Enhancing EDP of multicore processors through DVFS - Hajiamini, Shervin; Shirazi, Behrooz; Rilvan, Mohamed Azard (Institute of Electrical and Electronics Engineers Inc., 2015)
Dynamic voltage and frequency scaling (DVFS) is a well-known technique to optimize the power dissipation of electronic systems without significantly compromising overall system performance. DVFS exploits the periods of inter-core data exchange (memory-bound operations) to reduce the voltage and frequency (V/F) of the cores in order to reduce the power dissipation during the execution flow of an application running on the CMP. As the lengths of the idle and busy periods of the cores vary depending on the benchmarks, it is crucial for any DVFS technique to maximize the power saving without losing a significant performance. In this work we present two power optimization methodologies that are integrated into a full-system simulator to make online predictions about the voltage and frequency of the cores during the execution time of the benchmarks. We evaluate these methodologies in terms of the V/F predictions vs. the actual utilization of each core periodically. We also compare the overall execution time, energy dissipation, and energy-delay product (EDP) of the power optimization methodologies for various benchmarks. © 2015 IEEE.
Keywords: Benchmarking; DVFS; Dynamic frequency scaling; Dynamic voltage and frequency scaling; Electric losses; Electronic data interchange; Electronic systems; Energy delay product; Energy dissipation; Energy efficiency; Forecasting; Full system simulators; Multi core; Multi-core processor; Multicore; Power Optimization; V/F tracking; Voltage scaling; dept.csc; energy efficiency

Implementing power optimization methodologies to improve energy efficiency of multi-core processors - Rilvan, Mohamed Azard; Hajiamini, Shervin; Hiland, Lauren (2016 NCUR, 2016)
Keywords: dept.csc; ⛔ No DOI found

User authentication and identification on smartphones by incorporating capacitive touchscreen - Rilvan, Mohamed Azard; Lacy, Kolby Isiah; Hossain, Shafaeat; Wang, Bing (Institute of Electrical and Electronics Engineers Inc., 2016)
Smartphones, while providing users ease of access to sensitive information on the go, also present severe security risks if an attacker is able to gain access to them. To strengthen the user authentication and identification in a smartphone, we develop a biometric authentication and identification system which uses the capacitive touchscreen that is featured in all current smartphones. Our methodology focuses on using the touchscreen as a sensor to capture the image of a user's ear, thumb or four fingers. We extract the capacitive raw data from the touched body part to obtain a capacitive image, and then use it to capture geometric features (e.g., length and width of a finger) and principal components. After that, we experiment with Support Vector Machine (SVM) and Random Forest (RF) classifiers to verify and also identify each user. We achieved the maximum authentication accuracy of 98.84% by four fingers with SVM, and maxinum identification accuracy of 97.61% by four fingers with RF. © 2016 IEEE.
Keywords: Authentication; Biometric; Biometric authentication; Biometrics; Decision trees; Geometric feature; Identification (control systems); Identification accuracy; Principal Components; Random forests; Security risks; Sensitive informations; Smartphones; Support vector machines; User authentication; authentication; capacitive touchscreen; dept.csc; identification; smart-phones

Capacitive swipe gesture based smartphone user authentication and identification - Rilvan, Mohamed Azard; Chao, Jedrik; Hossain, Md Shafaeat (Institute of Electrical and Electronics Engineers Inc., 2020)
Weaknesses in smartphone security pose a severe privacy threat to users. Currently, smartphones are secured through methods such as passwords, fingerprint scanners, and facial recognition cameras. To explore new methods and strengthen smartphone security, we developed a capacitive swipe based user authentication and identification technique. Swipe is a gesture that a user performs throughout the usage of a smartphone. Our methodology focuses on using the capacitive touchscreen to capture the user's swipe. While the user swipes, a series of capacitive frames are captured for each swipe. We developed an algorithm to process this series of capacitive frames pertaining to the swipe. While different swipes may contain different numbers of capacitive frames, our algorithm normalizes the frames by constructing the same number of frames for every swipe. After applying the algorithm, we transform the normalized frames into gray scale images. We apply principal component analysis (PCA) to these images to extract principal components, which are then used as features to authenticate/identify the user. We tested random forest (RF) and support vector machine (SVM) algorithms as classifiers. For authentication, the performance of SVM (tested with left swipes) was more promising than RF, yielding a maximum accuracy of 79.88% with an FAR and FRR of 15.84% and 50%, respectively. SVM (tested with right swipes) produced our maximum identification accuracy at 57.81% along with an FAR and FRR of 0.60% and 42.18%, respectively. © 2020 IEEE.
Keywords: Authentication; Decision trees; Face recognition; Facial recognition; Fingerprint scanners; Identification accuracy; Identification techniques; Mobile security; Principal Components; Privacy by design; Smartphone securities; Smartphone security; Smartphones; Support vector machine algorithm; Support vector machines; User authentication; authentication; capacitive swipe; capacitive touchscreen; dept.csc; identification