In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.

Conventional TCP is window based, which exploits the sliding window mechanism to conduct the flow control. It increases the sending window additively and decreases the sending window multiplicatively in response to successful transmission and, packet loss/timeout events respectively. While the mechanism works quite well in normal networks, TCP can hardly reach the ideal bandwidth utilization in long fat networks (LFNs) due to long delay and bursts of packet losses. Besides, as wireless and mobile computing has become popular today, packet loss in such networks may occur due to noise, interference and handoff across different domains. TCP could not react to different situations effectively as it sees all packet losses as an indication of network congestion. In this thesis, we proposed a new transmission control mechanism called Active Rate Anchoring TCP (ARCH-TCP). In ARCH-TCP, we explicitly integrate bandwidth measurement into TCP to solve the aforementioned problem. Specifically, we exploit packet-pair measurement to quickly estimate bandwidth share and then RTT variation is observed to compensate measurement error. We built the model in J-Sim network simulator to evaluate the effectiveness of our proposal. We found that ARCH-TCP can react to network conditions quickly and precisely in both wired and wireless networks and both in the normal networks and LFNs.

Symmetrical Multilevel Diversity Coding (SMDC) is a network compression problem for which a simple separate coding strategy known as superposition coding is optimal in terms of achieving the entire admissible rate region. Carefully constructed induction argument along with the classical subset entropy inequality of Han played a key role in proving the optimality. This thesis considers a generalization of SMDC for which, in addition to the randomly accessible encoders, there is also an all-access encoder. It is shown that superposition coding remains optimal in terms of achieving the entire admissible rate region of the problem. Key to our proof is to identify the supporting hyperplanes that define the boundary of the admissible rate region and then build on a generalization of Han's subset inequality. As a special case, the (R0,Rs) admissible rate region, which captures all possible tradeoffs between the encoding rate, R0, of the all-access encoder and the sum encoding rate, Rs, of the randomly accessible encoders, is explicitly characterized. To provide explicit proof of the optimality of superposition coding in this case, a new sliding-window subset entropy inequality is introduced and is shown to directly imply the classical subset entropy inequality of Han.

Orthogonal frequency division multiplexing (OFDM) technique has been extensively used in digital wireless communications, such as Digital Broadcasting and wireless local area network (WLAN). It is considered to be one of the most promising techniques for transmission on the downlinks of broadband wireless access systems to combat multipath and multiple access interference (MAI). Spatial processing that exploits the diversity provided by smart antenna (SA) or intelligent antenna (IA) arrays, in which the adaptive beamformer is employed, is another alternatives to increase the efficiency of wireless system capacity and performance without allocating additional frequency spectrum. It allows the system to make full use of spatial diversity due to multiple antennas [5][6]. To further improve the performance for suppressing various interference sources; including narrowband and wideband interference, flat and frequency selective fading, for different channel environmentin. In this thesis, a smart antenna with wideband beamspace approach array beamformer associated with the slideing window (SW) linearly constrained RLS (SW-LC-RLS) algorithm, and the OFDM systems with smart antenna array are emhasized. Computer simulation results confirmed that our proposed scheme could achieve desired performance compared with the conventional approach, in terms of MAI and other interference suppression.

Spatial processing that exploits the diversity provided by smart antenna arrays, in which the adaptive beamformer is employed, is another alternative to increase the efficiency of wireless system capacity and performance without allocating additional frequency spectrum. An array beamformer is a processor used in conjunction with an array of sensors to provide a versatile form of spatial filtering; it can be designed to form main lobe in direction corresponding to the desired source and nulling the interferences from others direction. They are two types of adaptive array beamformer structures, viz., broadband and narrowband array structures. To deal with the wideband desired signal or interferences the broadband array beamformer is preferred. For broadband interferences suppression, many adaptive array beamforming algorithms, based on the linearly constrained have been extensively used. In this thesis, the beamspace approach for designing the broadband antenna array beamformer, with frequency invariant character, is devised and implemented with the sliding window linearly constrained RLS (SW-LC-RLS) algorithm, to deal with the broadband moving jammers (or interferences) suppression. Also, to combat the pointing error effect of desired user¡¦s look direction, the derivative constraint is adopted for devising the derivative SW-LC-RLS beamforming algorithm for broadband moving jammers suppression. Computer simulation results confirmed that the proposed scheme is more robust against the moving jammers over the conventional algorithms. It can be applied to the existing wideband wireless communications systems to achieve desired performance for supporting high data rate communication services.

The direct sequence (DS) code division multiple access (CDMA) is one of the most promising multiplexing technologies for wireless communications. It is also a core technology used in the wideband CDMA (WCDMA) system for the third generation (3G) wireless communication systems. In practice, in the CDMA systems the incomplete orthogonal of the spreading codes between users may introduce the so-called multiple access interference (MAI). Usually, the near-far problem exists when the interfering users are assigned powers much higher than the desired user. Such that the system performance might degrade, dramatically, and thus limits the system capacity. To circumvent the above-mentioned problems many effective adaptive multiuser detectors, based on the minimum mean square error (MMSE) and the minimum output energy (MOE) criteria subject to certain constraints have been proposed. In addition, to mitigate multipath fading effect, RAKE receiver was adopted due to the advantages of path diversity, thus, enhances the system performance. To implement the blind adaptive multiuser detector the linearly constrained minimum variance (LCMV), which is the constrained version of MOE, has been suggested. Further, the LCMV-based receivers exhibit high sensitivity to the channel mismatch caused by the unreliable estimation. To deal with this problem the constant modulus (CM) criterion was considered. In this dissertation, to deal with diverse phenomena encountered in practical channels, we first propose new blind adaptive multi-user detectors, based on the Min/Max criterion associated with the LCCM approach. For implementation the LC exponential window (EW) recursive least-square (RLS) algorithm is derived, and is referred to as the EW LCCM-RLS receiver. It can be used to effectively suppress the MAI and ISI, simultaneously, over multipath fading channels and are robust to mismatch problem caused by inaccuracies in the acquisition of timing and spreading code of the desired user. To reduce the complexity of the above-mentioned blind adaptive multi-user receiver with the LCCM-RLS algorithm, the so-called generalized sidelobe-canceller (GSC) structure is adopted, results in obtaining new CM-GSC-RLS algorithm. Moreover, to further improve the system performance for multipath fading and time-varying channel, the sliding window (SW) LCCM-RLS and SW CM-GSC-RLS algorithms are developed. It can be employed for multipath fading channel with the rapidly changing strong narrowband interference (NBI), which is joined suddenly to the CDMA systems. To look more inside the effect of selecting the initial value of the input signals autocorrelation matrix, some theoretical analyses for the SW LC-RLS as well as EW LC-RLS are provided. Since, unfortunately, the LCCM criterion is known to highly depend on the exact knowledge of the desired user amplitude that is not known exactly at receiver. In the final of this dissertation, a novel linearly constrained adaptive constant modulus RLS (LC-ACM-RLS) algorithm for blind DS-CDMA receiver is proposed. With this new proposed LC-ACM-RLS algorithm, the amplitude variation of the desired user, due to changing characteristics of the channel, can be tracked adaptively. Thus, better performance achievement, in terms of output signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER), over the conventional LCCM-LMS and LCCM-RLS algorithms can be expected.

The technique of direct sequence code division multiple access (DS-CDMA) cellular system has been the focus of increased attention. In this thesis, we will consider the environment of DS-CDMA systems, where the asynchronous narrow band interference due to other systems is joined suddenly to the CDMA system. The suddenly joined narrow band interference will make the system crush down. The main concern of this thesis is to deal with suddenly joined narrow band interference cancellation. An adaptive filtering algorithm based on sliding window criterion and variable forgetting factor is known to be very attractive for violent changing environment. In this thesis, a new sliding window linearly constrained recursive least squares (SW LC-RLS) algorithm and variable forgetting factor linearly constrained recursive least squares (VFF LC-RLS) algorithm on the modified minimum mean squared error (MMSE) structure [9] is devised for RAKE receiver in direct sequence code-division multiple access (DS-CDMA) system over multipath fading channels. Where the channel estimation scheme is accomplished at the output of adaptive filter. The proposed SW LC-RLS algorithm and VFF LC-RLS has the advantage of having faster convergence property and tracking ability, and can be applied to the environment, where the narrow band interference is suddenly joined to the system, to achieve desired performance. Via computer simulation, we show that the performance, in terms of mean square errors (MSE) and signal to interference plus noise ratio (SINR), is superior to the conventional LC-RLS and orthogonal decomposition-based LMS algorithms based on the MMSE structure [9].

Title: Suffix Graphs and Lossless Data Compression Author: Martin Senft Department: Department of Software and Computer Science Education Supervisor of the doctoral thesis: doc. RNDr. Tomáš Dvorˇák, CSc., Depart- ment of Software and Computer Science Education Abstract: Suffix tree and its variants are widely studied data structures that enable an efficient solution to a number of string problems, but also serve for implementation of data compression algorithms. This work explores the opposite approach: design of compression methods, based entirely on prop- erties of suffix graphs. We describe a unified construction algorithm for suf- fix trie, suffix tree, DAWG and CDAWG, accompanied by analysis of implicit suffix link simulation that yields two practical alternatives. Since the com- pression applications require maintaining text in the sliding window, an in- depth discussionof slidingsuffixgraphsisneeded. Fillinggapsin previously published proofs, we verify that suffix tree is capable of perfect sliding in amortised constant time. On the other hand, we show that this is not the case with CDAWG, thus resolving a problem of Inenaga et al. Building on these investigations,we describea family of data compression methods,based on a description of suffix tree construction for the string to be compressed. While some of...

This thesis considers the case of mobile crime-reporting systems that have emerged as an effective and efficient data collection method in low and middle-income countries. Analyzing the data, can be helpful in addressing crime. Since law enforcement agencies in resource-constrained context typically do not have the expertise to handle these tasks, a cost-effective strategy is to outsource the data analytics tasks to third-party service providers. However, because of the sensitivity of the data, it is expedient to consider the issue of privacy. More specifically, this thesis considers the issue of finding low-intensive computational solutions to protecting the data even from an "honest-but-curious" service provider, while at the same time generating datasets that can be queried efficiently and reliably. This thesis offers a three-pronged solution approach. Firstly, the creation of a mobile application to facilitate crime reporting in a usable, secure and privacy-preserving manner. The second step proposes a streaming data anonymization algorithm, which analyses reported data based on occurrence rate rather than at a preset time on a static repository. Finally, in the third step the concept of using privacy preferences in creating anonymized datasets was considered. By taking into account user preferences the efficiency of the anonymization process is improved upon, which is beneficial in enabling fast data anonymization. Results from the prototype implementation and usability tests indicate that having a usable and covet crime-reporting application encourages users to declare crime occurrences. Anonymizing streaming data contributes to faster crime resolution times, and user privacy preferences are helpful in relaxing privacy constraints, which makes for more usable data from the querying perspective. This research presents considerable evidence that the concept of a three-pronged solution to addressing the issue of anonymity during crime reporting in a resource-constrained environment is promising. This solution can further assist the law enforcement agencies to partner with third party in deriving useful crime pattern knowledge without infringing on users' privacy. In the future, this research can be extended to more than one low-income or middle-income countries.

<p>Digital front-end and Turbo decoder are the two key components in the digital wireless communication system. This thesis will discuss the implementation issues of both digital front-end and Turbo decoder.The structure of digital front-end for multi-standard radio supporting wireless standards such as IEEE802.11n, WiMAX, 3GPP LTE is investigated in the thesis. A top-to-down design methods. 802.11n digital down-converter is designed from Matlab model to VHDL implementation. Both simulation and FPGA prototyping are carried out.As another significant part of the thesis, a parallel Turbo decoder is designed and implemented for 3GPPLTE. The block size supported ranges from 40 to 6144 and the maximum number of iteration is eight.The Turbo decoder will use eight parallel SISO units to reach a throughput up to 150Mits.</p>