Architectural Support for Secure Virtualization under a Vulnerable Hypervisor

Although cloud computing has emerged as a promising future computing model, security concerns due to malicious tenants have been deterring its fast adoption. In cloud computing, multiple tenants may share physical systems by using virtualization techniques. In such a virtualized system, a software hypervisor creates virtual machines (VMs) from the physical system, and provides each user with an isolated VM. However, the hypervisor, with a full control over hardware resources, can access the memory pages of guest VMs without any restriction. By compromising the hypervisor, a malicious user can access the memory contents of the VMs used by other users. In this paper, we propose a hardware-based mechanism to protect the memory of guest VMs from unauthorized accesses, even with an untrusted hypervisor. With this mechanism, memory isolatio

Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training

Study on Load Sensing Method by Speed of PMSM

The PMSM is widely used in industry. In order to detect faults and to monitor the system, it is important to detect changes in load for systems in which the PMSM is installed.However, the price of sensors is high and it is difficult to install sensors because of space limitations. Therefore, a method is proposed to detect the load using the speed of the motor in a system without sensors. An FOC method for operating the motor and a detection method using speed fluctuation have been studied; however, the speed includes many error components such as motor inertia and measurement error. To improve the accuracy of the speed measurement, the pulse count method has been studied and implemented. Actual experiments were performed to verify the proposed algorithm. The proposed algorithm detects the load variation at approximately the same time as the actual load is applied.1

Super Wide-Field Photoacoustic Microscopy of Animals and Humans < italic > In Vivo </italic >

Acoustic-resolution photoacoustic microscopy (AR-PAM) is an emerging biomedical imaging modality that combines superior optical sensitivity and fine ultrasonic resolution in an optical quasi-diffusive regime (similar to 1-3 mm in tissues). AR-PAM has been explored for anatomical, functional, and molecular information in biological tissues. Heretofore, AR-PAM systems have suffered from a limited field-of-view (FOV) and/or slow imaging speed, which have precluded them from routine preclinical and clinical applications. Here, we demonstrate an advanced AR-PAM system that overcomes both limitations of previous AR-PAM systems. The new AR-PAM system demonstrates a super wide-field scanning that utilized a 1-axis water-proofing microelectromechanical systems (MEMS) scanner integrated with two linear stepper motor stages. We achieved an extended FOV of 36x80mm(2) by mosaicking multiple volumetric images of 36 x 2.5 mm(2) with a total acquisition time of 224 seconds. For one volumetric data (i.e., 36 x 2.5 mm(2)), the B-scan imaging speed over the short axis (i.e., 2.5 mm) was 83 Hz in humans. The 3D volumetric image was also provided by using MEMS mirror scanning along the X-axis and stepper-motor scanning along the Y-axis. The super-wide FOV mosaic image was realized by registering and merging all individual volumetric images. Finally, we obtained multi-plane whole-body in-vivo PA images of small animals, illustrating distinct multi-layered structures including microvascular networks and internal organs. Importantly, we also visualized microvascular networks in human fingers, palm, and forearm successfully. This advanced MEMS-AR-PAM system could potentially enable hitherto not possible wide preclinical and clinical applications.11Nsciescopu

Organic Dye-Derived N, S Co-Doped Porous Carbon Hosts for Effective Lithium Polysulfide Confinement in Lithium–Sulfur Batteries

Lithium–sulfur batteries are considered as attractive candidates for next-generation energy storage systems originating from their high theoretical capacity and energy density. However, the severe shuttling of behavior caused by the dissolution of lithium polysulfide intermediates during cycling remains a challenge for practical applications. Herein, porous carbon materials co-doped with nitrogen and sulfur atoms were prepared through a facile hydrothermal reaction of graphene oxide and methylene blue to obtain a suitable host structure for regulating the lithium polysulfide shuttling behavior. Experimental results demonstrated that the abundant heteroatom-containing moieties in the carbon frameworks not only generated favorable active sites for capturing lithium polysulfide but also enhanced redox reaction kinetics of lithium polysulfide intermediates. Consequently, the corresponding sulfur composite electrodes exhibited excellent rate performance and cycling stability along with high Columbic efficiency. This work highlights the approach for the preparation of nitrogen and sulfur co-doped carbon materials derived from organic dye compounds for high performance energy storage systems