In an era marked by the proliferation of interconnected devices and the boundless possibilities of IoT, Macro IoT Solution stands at the forefront of innovation. This article unveils a compelling MATLAB project crafted by Macro IoT Solution, showcasing the convergence of IoT technology and MATLAB’s robust computational capabilities.
With a keen focus on addressing contemporary challenges and optimizing processes, this project exemplifies the transformative potential of integrating advanced analytics with IoT infrastructure. Through a detailed exploration of its components, methodologies, and outcomes, we embark on a journey to unravel the intricate layers of this groundbreaking project.
Outlier Detection for Sensor Systems (ODSS): A MATLAB Macro for Evaluating Microphone Sensor Data Quality:
Microphone sensor systems provide information that may be used for a variety of applications. Such systems generate large amounts of data. One concern is with microphone failure and unusual values that may be generated as part of the information collection process. This paper describes methods and a MATLAB graphical interface that provides rapid evaluation of microphone performance and identiﬁes irregularities. The approach and interface are described. An application to a microphone array used in a wind tunnel is used to illustrate the methodology.
Modern wind tunnel testing involves multiple diverse sensor systems with anywhere from hundreds to thousands of individual sensors. These large-scale tests can be quite expensive and are usually time-sensitive. As such, any delays due to faulty instrumentation can have serious consequences. Equally serious is the possibility of discovering a sensor failure after the test has been completed because time and effort will have been spent collecting useless information. Although sometimes correctable, the time involved in doing so distracts the experimenter from achieving the experimental goals.
Common failures with microphones include complete failure in which the microphone turns off resulting in a ﬂat signal with low variability, drift of the signal and groups of signals that are different from regular signals. In addition, some microphones exhibit over-range and clipping. Over-range is deﬁned here as having values that exceed normal ranges for the microphone. Clipping occurs when a value, usually at the upper-most or lower-most value, occurs multiple times.
The data set used in this analysis is viewed as consisting of measurements on K microphones or sensors. Each microphone is assumed to have the same starting and ending value and the same number of measurements. The measurements are indexed by a time sequence. Thus, we take xij to be a measurement from microphone i, i = i, 2, K at time j, j = 1, 2, T.
Simulation Result and Sensitivity Measure:
One critical decision in outlier and anomaly detection is the numerical criterion for deciding if an observation is sufﬁciently extreme to be deemed an outlier or potential outlier. While the actual determination might involve other factors and checks on the microphone, the determination of the cutoff is critical as too small a value will result in numerous false signals and too high a value in numerous missed outliers. To evaluate the method and estimate potential error rates, a simulation is used. Data were generated to mimic the microphone data in terms of the number of data points and segment size.
Evaluation of Microphone Array Data:
This section describes an experiment that will be used as an example. The experiment is part of a study that focuses on the acoustic effects of a discontinuity or step in an otherwise smooth surface. For example, the surface of a ship may be mostly smooth except for discontinuities in the hull where plates are joined. Such steps may be acoustically loud and interest lies in the study of the effect of steps on acoustic characteristics of ﬂow and the design of efﬁcient low-noise vehicles.
Profile and Instrumentation – Driven Methods for Embedded Signal Processing:
Modern embedded systems for digital signal processing (DSP) run increasingly sophisticated applications that require expansive performance resources, while simultaneously requiring better power utilization to prolong battery-life. Achieving such conflicting objectives requires innovative software/hardware design space exploration spanning a wide-array of techniques and technologies that offer trade-offs among performance, cost, power utilization, and overall system design complexity.
To save on non-recurring engineering (NRE) costs and in order to meet shorter time-to-market requirements, designers are increasingly using an iterative design cycle and adopting model-based computer-aided design (CAD) tools to facilitate analysis, debugging, profiling, and design optimization. In this dissertation, we present several profile and instrumentation-based techniques that facilitate design and maintenance of embedded signal processing systems:
We propose and develop a novel, translation look aside buffer (TLB) preloading technique. This technique, called context-aware TLB preloading (CTP), uses a synergistic relationship between the compiler for application speciﬁc analysis of a task’s context, and operating system (OS), for run-time introspection of the context and efﬁcient identiﬁcation of TLB entries for current and future us age.
CTP works by identifying application hotspots using compiler-enabled (or manual) proﬁling, and exploiting well-understood memory access patterns, typical in signal processing applications, to preload the TLB at context switch time. The beneﬁts of CTP in eliminating inter-task TLB interference and preemptively allocating TLB entries during context-switch are demonstrated through extensive experimental results with signal processing kernels.
We develop an instrumentation-driven approach to facilitate the conversion of legacy systems, not designed as dataﬂow-based applications, to dataﬂow semantics by automatically identifying the behavior of the core actors as instances of well-known dataﬂow models. This enables the application of powerful dataﬂow-based analysis and optimization methods to systems to which these methods have previously been unavailable.
We introduce a generic method for instrumenting data ﬂowgraphs that can be used to proﬁle and analyze actors, and we use this instrumentation facility to instrument legacy designs being converted and then automatically detect the dataﬂow models of the core functions. We also present an iterative actor partitioning process that can be used to partition complex actors into simpler entities that are more prone to analysis. We demonstrate the utility of our proposed new instrumentation-driven dataﬂow approach with several DSP-based case studies.
We extend the instrumentation technique discussed in to introduce a novel tool for model-based design validation called dataﬂow validation framework (DVF):
DVF addresses the problem of ensuring consistency between (1) dataﬂow properties that are declared or otherwise assumed as part of dataﬂow-based application models, and (2) the dataﬂow behavior that is exhibited by implementations that are derived from the models. The ability of DVF to identify disparities between an application’s formal dataﬂow representation and its implementation is demonstrated through several signal processing application development case studies.
- Embedded Projects,
- MATLAB Projects
Systems Engineering-based Model Development: Application to Predictive Simulation of a Net-zero Home:
Building design has grown increasingly sophisticated throughout the decades. In recent years, assessments of building performance and sustainability has grown in popularity as the U.S. Green Building Council published LEED certiﬁcations for new and existing constructs. The LEED rating system utilizes standards made by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) for areas in thermal comfort, air quality, energy building performance, and heat, ventilation, and air conditioning (HVAC) operation.
Energy building performance has a more overarching role in this rating as the other three standards play into the overall loads associated with any building. Submittal of energy performance building reports for construction design and green building rating systems is becoming more common as building performance assessment software becomes more widely available.
The University of Maryland currently is a participant in the Solar Decathlon intercollegiate competition sponsored by the Department of Energy. The University of Maryland’s react team is working to construct a net-zero solar powered house for judging in Denver, CO in October 2017.Concurrently with the housing design, a substantial eﬀort was put into assessing the projected building performance to aid in the design process and to set the stage for model-based home automation.
While software such as Open Studio and BEOPT are available and were used for year-averaged performance reports, a physically based model of the house was built from scratch to serve as a real-time simulation of virtual versions of react located in College Park, MD and Denver, CO and is described in detail as the Virtual House. The overall system design of the Virtual House can be described as a general set of inputs, dynamic simulation, and output of overall proﬁles.
Inputs for the system include geometric design of the house, speciﬁed materials, schedules, daily weather data, and solar irradiance. Dynamic simulation refers to a simultaneous integration of both independent and dependent ﬂuctuating loads upon the time of day regarding both heat and power balances. Finally, outputs showcase heat and power proﬁles throughout a day. The bulk of analysis of inputs and simulation has been rooted in fundamental calculations.
In terms of future work, outputs coming from the Virtual House are currently being stored and are now looking towards validation with measured sensor data. As of now react is not in construction phase and measured data is unavailable. In order to validate the Virtual House, there are current plans to outﬁt the previous Solar Decathlon 2007 entry LEAF house with sensors. With this, measured and simulated data can be assessed after modifying the current Virtual House model for LEAF house speciﬁc inputs.
As we conclude our exploration of the MATLAB project by Macro IoT Solution and Engineering Services, we are left awe-inspired by the ingenuity and efficacy of their approach. From sensor data acquisition to real-time analytics and decision-making, the seamless integration of MATLAB has elevated the capabilities of IoT solutions to unprecedented heights. This project serves as a testament to the power of innovation and collaboration in driving tangible advancements across diverse domains. Moving forward, it paves the way for continued exploration and refinement, promising a future where IoT-driven insights reshape industries and enhance lives.