Sajid Mohamed

Bio: Sajid Mohamed is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Control system & Control theory. The author has an hindex of 5, co-authored 13 publications receiving 44 citations. Previous affiliations of Sajid Mohamed include Indian Institutes of Technology.

Optimising Quality-of-Control for Data-Intensive Multiprocessor Image-Based Control Systems Considering Workload Variations

Abstract: Image-Based Control (IBC) systems have a long sample period. Sensing in these systems consists of compute-intensive image processing algorithms whose response times are dependent on image workload. IBC systems are typically designed for the worst-case workload that results in a long sample period and hence suboptimal quality-of-control (QoC). This worst-case based design is further considered for mapping of controller tasks and allocating platform resources, resulting in significant resource over-provisioning. Our design philosophy is to sample as fast as possible to optimise QoC for a given platform allocation, and for this, we present a structured design flow. Workload variations determine how fast we can sample and we model this dynamic behaviour using the concept of workload scenarios. Our choice of scenario-aware dataflow as the formal model for our application enables us to: i) model dynamic behaviour, analyse timing, and optimally map application tasks to the platform for maximising the effective utilisation of allocated resources, ii) relate throughput of the dataflow graph to the sample period, and thus combine dataflow analysis and mapping with control design parameters and QoC to identify system scenarios, and iii) to efficiently implement a run-time mechanism that manages necessary dynamic reconfiguration between system scenarios. Our results show that our design approach outperforms the worst-case based design with respect to optimising QoC and maximising effective resource utilisation.

Designing image-based control systems considering workload variations

Abstract: We consider the problem of designing an Image-Based Control (IBC) application mapped to a multiprocessor platform. Sensing in IBC consists of compute-intensive image processing algorithms whose execution times are dependent on image workload. The challenge is that the IBC systems have a high (worst-case) workload with significant workload variations. Designing controllers for such IBC systems typically consider the worst-case workload that results in a long sensing delay with suboptimal quality-of-control (QoC). The challenge is: how to improve the QoC of IBC for a given multiprocessor platform allocation?We present a controller synthesis method based on a Markovian jump linear system (MJLS) formulation considering workload variations. Our method assumes that system knowledge is available for modelling the workload variations as a Markov chain. We compare the MJLS-based method with two relevant control paradigms - LQR control considering worst-case workload, and switched linear control - with respect to QoC and available system knowledge. Our results show that taking into account workload variations in controller design benefits QoC. We then provide design guidelines on the control paradigm to choose for an IBC application given the requirements and the system knowledge.

IMACS: A Framework for Performance Evaluation of Image Approximation in a Closed-loop System

Abstract: Image Processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. Image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e.g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. We propose a framework - for both software-in-the-loop (SiL) and hardware-in-the-loop (HiL) simulation - for performance evaluation of image approximation on a closed-loop automotive IBC system (IMACS). Both simulation setups model the 3D environment in 3ds Max, and simulate the system dynamics, camera position and environment in V-REP. Our SiL setup simulates the system software in C++ or Matlab. Here, V-REP runs as a server and the software as a client in synchronous mode. Our HiL simulation setup runs the system software in the NVIDIA Drive PX2 platform and communicates to V-REP using application programming interfaces (APIs) for synchronous execution. We show the effectiveness of our framework using a vision-based lateral control example.

Approximation trade offs in an image-based control system

Abstract: Image-based control (IBC) systems use camera sensor(s) to perceive the environment The inherent compute-heavy nature of image processing causes long processing delay that negatively influences the performance of the IBC systems Our idea is to reduce the long delay using coarse-grained approximation of the image signal processing pipeline without affecting the functionality and performance of the IBC system The question is: how is the degree of approximation related to the closed-loop quality-of-control (QoC), memory utilization and energy consumption? We present a software-in-the-loop (SiL) evaluation framework for the above approximation-in-the-loop system We identify the error resilient stages and the corresponding coarse-grained approximation settings for the IBC system We perform trade off analysis between the QoC, memory utilisation and energy consumption for varying degrees of coarse-grained approximation We demonstrate the effectiveness of our approach using a concrete case study of a lane keeping assist system (LKAS) We obtain energy and memory reduction of upto 84% and 29% respectively, for 28% QoC improvements

Stability Theory Of Switched Dynamical Systems

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Introduction to electron microscopy

Abstract: Light microscopes were developed in the early 1600’s. By the end of the century Dutch microscopist van Leeuwenhoek had imaged blood cells, bacteria and structure within the cell using a simple one-lens microscope with x300 magnification which was awkward to use. At the same time the compound microscope was being developed. This uses at least two lenses; an objective, placed close to the specimen, and an eyepiece (ocular), placed close to the eye. Such was van Leeuwenhoek’s skill, however, that it was two centuries before the compound microscope could match his work. Compound optical microscopes, these days, have a maximum magnification of about x1000 and an imaging resolution of around 0.3μm for light in the middle of the visible region (λ~0.5μm). Aberrations in light optical lenses can be reduced to a minimum by grinding the lens surface to a correct shape or spacing the lenses so that their aberrations are compensated. In the absence of aberrations the limit of resolution (d) of any wave optical microscope is given by the Rayleigh criterion:-