Automatically Detecting "Excessive Dynamic Memory Allocations" Software Performance Anti-Pattern

2016 ◽  
Author(s):  
Manjula Peiris ◽  
James H. Hill
Keyword(s):  
Software Performance ◽  
Dynamic Memory

How to write a class

2018 ◽  
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson
Keyword(s):  
Computer Science ◽  
Software Design ◽  
Memory Allocation ◽  
Quantum Mechanical ◽  
Sufficient Information ◽  
Trial And Error ◽  
Dynamic Memory ◽  
Dynamic Memory Allocation

While there is no such thing as a “typical” C++ class, several common syntactical constructs lend themselves to extremely widespread use and must be mastered by C++ programmers. To motivate the discussion of software design at the level of the C++ class, examples from computer science and optics are introduced. Important syntactical elements such as constructors, destructors, copy constructors, assignment operators, cast operators, and const qualifiers, together with function overloading, operator overloading, and dynamic memory allocation are discussed. These concepts, illustrated with examples from physics, are presented and explained. Further examples from optical and quantum mechanical problems are left to the exercises. This chapter and its exercises gives the reader sufficient information to begin developing his or her own classes and to experiment with class design through trial and error.

Artificial intelligence-guided image acquisition on patients with implanted electrophysiological devices: results from a pivotal prospective multi-center clinical trial

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Surette ◽  
A Narang ◽  
R Bae ◽  
H Hong ◽  
Y Thomas ◽  
...  
Keyword(s):  
Qualitative Assessment ◽  
Funding Source ◽  
Software Performance ◽  
Private Company ◽  
Diagnostic Quality ◽  
Icd Leads ◽  
Significant Difference ◽  
Level 3 ◽  
And Function ◽  
The Impact

Abstract Background A novel, recently FDA-authorized software uses deep learning (DL) to provide prescriptive transthoracic echocardiography (TTE) guidance, allowing novices to acquire standard TTE views. The DL model was trained by >5,000,000 observations of the impact of probe motion on image orientation/quality. This study evaluated whether novice-acquired TTE images guided by this software were of diagnostic quality in patients with and without implanted electrophysiological (EP) devices, focusing on RV size and function, which were thought to be sensitive to EP devices. Some aspects of the study have previously been presented. Methods 240 patients (61±16 years old, 58% male, 33% BMI >30 kg/m2, 91% with cardiac pathology) were recruited. 8 nurses without echo experience each acquired 10 view TTEs in 30 patients guided by the software. 235 of the patients were also scanned by a trained sonographer without assistance from the software. 5 Level 3 echocardiographers independently assessed the diagnostic quality of the TTEs acquired by the nurses and sonographers to evaluate the effect of EP devices on DL software performance. Results Nurses using the AI-guided acquisition software acquired TTEs of sufficient quality to make qualitative assessments of right ventricular (RV) size and function in greater than 80% of cases for patients with and without implanted EP devices (Table). There was no significant difference between nurse- and sonographer-acquired scans. Conclusion These results indicate that new DL software can guide novices to obtain TTEs that enable qualitative assessment of RV size even in the presence of implanted EP devices. The results of the comparison to sonographer-acquired exams indicate the software performance is robust to presence of pacemaker/ICD leads visible in the images (Figure). Nurse-acquired TTE with visible ICD lead Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Caption Health, Inc.

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