Exploring the Long Tail

The migration of datasets online has created a near-infinite inventory for big name retailers such as Amazon and Netflix, giving rise to recommendation systems to assist users in navigating the massive catalog. This has also allowed for the possibility of retailers storing much less popular, uncommon items which would not appear in a more traditional brick-and-mortar setting due to the cost of storage. Nevertheless, previous work has highlighted the profit potential which lies in the so-called long tail\u27\u27 of niche, unpopular items. Unfortunately, due to the limited amount of data in this subset of the inventory, recommendation systems often struggle to make useful suggestions within the long tail, lending them prone to a popularity bias. Our work explores different approaches which recommendation systems typically employ and evaluate the performance of each approach on various subsets of the Netflix Prize data to the end of determining where each approach performs best. We survey collaborative filtering approaches, content-based filtering approaches, and hybrid mechanisms utilizing both of the previous methods. We analyze their behavior on the most popular items, the least popular items, and a composite of the two subsets, and we judge their performance based on the quality of the clusters they produce

Measurement of the magnetic anisotropy energy constants for magneto-optical recording media

Measurement of the magneto-optical polar Kerr effect is performed on rare earth-transition metal (RE-TM) amorphous films using in-plane fields. From this measurement and the measurement of the saturation magnetization using a vibrating sample magnetometer (VSM), the magnetic anisotropy constants are determined. The temperature dependence is presented of the magnetic anisotropy in the range of -175 to 175 C. The results show a dip in the anisotropy near magnetic compensation. This anomaly is explained based on the finite exchange coupling between the rare earth and transition metal subnetworks

Near-capacity MIMOs using iterative detection

In this thesis, Multiple-Input Multiple-Output (MIMO) techniques designed for transmission over narrowband Rayleigh fading channels are investigated. Specifically, in order to providea diversity gain while eliminating the complexity of MIMO channel estimation, a Differential Space-Time Spreading (DSTS) scheme is designed that employs non-coherent detection. Additionally, in order to maximise the coding advantage of DSTS, it is combined with Sphere Packing (SP) modulation. The related capacity analysis shows that the DSTS-SP scheme exhibits a higher capacity than its counterpart dispensing with SP. Furthermore, in order to attain additional performance gains, the DSTS system invokes iterative detection, where the outer code is constituted by a Recursive Systematic Convolutional (RSC) code, while the inner code is a SP demapper in one of the prototype systems investigated, while the other scheme employs a Unity Rate Code (URC) as its inner code in order to eliminate the error floor exhibited by the system dispensing with URC. EXIT charts are used to analyse the convergence behaviour of the iteratively detected schemes and a novel technique is proposed for computing the maximum achievable rate of the system based on EXIT charts. Explicitly, the four-antenna-aided DSTSSP system employing no URC precoding attains a coding gain of 12 dB at a BER of 10-5 and performs within 1.82 dB from the maximum achievable rate limit. By contrast, the URC aidedprecoded system operates within 0.92 dB from the same limit.On the other hand, in order to maximise the DSTS system’s throughput, an adaptive DSTSSP scheme is proposed that exploits the advantages of differential encoding, iterative decoding as well as SP modulation. The achievable integrity and bit rate enhancements of the system are determined by the following factors: the specific MIMO configuration used for transmitting data from the four antennas, the spreading factor used and the RSC encoder’s code rate.Additionally, multi-functional MIMO techniques are designed to provide diversity gains, multiplexing gains and beamforming gains by combining the benefits of space-time codes, VBLASTand beamforming. First, a system employing Nt=4 transmit Antenna Arrays (AA) with LAA number of elements per AA and Nr=4 receive antennas is proposed, which is referred to as a Layered Steered Space-Time Code (LSSTC). Three iteratively detected near-capacity LSSTC-SP receiver structures are proposed, which differ in the number of inner iterations employed between the inner decoder and the SP demapper as well as in the choice of the outer code, which is either an RSC code or an Irregular Convolutional Code (IrCC). The three systems are capable of operating within 0.9, 0.4 and 0.6 dB from the maximum achievable rate limit of the system. A comparison between the three iteratively-detected schemes reveals that a carefully designed two-stage iterative detection scheme is capable of operating sufficiently close to capacity at a lower complexity, when compared to a three-stage system employing a RSC or a two-stage system using an IrCC as an outer code. On the other hand, in order to allow the LSSTC scheme to employ less receive antennas than transmit antennas, while still accommodating multiple users, a Layered Steered Space-Time Spreading (LSSTS) scheme is proposed that combines the benefits of space-time spreading, V-BLAST, beamforming and generalised MC DS-CDMA. Furthermore, iteratively detected LSSTS schemes are presented and an LLR post-processing technique is proposed in order to improve the attainable performance of the iteratively detected LSSTS system.Finally, a distributed turbo coding scheme is proposed that combines the benefits of turbo coding and cooperative communication, where iterative detection is employed by exchanging extrinsic information between the decoders of different single-antenna-aided users. Specifically, the effect of the errors induced in the first phase of cooperation, where the two users exchange their data, on the performance of the uplink in studied, while considering different fading channel characteristics

Element-based Lattice Reduction aided K-Best detector for large-scale MIMO systems

Recently, large-scale Multiple-Input Multiple-Output (MIMO) systems have caught great attention for increasing the system throughput as well as improving the system performance. The main challenge in the design of these MIMO systems is the detection techniques used at the receiver. Lattice Reduction (LR) techniques have shown good potential in MIMO decoding due to their good performance and low complexity compared to Maximum Likelihood (ML) detector. The Lenstra, Lanstra, and Lovasz (LLL) LR algorithm has been employed for decoding while combined with linear detectors such as ZF as well as with K-Best detection. However, the LLL-aided detectors have shown limited performance, when increasing the number of antennas at the transmitter and receiver. Therefore, in this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better BER performance and lower complexity than the LLL-aided detection. Explicitly, the ELR-aided detectors are capable of attaining a 2 dB performance improvement at BER of 10-5 compared to the LLL-aided detectors when considering a MIMO system with 200 transmit and receive antennas. Furthermore, for the same MIMO configuration, the ELR basis update requires nearly an order of magnitude reduction in the number of arithmetic operations compared to the LLL algorithm

Energy-efficient hardware implementation of LR-aided K-Best MIMO decoder for 5G networks

Energy efficiency is a primary design goal for future green wireless communication technologies. Multiple-input multiple-output (MIMO) schemes have been proposed in literature to improve the throughput of communication systems, they are expected to play a prominent role in the upcoming 5th generation (5G) standard. This paper presents a novel high efficiency MIMO decoder based on the K-Best algorithm with lattice reduction. We have designed a novel hardware architecture for this decoder, which was implemented using 32nm standard CMOS technology. Our results show that the proposed decoder can achieve on average a four-fold reduction in the power costs compared to recently published designs for 5G networks. The throughput of the design is 506 Mbits/sec, which is comparable to existing designs

LPV Modeling and Control for Active Flutter Suppression of a Smart Airfoil

In this paper, a novel technique of linear parameter varying (LPV) modeling and control of a smart airfoil for active flutter suppression is proposed, where the smart airfoil has a groove along its chord and contains a moving mass that is used to control the airfoil pitching and plunging motions. The new LPV modeling technique is proposed that uses mass position as a scheduling parameter to describe the physical constraint of the moving mass, in addition the hard constraint at the boundaries is realized by proper selection of the parameter varying function. Therefore, the position of the moving mass and the free stream airspeed are considered the scheduling parameters in the study. A state-feedback based LPV gain-scheduling controller with guaranteed H infinity performance is presented by utilizing the dynamics of the moving mass as scheduling parameter at a given airspeed. The numerical simulations demonstrate the effectiveness of the proposed LPV control architecture by significantly improving the performance while reducing the control effort

Towards the Design of Cold-formed Steel Foam Sandwich Columns

In this paper a design method for the compressive capacity of sandwich panels comprised of steel face sheets and foamed steel cores is derived and verified. Foamed steel, literally steel with internal voids, provides the potential to mitigate many local stability issues through increasing the effective width-to thickness of the component for the same amount of material. Winter’s classical effective width expression was generalized to the case of steel foam sandwich panels. The provided analytical expressions are verified with finite element simulations employing brick elements that explicitly model the steel face sheets and steel foam cores. The closed-form design expressions are employed to conduct parametric studies of steel foam sandwich panels with various face sheet and steel foamed core configurations. The studies show the significant strength improvements possible with steel foam sandwich panels when compared with plain steel sheet/plate