A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

This work presents a fully-programmable Internet of Things (IoT) visual sensing node that targets sub-mW power consumption in always-on monitoring scenarios. The system features a spatial-contrast $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ at 10 fps), provides as output the number of changed pixels. Based on this information, a dedicated camera interface, implemented on a low-power FPGA, wakes up an ultra-low-power parallel processing unit to extract context-aware visual information. We evaluate the smart sensor on three always-on visual triggering application scenarios. Triggering accuracy comparable to RGB image sensors is achieved at nominal lighting conditions, while consuming an average power between $193\mu W$ and $277\mu W$, depending on context activity. The digital sub-system is extremely flexible, thanks to a fully-programmable digital signal processing engine, but still achieves 19x lower power consumption compared to MCU-based cameras with significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

Car parking management system

In the contemporary world, a prevalent challenge we confront is the rapid growth of the global population. This demographic expansion has given rise to several consequential problems, including the proliferation of automobiles on our roadways. The surge in vehicle numbers has led to congested traffic conditions and a scarcity of available parking spots, consequently fostering the problem of unauthorized parking. This unauthorized parking, in turn, poses a significant threat to the security of vehicles. In this area of high automobile traffic, the main issue arises when there is no management in the parking of automobiles. Due to this, there are high chances of accidents. To accomplish this task, we implemented a car parking system which is really very reliable and decreases the chance of risk in parking the vehicles. We proposed a car parking system which will calculate the empty slots available in the given parking place. Here, we have taken an ideal case of having 32 slots available in the parking place. We implemented and synthesized the project on the XILINX VIVADO platform using Verilog HDL. Hardware prototyping is done on Nexys a7 FPGA board

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Development and Implementation of a Smart Parking Spot Allocation System Based on the User’s Category and Priority using Verilog HDL

Finding parking spots for automobiles is a major issue in many large and congested cities. Usually, drivers lose time searching for parking spots, especially during peak hours, which increases traffic congestion and makes drivers frustrated and annoyed. Large building parking areas could also become dangerous to women, pregnant women, and the elderly, as several criminal cases in the parking area, were reported. In this project, a prototype of a smart parking spot allocation system based on the user’s category and priority was developed. The choice of user categories is people with disabilities (OKU), pregnant women/elderly, women, and normal users. The highest priority is assigned to OKU, followed by pregnant women/elderly, followed by women and the lowest priority is assigned to normal users. The parking spots for the highest priority category are placed near building entrances such as mall entrances. The controller for the automatic parking spot allocation system was developed using Verilog HDL code and the prototype was implemented on FPGA DE2-115. The controller is programmed to process the user’s category which is selected by the user at the second entrance and assign a specific parking spot number according to the category’s priority. The prototype was tested with multiple parking spots condition with different user inputs for different user categories. The system was able to allocate parking spots based on the user’s category depending on the parking spot available for the selected category with 75% out of 12 tests correct. However, all 12 tests, or 100% recorded accurate allocation based on the expected output of the system design. In a conclusion, this proposed system would be able to cater to the issue of finding parking spots hence directly avoiding traffic congestion and frustration among users. In addition, this system can indirectly reduce crime cases in the parking area due to parking spaces that prioritize categories of users needing to be parked near the entrance

SysMART Indoor Services: A System of Smart and Connected Supermarkets

Smart gadgets are being embedded almost in every aspect of our lives. From smart cities to smart watches, modern industries are increasingly supporting the Internet of Things (IoT). SysMART aims at making supermarkets smart, productive, and with a touch of modern lifestyle. While similar implementations to improve the shopping experience exists, they tend mainly to replace the shopping activity at the store with online shopping. Although online shopping reduces time and effort, it deprives customers from enjoying the experience. SysMART relies on cutting-edge devices and technology to simplify and reduce the time required during grocery shopping inside the supermarket. In addition, the system monitors and maintains perishable products in good condition suitable for human consumption. SysMART is built using state-of-the-art technologies that support rapid prototyping and precision data acquisition. The selected development environment is LabVIEW with its world-class interfacing libraries. The paper comprises a detailed system description, development strategy, interface design, software engineering, and a thorough analysis and evaluation.Comment: 7 pages, 11 figur

Real-time multi-camera video acquisition and processing platform for ADAS

The paper presents the design of a real-time and low-cost embedded system for image acquisition and processing in Advanced Driver Assisted Systems (ADAS). The system adopts a multi-camera architecture to provide a panoramic view of the objects surrounding the vehicle. Fish-eye lenses are used to achieve a large Field of View (FOV). Since they introduce radial distortion of the images projected on the sensors, a real-time algorithm for their correction is also implemented in a pre-processor. An FPGA-based hardware implementation, re-using IP macrocells for several ADAS algorithms, allows for real-time processing of input streams from VGA automotive CMOS cameras

Wireless vehicular communications for automatic incident detection and recovery

Incident detection is the process by which an incident is brought to the attention of traffic operators in order to design and activate a response plan. To minimize the detection time is crucial to mitigate the incident severity for victims as well to reduce the risk of secondary crashes. Automated incident information dissemination and traffic conditions is useful to alert in-route drivers to decide alternative routes on unexpected traffic congestion and may be also used for the incident recovery process, namely to optimize the response plan including the “nearest” rescue teams, thereby shortening their response times. Wireless vehicular communications, notably the emergent IEEE 802.11p protocol, is the enabling technology providing timely, dependable and secure properties that are essential for the devised target application. However, there are still some open issues with vehicular communications that require further research efforts. This paper presents an overview of the state of the art in wireless vehicular communications and describes the field operational tests proposed within the scope of the upcoming FP7 project ICSI - Intelligent Cooperative Sensing for Improved traffic efficiency