There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Energy harvesting is a promising technique to overcome the limit on energy availability and increase the lifespan of battery-powered embedded systems. In this paper, the question of how one can achieve the prolonged lifespan 1 of a real-time embedded system with energy harvesting capability (RTES-EH) is investigated. The RTES-EH comprises a photovoltaic (PV) panel for energy harvesting, a supercapacitor for energy storage, and a real-time sensor node as the embedded load device. A global controller performs simultaneous optimal operating point tracking for the PV panel, state-of-charge (SoC) management for the supercapacitor, and energy-harvesting-aware real-time task scheduling with dynamic voltage and frequency scaling (DVFS) for the sensor node, while employing a precise solar irradiance prediction method. The controller employs a cascaded feedback control structure, where an outer supervisory control loop performs real-time task scheduling with DVFS in the sensor node while maintaining the optimal supercapacitor SoC for improved system availability, and an inner control loop tracks the optimal operating point of the PV panel on the fly. Experimental results show that the proposed global controller lowers the task instance drop rate by up to 63% compared with the baseline controller within the same service time (i.e., from sunrise to sunset). 1 The lifetime of a battery is usually less than five years, and in this paper we propose to use supercapacitor instead of battery for embedded systems to prolong the lifespan of the whole system to more than 20 years.

Concurrent Task Scheduling and Dynamic Voltage and Frequency Scaling in a Real-Time Embedded System With Energy Harvesting

We present LEOPARD, a fanout optimization algorithm based on the effort delay model for near-continuous size buffer libraries. Our algorithm minimizes area under required timing and input capacitance constraints by finding the tree topology and assigning different gains to each buffer to minimize the total buffer area. Experimental results show that the new algorithm achieves significant buffer area improvement compared to previous approaches.

/pdf/leopard-a-logical-effort-based-fanout-optimizer-for-area-and-1jef7ftzvp.pdf

LEOPARD: a Logical Effort-based fanout OPtimizer for ARea and Delay

This paper presents a new algorithm for timing-driven cell placement using the notion of movable Steiner points that capture the net topology. The proposed algorithm improves the timing closure at the backend of the EDA design flow. Unlike conventional flows that perform placement and routing in two separate steps and use rough estimates of the net lengths during placement, our algorithm uses accurate net lengths by considering the net topologies during the Elmore delay calculation step and dynamically updates the routing during the concurrent placement of Steiner points and cells. The simultaneous placement and routing problem is formulated as a mathematical program with a small number of variables and solved by the Han-Powell method. Experimental results demonstrate the effectiveness of the new approach compared to the conventional flows.

/pdf/post-layout-timing-driven-cell-placement-using-an-accurate-l6mhgln44o.pdf

Post-layout timing-driven cell placement using an accurate net length model with movable Steiner points

This paper describes a dynamic voltage and frequency scaling (DVFS) technique for MPEG decoding to reduce the energy consumption while maintaining a quality of service (QoS) constraint. The computational workload for an incoming frame is predicted by using a frame-based history so that the processor voltage and frequency can be scaled to provide the exact amount of computing power needed to decode the frame. More precisely, the required decoding time for each frame is separated into two parts: a frame-dependent (FD) part and a frame-independent (FI) part. The FD part varies greatly according to the type of the incoming frame whereas the FI part remains constant regardless of the frame type. Separation of the FI part from the overall decoding sequence provides two key benefits depending on the hardware platform: better compensation of the error due to workload prediction and higher level of energy saving when given a QoS degradation level. The proposed DVFS scheme has been implemented on two platforms, a low performance StrongArm-1110-based evaluation board from Intel and a high performance XScale-based testbed designed at USC. In the StrongArm-1110-based system, the FI part is used to compensate for the prediction error that may occur during the FD part, whereas in the XScale-based system, the FI part is used to reduce energy consumption by employing the lowest CPU frequency during the corresponding time intervals. Detailed current measurements in these two platforms demonstrate larger than 87 % and 80 % CPU energy saving while maintaining a user-provided frame rate, respectively. Index Terms — Dynamic voltage and frequency scaling, MPEG decoding, workload decomposition.

Frame-Based Dynamic Voltage and Frequency Scaling for an MPEG Player

An algorithm is presented for assigning off-chip I/O pads for a logic circuit. The technique, which is based on the analysis of the circuit structure and path delay constraints, uses linear placement, goal-programming, linear-sum assignment and I/O pad clustering to assign locations to I/O pads. The I/O pad assignment is then used by placement tools. Experimental data show that as a result of using the I/O pad assignment procedure, the total interconnection length and circuit delay (after placement and routing) are reduced by 8-15% and 3-4%, respectively. This technique is general and can handle I/O pad assignment prior to logic synthesis or detailed placement procedures. >

Massoud Pedram

Papers

Concurrent Task Scheduling and Dynamic Voltage and Frequency Scaling in a Real-Time Embedded System With Energy Harvesting

LEOPARD: a Logical Effort-based fanout OPtimizer for ARea and Delay

Post-layout timing-driven cell placement using an accurate net length model with movable Steiner points

Frame-Based Dynamic Voltage and Frequency Scaling for an MPEG Player

I/O pad assignment based on the circuit structure