No abstract

Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.

This article presents fast online placement methods for dynamically reconfigu ra ble systems, as well as offline 3D placement algorithms for static a I l y r e m nf i g U r a b I e arch it e c t u res , As FPGAs get larger and faster, both the number and complexity of the modules to load on them increase, hence better speedups can be achieved by exploiting FPGAs in hardware systems. Eokhale et al.$ report speedups of 200 times for the string matching problem. Adario et al.] achieve three times the pipelined implementation of image processing applications by exploiting dynamic reconfiguration of the hardware. Furthermore, the ability to reconfigure the chip as it is running enables the implementation of dynamically reconfigurable hardware systems that adapt themselves to the application for better p e i I~r m a n c e .~,~~,~~ Hauclc has reported many applications in reconfigurable systeins.ll Such systems usually consist of a host processor and an FPGA "coprocessor" called a reconfigurable functional unit (RFU). The RFU can be programmed in fhe course of the running time of dre program, with valying configurations in different stages oi the program.An example is shown i n Figure 1. As shown in Figure la, three parts of the code are mapped to RFU operations (RFUOPs, also called modules). When the program is running the Imp containing RWOP2 (time fl), two RFUOPs are loaded on the chip. Later, when the program is about to enter the loop at time 12, there is no space on the RFU to place RFUOPJ. Hence, RFUOPS is swapped out of the chip, and RFUOP3 is loaded. RFUOPl is still on the chip and can be reused later in the program.Unforlunately, raCher long delays in reprogramming RFUs keep 11s from achieving very high speedups for general-purpose computing? Wirtlilin ancl H~t c h i n g s~~ report an overall speedup of 23 times, while the speedup could, be 80 times i t configuration time was zero (the configuration time is 16% to 71% of the total running time).We need fast and powerful physical design CAD tools to do configuration management of the RFUs both offline and online. In the offline version, the flow of the program is known in advance (e.g., in DSP applications or loops containing basic blocks); hence, ihcscheduler and configuration management component can do various optimizations in the configuration of the RFU before the system starts running, On the contrary, in the online version, the decision on what operations should be launched is not known beforehand. The flow of the program is not known in advance; hence, the RFU configuration management should be done on the fly. An example of such a case is multithreading, in which the flow of the code cannot be determined beforehand.Both online and offline versions of the template placement algorithms are important for 68 0744-7476100/$10,00 @ZOO0 l€EE IEEE Deslgn & last of Computers

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques.

Background Paralysis of the upper-limbs from spinal cord injury results in an enormous loss of independence in an individual’s daily life. Meaningful improvement in hand function is rare after one year of tetraparesis. Therapeutic developments that result in even modest gains in hand volitional function will significantly impact the quality of life for patients afflicted with high cervical injury. The ability to neuromodulate the lumbosacral spinal circuitry via epidural stimulation in regaining postural function and volitional control of the legs has been recently shown. A key question is whether a similar neuromodulatory strategy can be used to improve volitional motor control of the upper-limbs, i.e., performance of motor tasks considered to be less “automatic” than posture and locomotion. In this study, the effects of cervical epidural stimulation on hand function are characterized in subjects with chronic cervical cord injury. Objective Herein we show that epidural stimulation can be applied to the chronic injured human cervical spinal cord to promote volitional hand function. Methods and results Two subjects implanted with an cervical epidural electrode array demonstrated improved hand strength (approximately three-fold) and volitional hand control in the presence of epidural stimulation. Conclusions The present data are sufficient to suggest that hand motor function in individuals with chronic tetraplegia can be improved with cervical cord neuromodulation and thus should be comprehensively explored as a possible clinical intervention.