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Agriculture faces several uncertain problems in terms of making the best use of its natural resources. As a result, and in light of the growing threat of changing weather conditions, we must closely track local soil conditions and meteorological data to expedite the adoption of culture-friendly decisions. In the Internet of Things (IoT) era, deploying Wireless Sensor Networks (WSN) as a low-cost remote monitoring and management system for these types of features is a viable choice. However, the WSN is hampered by the motes' insufficient energy sources, which reduces the network's overall lifespan. Each mote collects the tracked feature regularly and sends the data to the sink for further analysis. This method of transmitting large amounts of data requires the sensor node to use a lot of energy and a lot of network bandwidth. We propose a lightweight lossless compression algorithm based on Differential Encoding (DE) and Huffman techniques in this paper, which is especially useful for IoT sensor nodes that track environmental features, especially those with limited computing and memory resources. Rather than attempting to create novel ad hoc algorithms, we show that, given a general understanding of the features to be monitored, classical Huffman coding can be used to effectively represent the same features that measure at different times and locations. Even though the proposed system does not achieve the theoretical limit, results using temperature measurements show that it outperforms standard methods built specifically for WSNs. [ABSTRACT FROM AUTHOR]

Compressive Video Sensing (CVS) is the video coding framework that uses compressive sensing (CS) theory into video codec to reduce the burden of encoding. Various existing methods are developed for CVS, but enhancing reconstruction speed and reconstruction quality remains a great challenge in video compression. Hence, an optimal method is developed using the proposed Differential Pulse Code Modulation+Conditional Autoregressive-Salp Swarm (DPCM+CA-SS) model for CVS system by considering the characteristics of CVS frame. The spatial redundancy of video frames is explored by partitioning the video frames into several blocks such that each block is measured using the measurement matrix. The measurement vectors are quantized using DPCM. The key frames are compressed into bits using Huffman coding, and the packet is transmitted to the decoder side. The temporal redundancy of CVS model is explored by encoding the frames using CA-SS-based prediction model to achieve Motion Aligned (MA) optimal reconstruction. However, the proposed CA-SS is designed by the integration of Conditional Autoregressive Value at Risk (CAViaR) and Salp Swarm Algorithm (SSA), respectively. However, the proposed DPCM+CA-SS obtained maximum PSNR and SSIM of 39.6649 db and 0.9120, and the minimum total time of 4.9523 s, respectively. [ABSTRACT FROM AUTHOR]

Each and every business enterprises require noise-free and clean data. There is a chance of an increase in dirty data as the data warehouse loads and refreshes a large quantity of data continuously from the various sources. Hence, in order to avoid the wrong conclusions, the data cleaning process becomes a vital one in various data-connected projects. This paper made an effort to introduce a novel data cleaning technique for the effective removal of dirty data. This process involves the following two steps: (i) dirty data detection and (ii) dirty data cleaning. The dirty data detection process has been assigned with the following process namely, data normalization, hashing, clustering, and finding the suspected data. In the clustering process, the optimal selection of centroid is the promising one and is carried out by employing the optimization concept. After the finishing of dirty data prediction, the subsequent process: dirty data cleaning begins to activate. The cleaning process also assigns with some processes namely, the leveling process, Huffman coding, and cleaning the suspected data. The cleaning of suspected data is performed based on the optimization concept. Hence, for solving all optimization problems, a new hybridized algorithm is proposed, the so-called Firefly Update Enabled Rider Optimization Algorithm (FU-ROA), which is the hybridization of the Rider Optimization Algorithm (ROA) and Firefly (FF) algorithm is introduced. To the end, the analysis of the performance of the implanted data cleaning method is scrutinized over the other traditional methods like Particle Swarm Optimization (PSO), FF, Grey Wolf Optimizer (GWO), and ROA in terms of their positive and negative measures. From the result, it can be observed that for iteration 12, the performance of the proposed FU-ROA model for test case 1 on was 0.013%, 0.7%, 0.64%, and 0.29% better than the extant PSO, FF, GWO, and ROA models, respectively. [ABSTRACT FROM AUTHOR]

This work proposes a lossless data compression algorithm for short data blocks. The proposed compression scheme combines a modified move-to-front algorithm with Huffman coding. This algorithm is applicable in storage systems where the data compression is performed on block level with short block sizes, in particular, in non-volatile memories. For block sizes in the range of 1kB, it provides a compression gain comparable to the Lempel-Ziv-Welch algorithm. Moreover, encoder and decoder architectures are proposed that have low memory requirements and provide fast data encoding and decoding. [ABSTRACT FROM AUTHOR]

Since different regions of an image have different importance, therefore only the important information of the image regions, which the users are really interested in, needs to be encrypted and protected emphatically in some special multimedia applications. However, the regions of interest (ROI) are always some irregular parts, such as the face and the eyes. Assuming the bulk data in transmission without being damaged, we propose a chaotic image encryption algorithm for ROI. ROI with irregular shapes are chosen and detected arbitrarily. Then the chaos-based image encryption algorithm with scrambling, S-box and diffusion parts is used to encrypt the ROI. Further, the whole image is compressed with Huffman coding. At last, a message authentication code (MAC) of the compressed image is generated based on chaotic maps. The simulation results show that the encryption algorithm has a good security level and can resist various attacks. Moreover, the compression method improves the storage and transmission efficiency to some extent, and the MAC ensures the integrity of the transmission data. [ABSTRACT FROM AUTHOR]

In manifold learning, the neighborhood is often called a patch of the manifold, and the corresponding open set is called the local coordinate of the patch. The so-called alignment is to align the local coordinates in the d-dimensional Euclidean space to get the global coordinate of the manifold. There are two kinds of alignment methods: global and progressive alignment methods. The global alignment methods align the local coordinates of the manifold all at one time by solving an eigenvalue problem. The progressive alignment methods often take the local coordinate of a patch as the basic local coordinate and then attach other local ordinates to the basic local coordinate patch-by-patch until the basic local coordinate evolves into the global coordinate of the manifold. In this paper, a new progressive alignment method is proposed, where only the local coordinates of the two patches with the largest intersection at the current stage of progressive alignment will be aligned into a larger local coordinate. It is inspired by the famous Huffman coding, where two random events with the smallest probabilities at the current phase will be merged into a random event with a larger probability. Therefore, the proposed method is a Huffman-like alignment method. The experiments on benchmark data show that the proposed method outperforms both the global alignment methods and the other progressive alignment methods and is more robust to the changes of data size. The experiments on real-world data show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we present new results on sequential and parallel construction of optimal and almost-optimal length-restricted prefix-free codes. We show that length-restricted prefix-free codes with error 1/nk for any k > 0 can be constructed in O(n log n) time, or in O(log n) time with n CREW processors. A length-restricted code with error 1/nk for any k ≤ L/ logΦ n, where $\Phi = (1 + \sqrt{5})/2$, can be constructed in O(log n) time with n/ log n CREW processors. We also describe an algorithm for the construction of optimal length-restricted codes with maximum codeword length L that works in O(L) time with n CREW processors. [ABSTRACT FROM AUTHOR]

We present an algorithm for parallel construction of Huffman codes in O([This equation cannot be presented in ASCII format] log p) time with p processors, where p > 1, improving the previous result of Levcopoulos and Przytycka. We also show, that a greedy Huffman tree can be constructed in O(√n log n) time with n processors. [ABSTRACT FROM AUTHOR]