Smart Mirror Using Raspberry PI: A Review

A Smart mirror is a mirror with technology integrated with it. It uses a two-way mirror and has an inbuilt display at the back showing us different information in the form of widgets about the date, time, temperature, daily news updates. The Raspberry Pi acts as the central controller, which powers the display and collects data through sensors. The data collected is stored on cloud servers for further use. The mirror comes with facial recognition technology, which helps authenticate the user every time the user comes in the mirror range. With the help of voice commands, the mirror application can be queried to get the desired data. This automation has helped in multitasking which strives to optimize time in our daily life. In this manuscript we will review different applications of smart mirror.

Equivalent Sliding Mode Controller for Stability of DC Microgrid

DC microgrids are gaining popularity due to their lack of reactive power compensation, frequency synchronization, and skin effect problems. However, DC microgrids are not exempted from stability issues. The stability of DC microgrids based on decentralized architecture is presented in this paper. Centralized architecture can degrade system performance and reliability due to the failure of a single central controller. Droop with proportional integral (PI) controller based on decentralized architecture is being used for DC microgrid stability. However, droop control requires a tradeoff between voltage regulation and droop gain. Further, global stability through PI controller cannot be verified and controller parameters cannot be optimized with different operating conditions. To address limitations, an equivalent sliding mode (SM) controller is proposed for a DC microgrid system in this paper. Detailed simulations are carried out, and results are presented, which show the effectiveness of an equivalent SM controller.

Wireless HART stack using multiprocessor technique with laxity algorithm

The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.

A Multichannel Allocation Strategy Based on Preemption Threshold and Preemption Probability in Cognitive Radio Networks

In traditional multichannel cognitive radio networks (CRNs), users are split into two different priorities. Because of the low priority of secondary users (SUs), SU packets’ transmissions are easily interrupted by primary users (PUs). In this paper, two control parameters, called preemption threshold H and preemption probability q, are used to regulate the preemption behavior of PU packets to improve the transmission performance of SU packets. When all channels in the system are occupied, the preemption behavior of PU packets will be adjusted according to the amount of SU packets that are transmitting in the system. If the amount is larger than H, the recently arrived PU packet either preempts a channel with probability q or leaves the system with probability 1 − q . The central controller manages the system’s channel usage right and determines a series of access behaviors of user packets. Considering the possible imperfect sensing, a discrete-time queueing model is developed with the proposed preemption control mechanism. Then we obtain some performance index expressions of PU and SU packets founded on the system’s state transition matrix and make the corresponding performance figures through numerical experiment. Finally, we construct a system utility function and determine the optimal preemption threshold and preemption probability through the seagull optimization algorithm (SOA). Experimental data show that the proposed mechanism by setting preemption threshold and preemption probability can significantly reduce SU packets’ outage rate and improve SU packets’ throughput rate.

Diffusion Model of Preemptive-Resume Priority Systems and Its Application to Performance Evaluation of SDN Switches

The increasing use of Software-Defined Networks brings the need for their performance analysis and detailed analytical and numerical models of them. The primary element of such research is a model of a SDN switch. This model should take into account non-Poisson traffic and general distributions of service times. Because of frequent changes in SDN flows, it should also analyze transient states of the queues. The method of diffusion approximation can meet these requirements. We present here a diffusion approximation of priority queues and apply it to build a more detailed model of SDN switch where packets returned by the central controller have higher priority than other packets.

Strategies for an Adaptive Control System to Improve Power Grid Resilience with Smart Buildings

Low-voltage distribution grids face new challenges through the expansion of decentralized,renewable energy generation and the electrification of the heat and mobility sectors. We present amulti-agent system consisting of the energy management systems of smart buildings, a central gridcontroller, and the local controller of a transformer. It can coordinate the provision of ancillary servicesfor the local grid in a centralized way, coordinated by the central controller, and in a decentralizedway, where each building makes independent control decisions based on locally measurable data.The presented system and the different control strategies provide the foundation for a fully adaptivegrid control system we plan to implement in the future, which does not only provide resilienceagainst electricity outages but also against communication failures by appropriate switching ofstrategies. The decentralized strategy, meant to be used during communication failures, could alsobe used exclusively if communication infrastructure is generally unavailable. The strategies areevaluated in a simulated scenario designed to represent the most extreme load conditions that mightoccur in low-voltage grids in the future. In the tested scenario, they can substantially reduce voltagerange deviations, transformer temperatures, and line congestions.

Controlled Switching Diffusions Under Ambiguity: The Average Criterion

This paper concerns controlled switching diffusions. In particular, we consider that the drift coefficient of the diffusion process depends on an unknown (and possibly nonobservable) parameter. For giving solution to our control problem, we formulate it as a game against nature, where the ambiguity is represented by nature that chooses values of the unknown parameter through actions so playing the role as an opposite player of the controller. Our objective is to give conditions to characterize the ergodic optimality and guarantee the existence of optiaml policies for the central controller. Finally, we provide two examples to illustrate our results.

Information Aggregation for Constrained Online Control

This paper considers an online control problem involving two controllers. A central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller's goal is to minimize the cumulative cost; however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.