Energy-aware lossless data compression

Thermally switchable building envelopes can provide energy savings compared to their traditional static counterparts. For example, in the summertime cooling season the envelope can switch into a thermally conductive state at night to exploit the free cooling of the nighttime air, while switching back into a highly insulating state during the warm daytime. Further benefits can be realized by incorporating phase change materials (PCMs) into the same building envelope between two thermal switches, because the PCM can store that nighttime cooling for later use during the day. Here we use analytical and numerical modelling to investigate the advantages of a switch-PCM-switch building envelope and specifically look to optimize the switching algorithms for the two switches. We frame the problem using dimensionless groups in order to identify the key parameter groupings that determine the response. We then build a lightweight lumped element model with runtime speeds of <0.5 seconds per scenario, which enables a brute force search of over 300,000 potential switching algorithms to identify the one with the highest total and net energy savings. We compare the results of the brute force approach with an algorithm that was proposed previously based on physical intuition [1]. We then conduct a similar brute force investigation of switching algorithms optimizing for average daily cost of energy, focusing on scenarios where time-of-day energy pricing is important. Using the resulting optimal switching algorithms, we present cost and energy savings for several real cities based on their realistic temperature data and time-of-day pricing.

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

Our objective was to examine the reliability of histological diagnosis achieved vis a vis the number of biopsy bits obtained along a single trajectory of the stereotactic needle. A retrospective analysis of stereotactic biopsies performed in a single tertiary care neurosciences center, during a period of 11 years, between 1995 to 2005 was done. The overall diagnostic accuracy achieved on histopathology was correlated with the number of bits obtained by stereotactic biopsy. A total of 86 cases were analyzed, which consisted of 58 males and 28 females. Age ranged from 6 to 75 years, with a mean age of 36.1 years. Twenty percent of the patients were in the pediatric age group and 15% were > or =60 years of age. Most common sites biopsied were thalamus/basal ganglia (55.8%), followed by eloquent areas and other sites. A definitive histological diagnosis was established in 70 cases (diagnostic yield, 81.3%), which encompassed 65 neoplastic and 5 nonneoplastic lesions. Astrocytic lesions, the most common, include 10 pilocytic astrocytomas (PA), 29 diffuse astrocytomas (DA), 11 anaplastic astrocytomas (AA), and 7 glioblastoma multiforme (GBM). In 16 cases no definite histological diagnosis could be offered. The number of biopsies ranged between 1 and 6 bits (mean, 2; median, 1). The majority (68.7%) of the biopsies were 1 or 2-bits. The diagnostic accuracy increased from 76.5% for single biopsies to 84% and 88.2% for 2 and 3 bits, respectively, and 100% for biopsies with 5 to 6 bits. Overall, a trend of higher diagnostic yield was seen in cases with more biopsies when compared with single bit biopsies. Thus, this small series confirms that stereotactic procedures involving multiple bits are associated with a high diagnostic yield.

A fixed-point squaring algorithm is formulated and implemented based on an approach that allows any number of bits to be computed in each iterative step. The primary contribution this new approach offers is the ability for a designer to change the area $\,\times\,$ latency product through the choice of a different radix or number of bits per subword to be processed in each iterative step. When the number of subword bits is increased, latency is reduced since fewer iterations are required whereas the area is increased due to the larger subcircuit for squaring each subword. Alternatively, choosing a smaller subword or radix decreases area at the expense of increasing latency since more iterations are required. The subword size can range from a single bit yielding a bit-serial squarer requiring $N$ iterations for an $N$ -bit operand or “squarand,” to using the entire squarand resulting in a fully parallel squarer requiring no iterations. Because each $m$ -bit subword can be considered a single digit in a number system with radix $2^{m}$ , the squarer presented here can be considered a multiple-valued logic (MVL) digit-serial architecture. This methodology allows for technologies based on any radix of two or greater to be used, including emerging technologies, thus yielding a true multiple-valued logic squaring circuit. The algorithm is derived through the generalization of a Vedic technique where any arbitrary integer-valued radix is used. Prototype hardware implementations using both a standard cell ASIC and FPGA technologies are developed. The prototype circuits are analyzed in terms of required resources and throughput characteristics and compared to a well-known prior art squaring circuit.

This paper studies the stabilizability of an n-dimensional quantized feedforward nonlinear system. The state of that system is first quantized into a finite number of bits, and then sent through a digital network to the controller. We want to minimize the number of transmitted bits subject to maintaining asymptotic stability. In the prior literature, n bits are used to stabilize the n-dimensional system by assigning one bit to each state variable (dimension). Under the stronger assumption of global Lipschitz continuity, this paper extends that result by stabilizing the system with a single bit. Its key contribution is a dynamic quantization policy which dynamically assigns the single bit to the most “important” state variable. Under this policy, the quantization error exponentially converges to 0 and the stability of the system can, therefore, be guaranteed. Because 1 is the minimum number of quantization bits (per sampling step), the proposed dynamic quantization policy achieves the minimum stabilizable bit number for that n-dimensional feedforward nonlinear system.

: This report presents the results of an energy evaluation of the Central Food Preparation System (CFPS) and three dining halls at Fort Lee, Virginia. As far as cooking was concerned, there were energy savings in cooking large batches of food with large equipment at CFPS vs. cooking small batches of food with small equipment at the individual dining halls. However, due to the extra steps required for freezing, storing, and reheating of the CFPF prepared entrees, there was an additional energy expenditure as compared to foods freshly prepared and served in dining halls. Concerning the preparation of salads, gelatin desserts, and slicing of cold-cut meats in the Ingredient Preparation Facility (IPF), there was a net energy savings in using IPF-prepared foods because of no cook-freeze system involved. This report also presents data on comparisons of electricity vs. gas cooking and large batches vs. small batches. Considering different energy costs, electricity is ranked as the major energy cost, and the cost of heating water is second.

This study develops estimates of energy use and recovery from managing municipal solid waste (MSW) under various collection, processing, and disposal scenarios. We estimate use and recovery -- or energy balance -- resulting from MSW management activities such as waste collection, transport, processing, and disposal, as well as indirect use and recovery linked to secondary materials manufacturing using recycled materials. In our analysis, secondary materials manufacturing displaces virgin materials manufacturing for 13 representative products. Energy implications are expressed as coefficients that measure the net energy saving (or use) of displacing products made from virgin versus recycled materials. Using data developed for the 1992 New York City Master Plan as a starting point, we apply our method to an analysis of various collection systems and 30 types of facilities to illustrate bow energy balances shift as management systems are modified. In sum, all four scenarios show a positive energy balance indicating the energy and advantage of integrated systems versus reliance on one or few technology options. That is, energy produced or saved exceeds the energy used to operate the solid waste system. The largest energy use impacts are attributable to processing, including materials separation and composting. Collection and transportation energy are relatively minor contributors. The largest two contributors to net energy savings are waste combustion and energy saved by processing recycled versus virgin materials. An accompanying spatial analysis methodology allocates energy use and recovery to New York City, New York State outside the city, the U.S., and outside the U.S. Our analytical approach is embodied in a spreadsheet model that can be used by energy and solid waste analysts to estimate impacts of management scenarios at the state and substate level.

&lt;div class="htmlview paragraph"&gt;The development of energy efficient, lightweight sorption systems for removal of environmental contaminants in space flight applications is an area of continuing interest to NASA. The current CO&lt;sub&gt;2&lt;/sub&gt; removal system on the International Space Station employs two pellet bed canisters of 5A molecular sieve that alternate between regeneration and sorption. A separate disposable charcoal bed removes trace contaminants.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;An alternative technology has been demonstrated using a sorption bed consisting of metal meshes coated with a sorbent, trademarked and patented [&lt;span class="xref"&gt;1&lt;/span&gt;] as Microlith&lt;sup&gt;®&lt;/sup&gt; by Precision Combustion, Inc. (PCI); these meshes have the potential for direct electrical heating for this application. This allows the bed to be regenerable via resistive heating and offers the potential for shorter regeneration times, reduced power requirement, and net energy savings vs. conventional systems. The capability of removing both CO&lt;sub&gt;2&lt;/sub&gt; and trace contaminants within the same bed has also been demonstrated. Thus, the need for a separate trace contaminant unit is eliminated resulting in an opportunity for significant weight savings. Unlike the charcoal bed, zeolites for trace contaminant removal are amenable to periodic regeneration. This paper describes the design and performance of a prototype sorber device for simultaneous CO&lt;sub&gt;2&lt;/sub&gt; and trace contaminant removal and its attendant weight and energy savings.&lt;/div&gt;

Maximum distance separable (MDS) codes are those codes whose minimum distance attains the Singleton bound for a given length and given dimension. It is a well-known fact that the only binary linear block codes that are MDS are the simple parity codes and the repetition codes. Known MDS codes (the Reed-Solomon codes, for instance) are defined over larger alphabets. This approach requires that (i) the encoding and decoding procedures are performed as operations over a finite field and (ii) an update in a single information bit (e.g. in storage applications) requires an update in the redundancy symbols and usually affects a number of bits in each redundancy symbol. Thus, the optimal redundancy is achieved at the expense of additional complexity in the encoding/decoding procedures as well as in the number of redundancy bits affected by an update in the information. We construct MDS codes that have the following two properties: (i) the redundancy bits are computed by simple XOR operations; and (ii) an update in an information bit affects a minimal number of redundancy bits. >

Development and application of a building energy performance metric for green roof systems

Optimal sizing of grid-connected photovoltaic system for a large commercial load in Saudi Arabia

Given a finite sequence x1, x2, .. . , x, the essential problem in lossless data compression is to process the symbols in some order and assign a conditional probability distribution for the current symbol based on the previously processed symbols [44]. For example, if we are to process x1, x2, .. . , x in a sequenal manner [53] then we need to estimate the distributions p(x+iIxi, x2, . . . , xe), 1 < j < fl The number of bits needed to optimally encode the sequence x1, . . , x, is then given by —log flp(x+iIx,...,x). Coding techniques that can encode the sequence at rates close to the optimal are known [43]. Hence, higher the probabilities assigned in the above product, the lesser the number of bits that are needed to encode the sequence. A model, in this context is then simply a scheme for assigning conditional probability distributions [53]. Clearly, it is the model that determines the rate at which we can encode the sequence. Hence the critical task in lossless data compression is finding good models for the data under consideration. Finding good models for a given data set is a difficult problem. In lossless compression applications some structure is usually imposed on the data in the form offinile-state models, Markov-models, tree-models, finiie-con1ex models etc to make the problem mathematically and/or computationally tractable. Algorithms are then designed that encode the given data, in an optimal or sub-optimal manner. Algorithms that 'learn' the parameters of the model that best describes the data and achieve rates that are asymptotically optimal are known as oplimal universal coding schemes. Such schemes have been applied very successfully in text or string compression applications. Unfortunately, universal coding schemes and other standard modelling techniques do not work well in practice when applied to gray-scale image data.

Efficient utilization of energy is a core area of research in wireless sensor networks. Data compression methods to reduce the number of bits to be transmitted by the communication module will significantly reduce the energy requirement and increase the lifetime of the sensor node. Based on the lifting scheme 2-point discrete cosine transform (DCT), this paper proposed a new reversible recursive algorithm named Difference-Median-Difference (DMD) transform for lossless data compression in sensor node. The DMD transform can significantly reduce the spatio-temporal correlations among sensor data and can smoothly run in resource limited sensor nodes. Through an entropy encoder, the results of DMD transform can be compressed more compactly based on their statistical characteristics to achieve compression. Compared with the typical lossless algorithms, the proposed algorithm indicated better compression ratios than others for non-slowly-varying data, despite a less computational effort.

Although subband and wavelet coding have been proved to be very efficient for lossy image compression, it is generally not possible to represent the output of most subband/wavelet filters by a limited number of bits and yet guarantee perfect reconstruction. This is a fundamental problem when applying subband and wavelet coding to lossless image compression. This paper presents a solution to this problem by an investigation of interpolative subband coding. Based on an evaluation of the decorrelation performance of some typical image interpolation techniques, a new interpolative coding algorithm has been proposed. The new algorithm has been shown to be superior to two related algorithms, the hierarchical interpolation algorithm and the recently published minimum entropy pyramid algorithm.

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.