Kernel density estimators are the basic tools for density estimation in non-parametric statistics. The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the location of the sample points. In this paper, we initially introduce the k-nearest neighbor kernel density estimator in the random Left-truncation model, and then prove some of its asymptotic behaviors, such as strong uniform consistency and asymptotic normality. In particular, we show that the proposed estimator has truncation-free variance. Simulations are presented to illustrate the results and show how the estimator behaves for finite samples. Moreover, the proposed estimator is used to estimate the density function of a real data set.

Abstract— Network-on-Chips (NoCs) as a standard interconnection impose high latency and excessive power consumption in many-core systems. Emerging data-intensive applications possess a high volume of data movement across the network which deteriorates the network congestion condition. These applications have an intrinsic feature, namely error tolerance, which presents a new communication paradigm. We employ a differential-based approximate method for packet transmission to reduce the packet size on the network. General NoC architectures have a large enough flit channel so that the packet header includes many free bits. As we reduce the packet size by transmitting the difference data on the network, we can accommodate the additional parts of the header to store the difference data that must be transmitted. This approach in data storage and transmission optimizes the packet size, which reduces the network congestion by using the idle space of head flit and employing approximate-based data transmission. We apply this method in 3D NoC due to its low latency architecture. Therefore, we could alleviate 3D NoC thermal challenges. The simulation results show that our approximate-based NoC architecture decreases the latency and dynamic power consumption by 37% and 42% in comparison to traditional 3D NoC, respectively.

Abstract— Network-on-Chips (NoCs) as a standard interconnection impose high latency and excessive power consumption in many-core systems. Emerging data-intensive applications possess a high volume of data movement across the network which deteriorates the network congestion condition. These applications have an intrinsic feature, namely error tolerance, which presents a new communication paradigm. We employ a differential-based approximate method for packet transmission to reduce the packet size on the network. General NoC architectures have a large enough flit channel so that the packet header includes many free bits. As we reduce the packet size by transmitting the difference data on the network, we can accommodate the additional parts of the header to store the difference data that must be transmitted. This approach in data storage and transmission optimizes the packet size, which reduces the network congestion by using the idle space of head flit and employing approximate-based data transmission. We apply this method in 3D NoC due to its low latency architecture. Therefore, we could alleviate 3D NoC thermal challenges. The simulation results show that our approximate-based NoC architecture decreases the latency and dynamic power consumption by 37% and 42% in comparison to traditional 3D NoC, respectively.

Myocardial perfusion SPECT is one of the most common imaging techniques performed in nuclear medicine departments. To avoid misleading interpretation, it is necessary to address the quality control and technical problems. The truncation artifact occurs when the patient size is large relative to the field of view of the camera, causing false perfusion defects in the LV myocardium, misinterpreted as myocardial perfusion abnormality. It usually happens in obese patients, who may deviate from the detector field. Here, we present a skinny patient showing myocardial truncation artifact, proved to be because of technical issues.

Image compression and Image processing are the two aspects that affect image specific e-learning environment. In this regard, there are various methods proposed to process and compress the image effectively. Recent works mainly concentrate on finding the memory complexity and processing complexity of various techniques. According to that, block truncation models are widely applied over various e-learning fields. Block truncation Model (BTM) considers the images as a collection of individual blocks to be processed. These blocks are extracted and evaluated for image compression. To compress the images, the least important blocks need to be ignored or suppressed. At this stage, standard BTC, Absolute Moment BTC (AMBTC), Machine Learning (ML) based BTC and Deep Learning (DL) based BTC techniques have emerged from various resources. This work is analyzing various BTC models in terms of time efficiency, memory efficiency and computation efficiency. The results shown in this work reveal the detailed comparisons of e-learning based block truncation models.