Assessment of Intelligence Complexity in Embedded Intelligent Real Time Systems

Intelligent systems and their applications are proliferating. Embedded Intelligent Real-Time Systems (EIRTS) are one type of intelligent system. Defining and measuring the complexity of this kind of system may help with better design, development, maintenance, and performance of EIRTS. In this paper, we propose a set of evaluation criteria to measure the complexity of Embedded Intelligent Real-Time Systems (EIRTS). We show an operationalization of the criteria with a sample EIRTS

Continuous glucose monitoring sensors: Past, present and future algorithmic challenges

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones

Channel management for WATM mobile satellite systems

This paper presents a dynamic channel allocation scheme for wireless asynchronous transfer mode (WATM) satellite systems. Handoff schemes of WATM are utilized to deal with handoff issues in a WATM mobile satellite system. Here we investigate and simulate a dynamic channel allocation scheme for handoff in WATM mobile satellite networks, which improves the network resource utilization, by measuring the network performance in terms of new call blocking probability (NCBP) and handoff call blocking probability (HCBP). Arriving calls are given channels based on their priority and calls are subdivided into real time and non real time calls. Highest priority is given to real time handoff calls, followed by non real time handoff calls, then real time new calls and finally non real time new calls

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction

Real-time motor rotation frequency detection with event-based visual and spike-based auditory AER sensory integration for FPGA

Multisensory integration is commonly used in various robotic areas to collect more environmental information using different and complementary types of sensors. Neuromorphic engineers mimics biological systems behavior to improve systems performance in solving engineering problems with low power consumption. This work presents a neuromorphic sensory integration scenario for measuring the rotation frequency of a motor using an AER DVS128 retina chip (Dynamic Vision Sensor) and a stereo auditory system on a FPGA completely event-based. Both of them transmit information with Address-Event-Representation (AER). This integration system uses a new AER monitor hardware interface, based on a Spartan-6 FPGA that allows two operational modes: real-time (up to 5 Mevps through USB2.0) and data logger mode (up to 20Mevps for 33.5Mev stored in onboard DDR RAM). The sensory integration allows reducing prediction error of the rotation speed of the motor since audio processing offers a concrete range of rpm, while DVS can be much more accurate.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0

Assessing collaborative and experiential learning

Collaborative and experiential learning has many proven merits. Team projects with real clients motivate students to put in the time for successfully completing demanding projects. However, assessing student performance where individual student contributions are separated from the collective contribution of the team as a whole is not a straightforward, simple task. Assessment data from multiple sources, including students as assessors of their own work and peers\u27 work, is critical to measuring certain student learning outcomes, such as responsible team work and timely communication. In this paper we present our experience with assessing collaborative and experiential learning in five Computer Information Systems courses. The courses were scheduled over three semesters and enrolled 57 students. Student performance and student feedback data were used to evaluate and refine our assessment methodology. We argue that assessment data analysis improved our understanding of (1) the assessment measures that support more closely targeted learning outcomes and (2) how those measures should be implemented

An Experimental Methodology to Evaluate Concept Generation Procedures Based on Quantitative Lifecycle Performance

This study presents an experimental methodology to measure how concept generation procedures can affect the anticipated lifecycle performance of engineering systems design concepts. The methodology is based on objective and quantitative measurements of anticipated lifecycle performance of the design concepts. It merges cognitive and computer-aided techniques from the fields of collaboration engineering, creativity, and engineering design. It complements the body of existing techniques relying on subjective expert assessments, and other objective metrics not explicitly measuring anticipated lifecycle performance (e.g. development time and cost). Application of the methodology is demonstrated through evaluation of design procedures generating flexibility in engineering systems design. Experiments had ninety participants generate creative design alternatives to a simplified real estate development design problem. Thirty-two teams of two to three participants performed the collaborative design exercise. An online Group-Support System interface enabled efficient data collection and analysis. A computationally efficient mid-fidelity model was used to evaluate flexible design concepts quantitatively based on real options analysis techniques.Massachusetts Institute of Technology. Center for Real EstateNatural Sciences and Engineering Research Council of CanadaMassachusetts Institute of Technology. Engineering Systems DivisionSingapore University of Technology and Design. International Design Cente