Investigation of the speed-up of a dual microcontroller parallel processing system in the execution of a mathematical operation

An investigation of the performance of a two microcontroller parallel processing system is presented. A twomicrocontroller parallel processing is developed using low end microcontrollers (PIC 16F877). An 8x8 bit multiply operation and a 16x16 bit multiply operation are executed on a single microcontroller and on the proposed dual microcontroller parallel processing system in order to assess the performance of the proposed system. Results presented show poor performance for the 8x8 bit multiply with an average speed up factor of 0.82 This is due to the time required to transfer data around the dual microcontroller system being significant in comparison to the time required to complete the multiply operation, thus nullifying the potential advantage that might be expected of a dual microcontroller system. The 16x16 multiplier exhibited good performance, with results showing a maximum average speed up factor of 1.7 and an average speed up factor of 1.5. The 16x16 multiplication requires longer time to compute and the data transfer time between microcontrollers whilst still having an impact on the overall computation time is significantly less than for the 8x8 multiplier A formula has been developed to provide an estimate of the possible speed up within a system in relation to the process execution time and the time required to communicate data around the proposed system. The proposed system was developed and tested using the Proteus simulation software

The Complexity of Orbits of Computably Enumerable Sets

The goal of this paper is to announce there is a single orbit of the c.e. sets with inclusion, \E, such that the question of membership in this orbit is $\Sigma^1_1$-complete. This result and proof have a number of nice corollaries: the Scott rank of \E is \wock +1; not all orbits are elementarily definable; there is no arithmetic description of all orbits of \E; for all finite $\alpha \geq 9$, there is a properly $\Delta^0_\alpha$ orbit (from the proof). A few small corrections made in this versionComment: To appear in the Bulletion of Symbolic Logi

Autonomous Drone Network: Non-Intrusive Control and Indoor Formation Positioning

The Teal Group estimated worldwide drone expenditure in 2013 to be $5.2 billion. Since then, worldwide drone expenditure has risen considerably, with the International Data Corporation (IDC) forecasting worldwide spending on drones to total$12.3 billion in 2019, with a compound annual growth rate (CAGR) forecast of 30.6% to 2022. As of 2019, Goldman Sachs report military applications account for 70% of the total spend with consumer applications accounting for 17%, and commercial/civil applications accounting for the remaining 13% where the latter are showing the fastest growth. Applications in construction, agriculture, offshore oil and gas, policing, journalism, border protection, mining and cinematography are predicted to see the greatest drone investment. As the demands increase, and particularly for applications that are time critical or that span large geographical areas, the single drone solution may be inadequate due to its limited energy and payload. A multiple drone solution, where the drones are networked and the drone’s position is established by GPS (global positioning system), is able to complete demanding applications more efficiently. In such systems however, the accuracy of GPS can be substantially compromised when deployed near tall buildings, trees, or bridges or if deployed indoors or underground. In this research, a drone position determination (DPD) algorithm, is proposed to overcome the shortcomings of GPS when satellite signals are compromised. An ad-hoc Wi-Fi network of autonomous quadcopter drones is constructed, as a platform to demonstrate the algorithm performance. To complement the DPD algorithm calculation, a method to estimate the distance flown, and also estimate the complete flightpath of a drone by considering the interaction of the angular velocities of a quadcopter’s four rotors (AVQR), is presented. The flight plan to examine the AVQR algorithm yields results enabling the distance flown to be calculated to an accuracy of 95%