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Utilization of multiple-input multiple-output (MIMO) systems as a means of increasing channel capacity has been an area of increasing consideration in radio communications. However, less study has been devoted to MIMO in the high-frequency band. This research is important because high-frequency communication using MIMO allows for international communication at long distances using lower power consumption than many other approaches. The inter-symbol interference caused by the selective fading of multiple received signals and the randomness of the ionospheric conditions means there is a need for a novel solution. The purpose of this research is to introduce two machine learning approaches that can adaptively apply equalization algorithms to address fading and optimize equalization parameters. The novelty of our approach lies in two main factors. The first is that our approach allows for a software-defined radio to switch equalization algorithms depending on conditions during run-time. The second is that we optimize this selected algorithm further by using two machine-learning approaches. The first proposed cognitive engine model, which utilizes a genetic algorithm, demonstrates the validity and advantage of using a cognitive engine to select optimal equalization parameters at the receiver under the problems created by utilizing the high-frequency band. This approach acts as a comparison for our second. We then propose a second cognitive engine, the adaptive manipulator, which optimizes not only by selecting equalization parameters but also continually changes the equalization algorithm. Finally, we compare the performance of the proposed cognitive engine models with state-of-the-art algorithms. 

While massive strides have been made in the field of search-based software testing (SBST) in recent years, there yet remains the problem of transitioning such techniques to reality. This paper discusses this problem in terms of cyber-physical systems, presents research challenges for applying SBST to this domain, and outlines the state-of-the-art achievements of the SBST community in this regard. 

The increased growth of the aging population (i.e., 65 years or older) has led to emerging technologies in health care that provide in-home support to patients using devices throughout the household. Such smart home environments can monitor and interact with patients and their doctors/caregivers to augment patient medical data for diagnosis than can be generated via traditional doctor visits. Moreover, smart homes are enabling older adults to stay at home longer as opposed to permanent moves to assisted living or nursing facilities, increasing health and well-being and decreasing overall costs to the individual and society at large. This paper proposes Cognitive Assisted Living (CAL), a cyber-physical system comprising a network of embedded devices for collecting and analyzing patient speech patterns over time for monitoring cognitive function beginning in the early stages of Alzheimer’s disease. Specifically, CAL will analyze patient speech patterns and spatial abilities, via a set of daily interactions, to provide a longitudinal analysis of speech deterioration, a significant indicator of cognitive decline resulting from Alzheimer’s disease. Understanding the rate of cognitive decline can enable caregivers and health care professionals to better manage the patient’s daily care and medical requirements. Additionally, the patient’s cognitive state can be shared across household devices to increase the patient’s comfort and better accommodate lifestyle changes. To these ends, we describe the architecture of the proposed system, the methods to which we will detect cognitive decline, and specify how the system will provide continuing fault tolerance and data security at run time. 

A self-adaptive system (SAS) can reconfigure at run time in response to adverse combinations of system and environmental conditions in order to continuously satisfy its requirements. Moreover, SASs are subject to cross-cutting non-functional requirements (NFRs), such as performance, security, and usability, that collectively characterize how functional requirements (FRs) are to be satisfied. In many cases, the trigger for adapting an SAS may be due to a violation of one or more NFRs. For a given NFR, different combinations of hierarchically-organized FRs may yield varying degrees of satisfaction (i.e., satisficement). This paper presents Providentia, a search-based technique to optimize NFR satisficement when subjected to various sources of uncertainty (e.g., environment, interactions between system elements, etc.). Providentia searches for optimal combinations of FRs that, when considered with different subgoal decompositions and/or differential weights, provide optimal satisficement of NFR objectives. Experimental results suggest that using an SAS goal model enhanced with search-based optimization significantly improves system performance when compared with manually and randomly-generated weights and subgoals.