Intelligent Cognitive Radio Architecture Applying Machine Learning and Reconfigurability

This paper presents a cognitive radio architecture incorporating machine learning into its cognition engine. Both the cognition engine and the software-defined transmitter and receiver chains make use of reconfigurable technologies to enable adaptation to the radio operating environment.acceptedVersionPeer reviewe

Composing Optimized Embedded Software Architectures for Physics-Based EKF-MPC Smart Sensor for Li-Ion Battery Cell Management

Efficient battery technology is imperative for the adoption of clean energy automotive solutions. In addition, efficient battery technology extends the useful life of the battery as well as provides improved performance to fossil fuel technology. Model predictive control (MPC) is an effective way to operate battery management systems (BMS) at their maximum capability, while maintaining the safety requirements. Using the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery. Since these processes are internal to the battery and are physically unobservable, the extended Kalman filter (EKF) serves as a virtual observer that can monitor the physical and chemical properties that are otherwise unobservable. These three methods (i.e., PBM, EKF, and MPC) together can prolong the useful life of the battery, especially for Li-ion batteries. This capability is not limited to the automotive industry: any real-world smart application can benefit from a portable/mobile efficient BMS, compelling these systems to be executed on resource-constrained embedded devices. Furthermore, the intrinsic adaptive control process of the PBM is uniquely suited for smart systems and smart technology. However, the sheer computational complexity of PBM for MPC and EKF prevents it from being realized on highly constrained embedded devices. In this research work, we introduce a novel, unique, and efficient embedded software architecture for a PB-EKF-MPC smart sensor for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded software architecture is created in such a way to be executed on a 32-bit embedded microprocessor running at 100 MHz with a limited memory of 128 KB, and still obtains an average execution time of 4.8 ms

Toward Composing Efficient FPGA-Based Hardware Accelerators for Physics-Based Model Predictive Control Smart Sensor for HEV Battery Cell Management

In the era of climate change, and with the rapid depletion of fossil resources, efficient and sustainable transportation systems, such as hybrid electric vehicles (HEVs), are becoming imperative. The cornerstone of HEVs is the efficient battery technology, in order to extend the useful life of the battery and to provide improved performance to fossil fuel technology. Model predictive control (MPC) is an effective technique for battery management systems (BMS), which enables cell-level monitoring and controlling of the battery pack, reducing the safety margin of operation, while maintaining the safety requirements. Furthermore, utilizing the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery that are the root cause of battery degradation. The non-linear extended Kalman filter (EKF) serves as the state observer to monitor the physical and chemical properties/processes of each battery cell, since these are internal processes of the battery, making them physically unobservable. The amalgamation of the aforementioned techniques, i.e., MPC, PBM, and EKF, can extend the useful life of the battery cell/pack, especially for lithium-ion batteries. In real-world scenarios, HEVs and smart applications often require portable BMS, compelling BMS to be executed on highly constrained embedded devices. Also, the inherent adaptive control process of physics-based (PB) MPC is uniquely suited for smart systems/applications. However, the high computational complexity of PB-MPC, comprising PB-EKF, prevents it from being realized on resource-constrained embedded devices, making it infeasible for portable BMS. In this research work, we introduce novel, unique, and efficient FPGA-based embedded hardware accelerator for PB-MPC smart sensor (comprising PB-EKF) for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded PB-MPC hardware accelerator achieved 58 times speedup compared to its embedded software counterpart, while maintain a small footprint required for portable systems. This speedup enables us to manage more battery cells utilizing a single chip compared to that of embedded processor-based solutions

Composing Efficient Computational Models for Real-Time Processing on Next-Generation Edge-Computing Platforms

In the era of IoT and smart systems, an enormous amount of data will be generated from various IoT/smart devices in smart homes, smart cars, etc. Typically, this big data is collected and sent directly to the cloud infrastructure for processing, analyzing, and storing. However, traditional cloud infrastructure faces serious challenges when handling this massive amount of data, including insufficient bandwidth, high latency, unsatisfactory real-time response, high power consumption, and privacy protection issues. The edge-centric computing is emerging as a complementary solution to address the aforementioned issues of the cloud infrastructure. Furthermore, for many real-world IoT and smart systems, such as smart cars, real-time, in situ, and online data analysis and processing are crucial. With edge computing, data processing and analysis can be done closer to the source of the data (i.e., at the edge of the networks), which in turn enables real-time and in-situ data analytics and processing. As a result, edge computing will soon become the cornerstone of many IoT and smart applications. However, edge computing is still in its infancy; thus, requires novel models and techniques to support real-time and in-situ data processing and analysis. In this research work, we introduce novel and efficient computation models that are suitable for real-time processing and analysis on next-generation edge-computing platforms. Since most common edge-computing tasks are data analytics/mining, we focus on widely used data analytics techniques, including dimensionality reduction and classification techniques, specifically, principal component analysis (PCA) and support vectors machine (SVM), respectively. This is mainly because it is demonstrated that combination of PCA and SVM leads to high classification accuracy in many fields. In this paper, we introduce three different PCA+SVM models (i.e., Model 1, Model 2, and Model 3), for real-time processing and analysis (for online training and inference) on edge computing platforms. Model 1 and Model 2 are created utilizing the same SVM algorithm but with a different design/functional flows, whereas Model 3 is created with the same functional flow as Model 2 but utilizing a modified SVM algorithm. Our experimental results and analysis demonstrate that Model 3 utilizes dramatically lower number of iterations to produce the results, compared to that of other two models, while achieving acceptable performance results. Our results and analysis demonstrate that Model 3 is the most suitable computation model for real-time processing and analysis of edge computing platform