face detection system but also ignites intriguing ideas in terms of improvement aspects, such as approximating the design for finding an optimal energy-quality tradeoff corresponding to different applications as our future work.

This paper reviews the security aspects of two Internet-of-Things (IoT) protocols, Z-Wave and Thread. Z-Wave is one of the oldest and most commercially successful IoT protocol, while Thread is one of the most recent protocols. As millions of IoT systems are installed for home and industrial automation, the security of these IoT systems is a concern. There is a potential for these IoT systems to be misused. This paper looks at security challenges for an IoT system. The security features of the IoT systems are spread across many parts of the IoT protocols. Paper discusses different attacks types on the IoT systems and the manners in which the protocols handle them. One of the most venerable times for an IoT system is when a new device is added to the IoT system. To avoid an intruder from getting access, the new device must be authenticated. Authentication of new network devices is discussed for both the protocols. IoT protocols are complex and the security aspects are also very complex, this paper should serve as a starting point to further study these and other IoT protocols.

Thread is a new IoT protocol released by Thread Group Inc. in 2015. More than 200 companies are currently members of the Thread Group Inc. and supporting this protocol. This paper provides an overview of the Thread protocol and discusses all the network layers. In the physical layer's overview we discuss the requirements for wireless communication. In the data link (MAC) layer we discuss the MAC link establishment (MLE) datagrams. We discuss use of 6LoWPAN addressing mode, which is a low power version of IPv6. In the network layer we discuss communication links and routing. In the transport layer we discuss the use of CoAP protocol and DTLS. In the security section we discuss setting up Commissioning agent and adding Thread devices to the Private Area Network.

This paper proposes the design of a facial expression recognition system based on the deep convolutional neural network by using facial parts. In this work, a solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method builds a two-channel convolutional neural network model in which facial parts are used as inputs: the extracted eyes are used as inputs to the first channel, while the mouth is the input into the second channel. Feature information from both channels converges in a fully connected layer which is used to learn global information from these local features and is then used for expression classification. Experiments are carried out on the Japanese female facial expression dataset and the extended Cohn-Kanada dataset to determine the expression recognition accuracy for the proposed facial expression recognition system based on facial parts. The results achieved shows that the system provides state of art classification accuracy with 97.6% and 95.7% respectively when compared to other methods.

Recently, rehabilitation treadmills are designed for helping injured persons such as stroke patients and injury athletes in the process of physical therapy. By monitoring the changes of paces and gaits, one can estimate the progress of rehabilitation. At present, most devices that can estimate paces and gaits are wearable and/or expensive. This paper presents an inexpensive, non-intrusive wireless binary sensor system for pace estimation and lower-extreme gait recognition with low data throughput and high energy efficiency. The asymmetric but periodic movement of injured person allows the study of pace and gait. The pace estimation is achieved by using autocorrelation function. The gait information is represented by three features (1) temporal correlation, (2) marginal density (intersection probability), and (3) spatial correlation from binary data steam. Experimental results shows that our system can estimate the pace of walking or running with the accuracy of 97.7%. By using only three features, abnormal gaits can also be recognized.