Post-Processing of Low Dose Mammography Images

In mammography, X-ray radiation is used in sufficient doses to be captured on film for cancer diagnosis. A problem lies in the inherent nature of X-rays to cause cancer. The resolution of the images obtained on film is directly related to the radiation dosage. Thus, a trade-off between image quality and radiation exposure is necessary to ensure proper diagnosis without causing cancer. A possible solution is to decrease the dosage of radiation and improve the image quality of mammograms using post- processing methods applied to digitized film images. Image processing techniques that may improve the resolution of images captured at lower doses include crispening, denoising, histogram equalization, and pattern recognition methods. The Wright Patterson Air Force Base Hospital Radiology Department sponsored this research and provided digitized images of the American College of Radiology (ACR) phantom, which is a model for mammogram image quality and classification. Side by side comparisons were performed of high dose images and low-dose images post-processed using the methods mentioned. The result was improved- resolution on mammography images for lower radiation doses. Thus, this research represents progress towards solving a problem that currently plagues mammography: exposure of patients to high doses of cancer- causing radiation to obtain quality mammography images. By improving the image quality of mammography images at lower radiation doses, the problem of cancer induced by high radiation exposure is alleviated

Code Generation from Hybrid Systems Models for Distributed Embedded Systems

Code generation from hybrid system models is a promising approach to producing reliable embedded systems. This approach presents new challenges as the precise semantics of the model are hard to capture in the code. A framework for generating code was introduced for single threaded/processor environments. Here, we extend it by considering code generation for distributed environments. We also define criteria for faithful implementation of the model. To this end, we define faulty and missed transitions. For preventing faulty transitions, we build on the idea of instrumentation we have developed for sound simulation of hybrid systems. Finally, we present sufficient conditions to avoid missed transitions and provide examples

Sound Code Generation From Hybrid System Models: Some Theoretical Results

Code generation from hybrid system models, a promising approach for producing reliable embedded systems, has been our research focus for some time now. In this report, we summarize the progress made thus far and provide directions for research towards realization of this goal

Run-Time Checking of Dynamic Properties

We consider a first-order property specification language for run-time monitoring of dynamic systems. The language is based on a linear-time temporal logic and offers two kinds of quantifiers to bind free variables in a formula. One kind contains the usual first-order quantifiers that provide for replication of properties for dynamically created and destroyed objects in the system. The other kind, called attribute quantifiers, is used to check dynamically changing values within the same object. We show that expressions in this language can be eficiently checked over an execution trace of a system

Model-Based Testing and Monitoring for Hybrid Embedded Systems

We propose an integrated framework for testing and monitoring the model-based embedded systems. The framework incorporates three components: 1) model-based test generation for hybrid system, 2) run-time verification, and 3) modular code generation for hybrid systems. To analyze the behavior of a model-based system, the model of the system is augmented with a testing automaton that represents a given test case, and with a monitoring automaton that captures the formally specified properties of the system. The augmented model allows us to perform the model-level validation. In the next step, we use the modular code generator to convert the testing and monitoring automata into code that can be linked with the system code to perform the validation tasks on the implementation level. The paper illustrates our techniques by a case study on the Sony AIBO robot platform

Modular Code Generation from Hybrid Automata Based on Data Dependency

Model-based automatic code generation is a process of converting abstract models into concrete implementations in the form of a program written in a high-level programming language. The process consists of two steps, first translating the primitives of the model into (approximately) equivalent implementations, and then scheduling the implementations of primitives according to the data dependency inherent in the model. When the model is based on hybrid automata that combine continuous dynamics with a finite state machine, the data dependency must be viewed in two aspects: continuous and discrete. Continuous data dependency is present between mathematical equations modeling timecontinuous behavior of the system. On the other hand, discrete data dependency is present between guarded transitions that instantaneously change the continuous behavior of the system. While discrete data dependency has been studied in the context of code generation from modeling languages with synchronous semantics (e.g., ESTEREL), there has been no prior work that addresses both kinds of dependency in a single framework. In this paper, we propose a code generation framework for hybrid automata which deals with continuous and discrete data dependency. We also propose techniques for generating modular code that retains modularity of the original model. The framework has been implemented based on the hybrid system modeling language CHARON, and experimented with Sony's robot platform AIBO

Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories

Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique