1,721,049 research outputs found
An algorithm to compute the free distance of turbo codes
No full textA new algorithm for computing the free distance of turbo codes is applied to the CCSDS and the UMTS standard codes. Results on the free distance behaviour for increasing interleaver length are also presented
Computing the free distance of turbo codes and serially concatenated codes with interleavers: algorithms and applications
No full textWe present a new algorithm for computing the free distance dfree of parallel and serially concatenated codes with interleavers, the parameter that dominates the code performance at very high signal-to-noise ratios (SNRs). The knowledge of dfree allows one to analytically estimate the error floor, which may prevent the use of concatenated codes in applications requiring very low error rates. The algorithm is based on the new notion of constrained subcodes, and permits the computation of large distances for large interleavers without a constraint on the input sequence weight (e.g., up to dfree=40 for a rate-1/3 turbo code with interleaver length N=3568). Applications to practical cases of relevant interest, i.e., (1) the new Consultative Committee for Space Data Systems (CCSDS) standard for deep-space telemetry and (2) the new UMTS/3GPP standard for third-generation personal communications, are presented for the first time. Other related aspects, like a study on the free distance distribution of turbo codes with small/medium interleaver length, and a comparison between parallel and serial concatenation behavior, are also discussed
A Machine Vision System for Manual Assembly Line Monitoring
No full textCustomers are asking more and more customized products and expect to receive them in really short times. That is only reachable if there is horizontal and vertical integration, together with high information availability and transparency inside a company. When the production is not fully automatized, i.e. in those companies where the assembly or the production still relies on manual work of people, the monitoring of the line production, in terms of number of pieces produced, may be tricky due to the inevitable variability that operators add to the process, thus making essential the creation of smart systems able to deal with such complex environments and autonomously monitor them. Computer vision systems can be customized and very smart in various contexts, if properly modeled. In this paper we are going to describe the developed Machine Vision algorithm that, building upon BLOB (Binary Large Objects) analysis, is able to detect and count objects produced in a complex industrial context of manual assembly. The developed algorithm would then be compared to a more robust one taken as reference point for a comparison. The comparison between our algorithm and the Machine Learning-based Detector aims at showing the comparable Accuracy, Specificity and Sensitivity of our method, together with its higher versatility and processing speed, thus making it applicable in the plant-wide real-time monitoring of the manual assembly lines
A computer vision system for staff gauge in river flood monitoring
Full text linkRivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames
Using Plastic Injection Moulding Machine Process Parameters for Predictive Maintenance Purposes
No full textIt is a well-known fact that maintenance cost inside a company can be the largest part of operational expenses, second only to energy. Usually, replacing a component or equipment just before a breakdown occurs is the best way to minimize the maintenance cost. This is the main reason why a lot of companies are struggling in collecting data from equipment, and in finding ways to exploit these data for predictive purposes. In this paper we are going to explore multiple sensors' data extracted from an injection moulding machine, with the final aim of developing a Predictive Maintenance model tailored on the specific machine utilization. After the extraction of a training set, we implemented Machine Learning algorithms in order to find the best predictive model able to discern between correct functioning and border line functioning of the machine. We are going to describe the performance reached by the developed model and to show how it deals with completely new data used for testing the model
A wireless body sensor network for clinical assessment of the flexion-relaxation phenomenon
Full text linkAn accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns
Towards Personalized AI-Based Diabetes Therapy: A Review
Full text linkInsulin pumps and other smart devices have recently made significant advancements in the treatment of diabetes, a disorder that affects people all over the world. The development of medical AI has been influenced by AI methods designed to help physicians make diagnoses, choose a course of therapy, and predict outcomes. In this article, we thoroughly analyse how AI is being used to enhance and personalize diabetes treatment. The search turned up 77 original research papers, from which we've selected the most crucial information regarding the learning models employed, the data typology, the deployment stage, and the application domains. We identified two key trends, enabled mostly by AI: patient-based therapy personalization and therapeutic algorithm optimization. In the meanwhile, we point out various shortcomings in the existing literature, like a lack of multimodal database analysis or a lack of interpretability. The rapid improvements in AI and the expansion of the amount of data already available offer the possibility to overcome these difficulties shortly and enable a wider deployment of this technology in clinical settings
Performance Comparison between Bluetooth Mesh Network with and without Preemption in Relay Nodes
No full textA performance comparison between two different implementations of message management of the scanning and advertising phases in relay nodes on Bluetooth Mesh network is analyzed. The main difference between the two is the introduction of a preemption principle in the management. The metric used is the end-to-end packet delivery ratio between individual source-destination pairs. The comparison examines different network topologies in terms of the number of transmission sources and coverage range, but also takes into account the influence of the width of the relay nodes' scan interval, which is closely related to the implementations examined. For the purposes of this work the Bluetooth Mesh compliant simulator provided by Mathworks has been used. The experimental results have shown that the use of message management without preemption allows better reliability performance than the case with preemption in every situation considered
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