Introduction: Medical engineering systems nowadays play a very significant role in the diagnosis of various diseases. One of the most applicable devices of the medical engineering are receiving and recording devices of the heart signals, such as electrocardiograph.Application of Holter monitoring devices could be known as one of the substantial methods of electrocardiograph during daily activities. In this article, design and manufacture of Holter monitoring device based-on Arm micro-controller are proposed.Materials and Methods: Atmel’s SAM3S Chip from the family of Arm has an eleven-channel transformer of analogue to digital which can transform analogue signals to digital values with 12 Bit accuracy by utilizing from Arm architecture used in the core of Cortex-M3 with the structure of Thumb2 processes the information with the speed of 60 MHZ. Furthermore, for receiving and reinforcement of heart signals, INA331 Chip, the product of TI Company was used. Using Chip CYRF6936, signals were transferred to the physician computer with maximum speed of one megabit/sec.Results: AT91Sam3S with the existence of the Internal Interface Circuit for communication with the external devices has enabled the system to be connected to memory cards and computer without requirement to any other interfaces. This instrumental amplifier with a single-polar feedback has 94dB Common-mode rejection ratio and 54dBamplification ratio. This chip has decreased the consumed power of the vehicle to 330 mW, which by using Lithium- polymer battery of 3000 mAH, the vehicle can be fed for one day without need for charge.Conclusion: Using wireless communication, keeping the quality of the received signal from body, limitation of information reservation was removed. Furthermore, by simplifying analogue circuits, consumed power of the device was reduced and thus time used from the device was increased without the need for recharge. In addition, by using from lowconsumption 32-Bit Arm processor, high processing power and digital process capability on the heart signals will be possible easily.

In this paper, a new effective and computationally reduced method for congestion control in high speed dynamic computer networks is introduced. The controller is designed using the well-known predictive functional control (PFC) scheme and an Armarkov model representation that considers the system delay explicitly. Use of the multi-step-ahead predictive Armarkov model structure within the PFC results in a simple algebraic control law that does not require recursive model output computation in the so-called prediction horizon performed in the other Model Predictive Controllers (MPC). This combination not only reduces the required computational load, but the accumulative error due to the model uncertainties decrease considerably. Packet-level simulations based on ns-2 are provided to show good performance of Arm-PFC in a large variety of topology and traffic mixtures for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay and jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the proposed method with respect to the well-known AQM methods such as RED, PI, and REM, which are also simulated for comparison.

Since smartphones are usually personal devices full of private information, they are a popular target for a vast variety of real-world attacks such as Code Reuse Attack (CRA). CRAs enable attackers to execute any arbitrary algorithm on a device without injecting an executable code. Since the standard platform for mobile devices is Arm architecture, we concentrate on available Arm-based CRAs. Currently, three types of CRAs are proposed on Arm architecture including Return2ZP, ROP, and BLX-attack in accordance to three sub-models available on X86. Ret2Libc, ROP, and JOP. In this paper, we have considered some unique aspects of Arm architecture to provide a general model for code reuse attacks called Patulous Code Reuse Attack (PCRA). Our attack applies all available machine instructions that change Program Counter (PC) as well as direct or indirect branches in order to deploy the principles of CRA convention. We have demonstrated the effectiveness of our approach by defining five different sub-models of PCRA, explaining the algorithm of finding PCRA gadgets, introducing a useful set of gadgets, and providing a sample proof of concept exploit on Android 4.4 platform.

Pose estimation is a process to identify how a human body and/or individual limbs are configured in a given scene. Hand pose estimation is an important research topic which has a variety of applications in human-computer interaction (HCI) scenarios, such as gesture recognition, animation synthesis and robot control. However, capturing the hand motion is quite a challenging task due to its high flexibility. Many sensor-based and vision-based methods have been proposed to fulfill the task. In sensor-based systems, specialized hardware is used for hand motion capture. Generally, vision-based hand pose estimation methods can be divided into two categories: appearance-based methods and model-based methods. In appearance-based approaches, various features are extracted from the input images to estimate the hand pose. Usually a lot of training samples are used to train a mapping function from the features to the hand poses in advance. Given the learned mapping function, the hand pose can be estimated efficiently. In model-based approaches the hand pose is estimated by aligning a projected 3D hand model to the extracted hand features in the inputs. Therefore, the desired information to be provided includes state at any time. These methods require a lot of calculations which are not possible in practice to implement them immediately. Hand pose estimation using (color/depth) images consist of three steps: 1. Hand detection and its separation2. Feature extraction3. Setting the parameters of the model using extracted feature and updating the modelTo extract necessary features for pose estimation, depending on used model and usage of hand gesture analysis, features such as fingertips position, number of fingers, palm position and joint angles are extracted. In this paper a model-based markerless dynamic hand poses estimation scheme is presented. Motion Capture is the process of recording a live motion event and translating it into usable mathematical terms by tracking a number of key points in space over time and combining them to obtain a single 3D representation of the performance. The sequence of depth images, color images and skeleton data obtained from Kinect (a new tool for markerless motion capture) at 30 frames per second are as inputs of this scheme. The proposed scheme exploits both temporal and spatial features of the input sequences, and focuses on index and thumb fingertips localization and joint angles of the robot Arm to mimic the user's Arm movements in 3D space in an uncontrolled environment. The RoboTECH II ST240 is used as a real robot Arm model. Depth and skeleton data are used to determine the angles of the robot joints. Three approaches to identify the tip of the thumb and index fingers are presented using existing data, each with its own limitations. In these approaches, concepts such as thresholding, edge detection, making convex hull, skin modeling and background subtraction are used. Finally, by comparing tracked trajectories of the user's wrist and robot end effector, the graphs show an error about 0. 43 degree in average which is an appropriate performance in this research. The key contribution of this work is hand pose estimation per every input frame and updating Arm robot according to estimated pose. Thumb and index fingertips detection as part of feature vector resulted using presented approaches. User movements transmit to the corresponding Move instruction for robot. Necessary features for Move instruction are rotation values around joints in different directions and opening value of index and thumb fingers at each other.

Currently, real-time force control of actuators is one of the most important issues found in different branches of industry. In order to achieve the best performance of a given electromagnetic actuator, first of all, the parameters of the dynamic model of the actuator should be identified. In this research, an experimental platform is designed and manufactured to conduct experiments and identify the parameters of the electromagnetic actuator. The process of generating a reference signal for identification is performed in a real-time manner by the STM32F746ZG board. The Arm-Cortex M7 microcontroller on the board has provided us with the ability to produce any type of signal with the desired frequency, amplitude and bias. In this research, a hArmonic chirp signal is generated to study the behavior of the actuator at different frequency intervals. The amplitude of inertial force generated by the actuator is measured by a Kistler Type 9255B dynamometer. Finally, by analyzing the system response, the accurate dynamic model for the system is successfully estimated.