Threading model optimization of the AEMB Microprocessor

AEMB is a 32-bit RISC architecture processor with multi threading. It is a soft core processor designed for FPGA implementation and available as an open source. The processor runs on the instruction set of the Microblaze processor developed by Xilinx. The current threading model in AEMB is a fine grained model that interleaves threads one instruction at a time with separate register sets for each thread. This project aims at understanding the architecture of the AEMB and improving the performance of its threading model. The chosen optimization is to change the current threading model to a coarse grained one that switches threads on branch instructions. The advantage of this approach is that the pipeline no longer has to stall on every branch instruction executed as the processor will be executing instructions from another thread. Thus, branches cause the processor to stall only when there is back to back branch instructions or when two branch instructions with one gap between them and the first of them has no delay slot. This is quite an improvement over the previous case where the processor stalls for one cycle on any branch instruction encountered. The disadvantage to the coarse grained threading model is that data hazards that can’t be forwarded can now cause the processor to stall up to three cycles in the worst case scenario compared to only one cycle stall in the old model. As for Area consumption on FPGA, synthesis showed that the modified core utilizes double the number of LUTs that the original AEMB needs but there was no significant increase in the number of register. Further quantitative analysis is necessary to determine the total gain in performance by running the suitable benchmarks on both versions of the processor. The results are expected to be in favor of the design if the improved case is more common that the negatively affected cases

Intravenous Lidocaine Infusion with Single Low-Dose Ketamine as an Adjuvant to General Anesthesia in Posterior Spine Fusion

Background: Posterior spinal fusion (PSF) is a common surgical operation used to correct degeneration of the lumbar spine with considerable postoperative pain. The primary objective of this study is to compare the total intraoperative and postoperative opioid consumption and numeric pain scale during the first 24 hours after surgery between the lidocaine/ketamine group and the narcotic-only group. Materials and Methods: Sixty adult patients (age 18–65 years) scheduled for elective PSF were included. Patients were divided randomly into either the lidocaine/ketamine group (LK group), who received lidocaine and ketamine injection in addition to usual perioperative narcotic analgesia, and the narcotic-only group (N group) who depended on narcotics only. The primary outcome measures were total intraoperative and postoperative opioid consumption and pain scores during the first 24 hours postoperatively. The secondary outcome measures were sedation score, intravenous rescue analgesia, postoperative nausea and vomiting, and pruritis during the first 24 hours postoperatively. Results: Patients in the LK group had lower intraoperative fentanyl consumption (216.3 ± 28.8 μg) than those in the N group (363 ± 35 μg). The LK group consumed less morphine during the first 24 hours after surgery (49.5 ± 6.0 mg) than the N group did (57.8 ± 8.6 mg). The LK group had lower pain scores at all-time intervals during the first 24 hours (2, 6, 12, and 24 hours) than the N group did. Conclusions: Intraoperative lidocaine infusion with low-dose ketamine reduced opioid consumption and pain scores in patients undergoing PSF

Intrinsic decoherence effects on correlated coherence and quantum discord in XXZ Heisenberg model

Spin qubits are at the heart of technological advances in quantum processors and offer an excellent framework for quantum information processing. This work characterizes the time evolution of coherence and nonclassical correlations in a two-spin XXZ Heisenberg model, from which a two-qubit system is realized. We study the effects of intrinsic decoherence on coherence (correlated coherence) and nonclassical correlations (quantum discord), taking into consideration the combined impact of an external magnetic field, Dzyaloshinsky-Moriya (DM) and Kaplan Shekhtman Entin-Wohlman-Aharony (KSEA) interactions. To fully understand the effects of intrinsic decoherence, we suppose that the system can be prepared in one of the two well-known extended Werner-like (EWL) states. The findings show that intrinsic decoherence leads the coherence and quantum correlations to decay and that the behavior of the aforementioned quantum resources relies strongly on the initial EWL state parameters. We, likewise, found that the two-spin correlated coherence and quantum discord; become more robust against intrinsic decoherence depending on the type of the initial state. These outcomes shed light on how a quantum system should be engineered to achieve quantum advantages

DSRC Performance Analysis in Foggy Environment for Intelligent Vehicles System

Advanced Driver Assistance System (ADAS) is one of the fastest growing areas in the Intelligent Transportation Systems (ITS). Research efforts has focused on developing a driver assistant alert system to warn driver in foggy environment. However, there is a lack of which effective V2V/V2I communication technology would be the best to extend and disseminate this information to nearby vehicles. In this paper, we examine the use of Dedicated Short Range Communications (DSRC) as a V2V communication mechanism to share the foggy conditions to nearby vehicles. The study also investigates the effect of changing the fog/air density on the DSRC performance in intelligent vehicles system. Simulation experiments are setup to study the influence of the fog density on the DSRC performance in communicating the road‟s foggy conditions to nearby vehicles via DSRC communications. The research findings proved that the DSRC performance can persist through fog/air density changes, which helps to confirm that it can help making up for lost human visibility and driver safety experience has been improved on roads during foggy times. This finding aims to promote safe highway operations in foggy or smoky conditions

DSCR Based Sensor-Pooling Protocol for Connected Vehicles in Future Smart Cities

Smart cities are racing to create a more connected Intelligent Transportation Systems (ITS) that rely on collecting data from every possible sensor such as a smart utility meter or a smart parking meter. The use of more sensors resulted in generating a lot of information that maps the smart city environment conditions to more real time data points that needed to be shared and analyzed among smart city nodes. One possibility, to carry and share the collected data, is in autonomous vehicles systems, which use the Dedicated Short Range Communications (DSRC) technology. For example, in a Car-to-Parking-Meter or a Vehicle-to-Vehicle (V2V) communications, short-range embedded sensors such as Bluetooth, Cameras, Lidar send the collected data to the vehicle’s Electronic Control Unit (ECU) or to a road side gateway for making collaborative decisions and react to the environment’s surrounding conditions. The goal of this research is to develop and test a DSRC based sensor-pooling protocol for vehicles to cooperatively communicate inclement weather or environment conditions. Five simulation experiments are setup using PreScan and Simulink to validate and study the scalability of the proposed solution. PreScan is an automotive simulation platform that is used for developing and testing Advanced Driver Assistance System (ADAS). The research findings proved that the DSRC can be used to effectively stream the short range sensors’ collected data over a long distance communications link

DSRC Performance Analysis in Foggy Environment for Intelligent Vehicles System

Advanced Driver Assistance System (ADAS) is one of the fastest growing areas in the Intelligent Transportation Systems (ITS). Research efforts has focused on developing a driver assistant alert system to warn driver in foggy environment. However, there is a lack of which effective V2V/V2I communication technology would be the best to extend and disseminate this information to nearby vehicles. In this paper, we examine the use of Dedicated Short Range Communications (DSRC) as a V2V communication mechanism to share the foggy conditions to nearby vehicles. The study also investigates the effect of changing the fog/air density on the DSRC performance in intelligent vehicles system. Simulation experiments are setup to study the influence of the fog density on the DSRC performance in communicating the road?s foggy conditions to nearby vehicles via DSRC communications. The research findings proved that the DSRC performance can persist through fog/air density changes, which helps to confirm that it can help making up for lost human visibility and driver safety experience has been improved on roads during foggy times. This finding aims to promote safe highway operations in foggy or smoky conditions