Imaging of Hernias

Abdominal wall hernias are usually suggested by the patient’s history and confirmed by physical examination; however, the history may be not typical, especially in patients with abdominal pain, distention, and overweight patients or in patients with small hernias located in unusual sites. Although most abdominal hernias are asymptomatic, the fear of developing complications like irreducibility, incarceration, and strangulation may necessitate prophylactic surgical repair; thus, early and accurate diagnosis is important. Before 20 years, herniorrhaphy was considered for imaging of hernias; however, in recent years, computed tomography (CT) (especially multidetector CT (MDCT)), together with ultrasound represented the mainstay of the diagnosis of abdominopelvic wall hernias by imaging, and magnetic resonance imaging (MRI) could be used as a diagnostic aid in a minority of the cases. Each imaging modality has its own privilege. The main advantage of ultrasound is the dynamic ability for assessment, while the main advantage of computed tomography is the multiplanar reformatting, allowing identification and accurate diagnosis of the hernia type, its content, and also the associated complications. Radiologists should be familiar with common sites of hernias and their detailed normal anatomy in order to reach the diagnosis easily

FlexiWi-Fi Security Manager Using Freescale embedded System

Among the current Wi-Fi two security models (Enterprise and Personal), while the Enterprise model (802.1X) offers an effective framework for authenticating and controlling the user traffic to a protected network, the Personal model (802.11) offers the cheapest and the easiest to setup solution. However, the drawback of the personal model implementation is that all access points and client radio NIC on the wireless LAN should use the same encryption key. A major underlying problem of the 802.11 standard is that the pre-shared keys are cumbersome to change. So if those keys are not updated frequently, unauthorized users with some resources and within a short timeframe can crack the key and breach the network security. The purpose of this paper is to propose and implement an effective method for the system administrator to manage the users connected to a router, update the keys and further distribute them for the trusted clients using the Freescale embedded system, Infrared and Bluetooth modules

Sensor Fusion to Detect Scale and Direction of Gravity in Monocular Slam Systems

Monocular simultaneous localization and mapping (SLAM) is an important technique that enables very inexpensive environment mapping and pose estimation in small systems such as smart phones and unmanned aerial vehicles. However, the information generated by monocular SLAM is in an arbitrary and unobservable scale, leading to drift and making it difficult to use with other sources of odometry for control or navigation. To correct this, the odometry needs to be aligned with metric scale odometry from another device, or else scale must be recovered from known features in the environment. Typically known environmental features are not available, and for systems such as cellphones or unmanned aerial vehicles (UAV), which may experience sustained, small scale, irregular motion, an IMU is often the only practical option. Because accelerometers measure acceleration and gravity, an inertial measurement unit (IMU) must filter out gravity and track orientation with complex algorithms in order to provide a linear acceleration measurement that can be used to recover SLAM scale. This paper will explore an alternative method, which detects and removes gravity from the accelerometer measurement by using the unscaled direction of acceleration derived from the SLAM odometry

Internet of Things Security Using Proactive WPA/WPA2

The Internet of Things (IoT) is a natural evolution of the Internet and is becoming more ubiquitous in our everyday home, business, health, education, and many other aspects. The data gathered and processed by IoT networks might be sensitive whichcallsforfeasibleandadequatesecuritymeasures.This paper describes the use of the Wi-Fi technology in the IoT connectivity, then proposes a new approach, the Proactive Wire- less Protected Access (PWPA), to protect the access networks. Then a new end to end (e2e) IoT security model is suggested to include the PWPA scheme. To evaluate the solution?s security and performance, firstly, the cybersecurity triad: confidentiality, integrity, and availability aspects were discussed, secondly, the solution?s performance was compared to a counterpart e2e security solution, the Secure Socket Layer security. A small IoT network was set up to simulate a real environment that uses HTTP protocol. Packets were then collected and analyzed. Data analysis showed a bandwidth efficiency increase by 2% (Internet links) and 12% (access network), and by 344% (Internet links) and 373% (access network) when using persistent and non- persistent HTTP respectively. On the other hand, the analysis showed a reduction in the average request-response delay of 25% and 53% when using persistent and non-persistent HTTP respectively. This scheme is possibly a simple and feasible solution that improves the IoT network security performance by reducing the redundancy in the TCP/IP layers security implementation

Image Classification with CondenseNeXt for ARM-Based Computing Platforms

In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of CondenseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency.Comment: 6 pages, 7 figures, conference, published IEEE Conference pape

Bio-H2 conversion of wastewater via hybrid dark/photo fermentation reactor

Hydrogen energy is a clean source for liveliness betterthan fossil fuel that has hazardous effects on the environmentand atmosphere. Food wastes and organics in the sewage sludgeare a promising sustainable and renewable source for hydrogenproduction where amalgamation of waste treatment and energyproduction would be more than one benefit expressed intreatment of organic pollutants and energy generation.Discovering biohydrogen production from industrialwastewater by dark and photo fermentation was the main aimof this paper. The biogas produced was composed of H2 andCO2, and the maximum H2 content was 25.94%. This ratio wasachieved at batch configuration system and initial pH 6.2 withstarch concentration 15 g/l. Cause of using dark fermentationeffluent (DFE) was used as substrate for A Rhodobactercapsulatus strain and a clostridium culture were cultivated toproduce hydrogen under different light-dark cycles. Acetic andbutyric acids decreased due to first and second photo stages by21.9% and 4.1 % respectively. Maximum hydrogen yield was470.9 ml H2/mol VFAs

R-MnasNet: Reduced MnasNet for Computer Vision

In Deep Learning, Convolutional Neural Networks (CNNs) are widely used for Computer Vision applications. With the advent of new technology, there is an inevitable necessity for CNNs to be computationally less expensive. It has become a key factor in determining its competence. CNN models must be compact in size and work efficiently when deployed on embedded systems. In order to achieve this goal, researchers have invented new algorithms which make CNNs lightweight yet accurate enough to be used for applications like object detection. In this paper, we have tried to do the same by modifying an architecture to make it compact with a fair trade-off between model size and accuracy. A new architecture, R-MnasNet (Reduced MnasNet), has been introduced which has a model size of 3 MB. It is trained on CIFAR-10 [4] and has a validation accuracy of 91.13%. Whereas the baseline architecture, MnasNet [1], has a model size of 12.7 MB with a validation accuracy of 80.8% when trained with CIFAR-10 dataset. R-MnasNet can be used on resource-constrained devices. It can be deployed on embedded systems for vision applications

Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060

With regards to Advanced Driver Assistance Systems in vehicles, vision and image-based ADAS is profoundly well known since it utilizes Computer vision algorithms, for example, object detection, street sign identification, vehicle control, impact cautioning, and so on., to aid sheltered and smart driving. Deploying these algorithms directly in resource-constrained devices like mobile and embedded devices etc. is not possible. Reduced Mobilenet V2 (RMNv2) is one of those models which is specifically designed for deploying easily in embedded and mobile devices. In this paper, we implemented a real-time RMNv2 image classifier in NXP Bluebox 2.0 and NXP i.MX RT1060. Because of its low model size of 4.3MB, it is very successful to implement this model in those devices. The model is trained and tested with the CIFAR10 dataset

Enhanced Data Transportation in Remote Locations Using UAV Aided Edge Computing

In recent years, the applications in the field of Unmanned Aerial Vehicle (UAV) systems has procured research interests among various communities. One of the primary factors being, thinking beyond the box of what could UAV system bring to the table other than military applications? Evidence to any answer for this question is the current day scenarios. We could see numerous applications of UAV starting from commercial applications of delivering consumer goods to life saving medical applications such as delievery of medical products. Using UAVs in for data transportation in remote locations or locations with no internet is a trivial challenge. In-order to perform the tasks and satisfy the requirement, the UAVs should be equipped with sensors and transmitters. Addition of hardware devices increases the number of connections in hardware design, leading to exposure during flight operation. This research proposes an advanced UAV system enabling wireless data transfer ability and secure data transmission with reduced wiring in comparison to a traditional design of UAV. The applications of this research idea targets using edge computing devices to acquire data in areas where internet connectivity is poor and regions where secured data transmission can be used along with UAV system for secure data transport

Real-Time 3-D Segmentation on An Autonomous Embedded System: using Point Cloud and Camera

Present day autonomous vehicle relies on several sensor technologies for it's autonomous functionality. The sensors based on their type and mounted-location on the vehicle, can be categorized as: line of sight and non-line of sight sensors and are responsible for the different level of autonomy. These line of sight sensors are used for the execution of actions related to localization, object detection and the complete environment understanding. The surrounding or environment understanding for an autonomous vehicle can be achieved by segmentation. Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available, however with the advancement in the computing processor, the progression is on developing the deep learning application replacing traditional methods. This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose. The proposed model for outdoor scene segmentation is based on the frustum pointnet, and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level. For real time application the model is deployed on the RTMaps framework with Bluebox (an embedded platform for autonomous vehicle). The proposed architecture is trained with the CITYScpaes and the KITTI dataset