Reduced Programming Time and Strong Symptom Control Even in Chronic Course Through Imaging-Based DBS Programming

Objectives: Deep brain stimulation (DBS) programming is based on clinical response testing. Our clinical pilot trial assessed the feasibility of image-guided programing using software depicting the lead location in a patient-specific anatomical model.Methods: Parkinson's disease patients with subthalamic nucleus-DBS were randomly assigned to standard clinical-based programming (CBP) or anatomical-based (imaging-guided) programming (ABP) in an 8-week crossover trial. Programming characteristics and clinical outcomes were evaluated.Results: In 10 patients, both programs led to similar motor symptom control (MDS-UPDRS III) after 4 weeks (medicationOFF/stimulationON; CPB: 18.27 ± 9.23; ABP: 18.37 ± 6.66). Stimulation settings were not significantly different, apart from higher frequency in the baseline program than CBP (p = 0.01) or ABP (p = 0.003). Time spent in a program was not significantly different (CBP: 86.1 ± 29.82%, ABP: 88.6 ± 29.0%). Programing time was significantly shorter (p = 0.039) with ABP (19.78 ± 5.86 min) than CBP (45.22 ± 18.32).Conclusion: Image-guided DBS programming in PD patients drastically reduces programming time without compromising symptom control and patient satisfaction in this small feasibility trial.

A lead through approach for programming a welding arm robot using machine vision

Welding is a complex manufacturing process. Its quality depends on the welder skills, especially in welding complex paths. For consistency in modern industries, the arm robot is used to accomplish this task. However, its programming and reprogramming are time consuming and costly and need an expert programmer. These limit the use of robots in medium and small industries. This paper introduces a new supervised learning technique for programming a 4-degree of freedom (DOF) welding arm robot with an automatic feeding electrode. This technique is based on grasping the welding path control points and motion behavior of an expert welder. This is achieved by letting the welder move the robot end effector, which represents the welding torch, through the welding path. At the path control points, the position and speed are recorded using a vision system. Later, these data are retrieved by the robot to replicate the welding path. Several 2D paths are tested to assess the proposed approach accuracy and programming time and easiness in comparison with the common one. The results prove that the proposed approach includes fewer steps and consumes less programming time. Moreover, programming can be accomplished by the welder and no need for an expert programmer. These enhancements will improve the share of robots in welding and similar industries.

Risk-Averse Stochastic Programming: Time Consistency and Optimal Stopping

This paper addresses time consistency of risk-averse optimal stopping in stochastic optimization. It is demonstrated that time-consistent optimal stopping entails a specific structure of the functionals describing the transition between consecutive stages. The stopping risk measures capture this structural behavior and allow natural dynamic equations for risk-averse decision making over time. Consequently, associated optimal policies satisfy Bellman’s principle of optimality, which characterizes optimal policies for optimization by stating that a decision maker should not reconsider previous decisions retrospectively. We also discuss numerical approaches to solving such problems.

Partial-Gated Memristor Crossbar for Fast and Power-Efficient Defect-Tolerant Training

A real memristor crossbar has defects, which should be considered during the retraining time after the pre-training of the crossbar. For retraining the crossbar with defects, memristors should be updated with the weights that are calculated by the back-propagation algorithm. Unfortunately, programming the memristors takes a very long time and consumes a large amount of power, because of the incremental behavior of memristor’s program-verify scheme for the fine-tuning of memristor’s conductance. To reduce the programming time and power, the partial gating scheme is proposed here to realize the partial training, where only some part of neurons are trained, which are more responsible in the recognition error. By retraining the part, rather than the entire crossbar, the programming time and power of memristor crossbar can be significantly reduced. The proposed scheme has been verified by CADENCE circuit simulation with the real memristor’s Verilog-A model. When compared to retraining the entire crossbar, the loss of recognition rate of the partial gating scheme has been estimated only as small as 2.5% and 2.9%, for the MNIST and CIFAR-10 datasets, respectively. However, the programming time and power can be saved by 86% and 89.5% than the 100% retraining, respectively.

Robotic Finishing of Interior Regions of Geometrically Complex Parts

Surface finishing is an important manufacturing process. Many parts with complex geometries require finishing of internal regions before they can be used. In small and medium volume productions most of the finishing tasks are non-repetitive in nature, and have to be performed manually. These finishing operations for parts with complex geometries can be quite labor intensive, and may pose risk to humans. We have developed a collaborative finishing system where human operators work on high level decision making, and the robot assistants carry out the labor intensive low level finishing tasks. The human operator guides the robotic system by transferring operator knowledge through a user interface. Our system generates instructions for the robots based on the user inputs and task requirements. We have also developed a planning algorithm that automatically computes the paths for the robots by using the CAD model of the part. This significantly reduces the robot programming time and improves the efficiency of the finishing system. If needed, the system seeks help from the human operator by generating notifications.

Essential Functionalities for Commercial Internet Presence

A balanced implementation of functionalities within a commercial internet presence is important, because of not only the involved complexity and cognitive load required in their use, but also for financial reasons. Investing money on features (e.g., software modules, programming time, maintenance and update, etc.) that are not valued by online consumers, hinders an efficient allocation of resources, especially when financial resources are scarce, a situation that corresponds too many small businesses operating on the internet. However, literature provides little help for managers to decide which functionalities should be implemented, according to a rational basis. Within this context, this research sought to fill in the gap between literature review and the need for helping companies to understand better how to build and keep online businesses. The authors work identified 16 essential functionalities grouped into four sets: order processing, advertising and prominence, product analysis, and payment.