A Low-Complexity Near-ML Performance Achieving Algorithm for Large MIMO Detection

In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 x 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10(-3) at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q(root SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNt) per symbol, where N-t and N-tau denote the number of transmit and receive antennas.

An efficient algorithm for scheduling in bluetooth piconets and scatternets

Bluetooth is a short-range radio technology operating in the unlicensed industrial-scientific-medical (ISM) band at 2.45 GHz. A piconet is basically a collection of slaves controlled by a master. A scatternet, on the other hand, is established by linking several piconets together in an ad hoc fashion to yield a global wireless ad hoc network. This paper proposes a scheduling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in bluetooth piconets and scatternets. We propose a novel algorithm for scheduling slots to slaves for both piconets and scatternets using multi-layered parameterized policies. Our scheduling scheme works with real data and obtains an optimal feedback policy within prescribed parameterized classes of these by using an efficient two-timescale simultaneous perturbation stochastic approximation (SPSA) algorithm. We show the convergence of our algorithm to an optimal multi-layered policy. We also propose novel polling schemes for intra- and inter-piconet scheduling that are seen to perform well. We present an extensive set of simulation results and performance comparisons with existing scheduling algorithms. Our results indicate that our proposed scheduling algorithm performs better overall on a wide range of experiments over the existing algorithms for both piconets (Das et al. in INFOCOM, pp. 591–600, 2001; Lapeyrie and Turletti in INFOCOM conference proceedings, San Francisco, US, 2003; Shreedhar and Varghese in SIGCOMM, pp. 231–242, 1995) and scatternets (Har-Shai et al. in OPNETWORK, 2002; Saha and Matsumot in AICT/ICIW, 2006; Tan and Guttag in The 27th annual IEEE conference on local computer networks(LCN). Tampa, 2002). Our studies also confirm that our proposed scheme achieves a high throughput and low packet delays with reasonable fairness among all the connections.

Inside out: Why do so many great buildings have drab interiors?

An extravaganza of shapes now forms our city skylines. CAD and BIM with their inbuilt links to manufacturing and construction processes has made possible this kind of effusive architectural expression, at least externally. Building developers clearly understand the enormous marketing potential for impact expression. The skilled manipulation of 3D CAD software enables architects to achieve usable gross floor space within an enticingly sinuous, but build-able, envelope. This critical factor is resulting in a fundamental change to the appearance of our cities. It has become plausible, at least, to design and build complex and non-repetitive buildings without incurring prohibitive additional labor costs.However The ground level lobby spaces often do manage to retain some of the external. However, the interior working spaces, particularly in commercial office buildings tend to loose this grand gesture. However - the internal activity - the very reason for the existence of the building – often takes place in monotonous spaces that seem driven predominately by the need to accommodate workstation furniture and functions in dire need of reconsideration.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A feedback-based algorithm forMotion analysis with application to object tracking

We present a motion detection algorithm which detects direction of motion at sufficient number of points and thus segregates the edge image into clusters of coherently moving points. Unlike most algorithms for motion analysis, we do not estimate magnitude of velocity vectors or obtain dense motion maps. The motivation is that motion direction information at a number of points seems to be sufficient to evoke perception of motion and hence should be useful in many image processing tasks requiring motion analysis. The algorithm essentially updates the motion at previous time using the current image frame as input in a dynamic fashion. One of the novel features of the algorithm is the use of some feedback mechanism for evidence segregation. This kind of motion analysis can identify regions in the image that are moving together coherently, and such information could be sufficient for many applications that utilize motion such as segmentation, compression, and tracking. We present an algorithm for tracking objects using our motion information to demonstrate the potential of this motion detection algorithm.

An efficient and optimized bluetooth scheduling algorithm for piconets

Bluetooth is an emerging standard in short range, low cost and low power wireless networks. MAC is a generic polling based protocol, where a central Bluetooth unit (master) determines channel access to all other nodes (slaves) in the network (piconet). An important problem in Bluetooth is the design of efficient scheduling protocols. This paper proposes a polling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in a Bluetooth Piconet. We present an extensive set of simulation results and performance comparisons with two important existing algorithms. Our results indicate that our proposed scheduling algorithm outperforms the Round Robin scheduling algorithm by more than 40% in all cases tried. Our study also confirms that our proposed policy achieves higher throughput and lower packet delays with reasonable fairness among all the connections.

Developing an Unmanned Aerial Vehicle (UAV) multi-sensor payload carrier for air quality monitoring

Emissions of gases and particles from sea-faring ships have been shown to impact on the atmospheric chemistry and climate. To efficiently monitor and report these emissions found from a ship’s plume, the concept of using a multi-rotor or UAV to hover inside or near the exhaust of the ship to actively record the data in real time is being developed. However, for the required sensors obtain the data; their sensors must face into the airflow of the ships plume. This report presents an approach to have sensors able to read in the chemicals and particles emitted from the ship without affecting the flight dynamics of the multi-rotor UAV by building a sealed chamber in which a pump can take in the surrounding air (outside the downwash effect of the multi-rotor) where the sensors are placed and can analyse the gases safely. Results show that the system is small, lightweight and air-sealed and ready for flight test.

Modern computed tomography techniques in stroke management

Technological development of fast multi-sectional, helical computed tomography (CT) scanners has allowed computed tomography perfusion (CTp) and angiography (CTA) in evaluating acute ischemic stroke. This study focuses on new multidetector computed tomography techniques, namely whole-brain and first-pass CT perfusion plus CTA of carotid arteries. Whole-brain CTp data is acquired during slow infusion of contrast material to achieve constant contrast concentration in the cerebral vasculature. From these data quantitative maps are constructed of perfused cerebral blood volume (pCBV). The probability curve of cerebral infarction as a function of normalized pCBV was determined in patients with acute ischemic stroke. Normalized pCBV, expressed as a percentage of contralateral normal brain pCBV, was determined in the infarction core and in regions just inside and outside the boundary between infarcted and noninfarcted brain. Corresponding probabilities of infarction were 0.99, 0.96, and 0.11, R² was 0.73, and differences in perfusion between core and inner and outer bands were highly significant. Thus a probability of infarction curve can help predict the likelihood of infarction as a function of percentage normalized pCBV. First-pass CT perfusion is based on continuous cine imaging over a selected brain area during a bolus injection of contrast. During its first passage, contrast material compartmentalizes in the intravascular space, resulting in transient tissue enhancement. Functional maps such as cerebral blood flow (CBF), and volume (CBV), and mean transit time (MTT) are then constructed. We compared the effects of three different iodine concentrations (300, 350, or 400 mg/mL) on peak enhancement of normal brain tissue and artery and vein, stratified by region-of-interest (ROI) location, in 102 patients within 3 hours of stroke onset. A monotonic increasing peak opacification was evident at all ROI locations, suggesting that CTp evaluation of patients with acute stroke is best performed with the highest available concentration of contrast agent. In another study we investigated whether lesion volumes on CBV, CBF, and MTT maps within 3 hours of stroke onset predict final infarct volume, and whether all these parameters are needed for triage to intravenous recombinant tissue plasminogen activator (IV-rtPA). The effect of IV-rtPA on the affected brain by measuring salvaged tissue volume in patients receiving IV-rtPA and in controls was investigated also. CBV lesion volume did not necessarily represent dead tissue. MTT lesion volume alone can serve to identify the upper size limit of the abnormally perfused brain, and those with IV-rtPA salvaged more brain than did controls. Carotid CTA was compared with carotid DSA in grading of stenosis in patients with stroke symptoms. In CTA, the grade of stenosis was determined by means of axial source and maximum intensity projection (MIP) images as well as a semiautomatic vessel analysis. CTA provides an adequate, less invasive alternative to conventional DSA, although tending to underestimate clinically relevant grades of stenosis.

Energy efficient area monitoring using information coverage in wireless sensor networks

In this paper, we are concerned with energy efficient area monitoring using information coverage in wireless sensor networks, where collaboration among multiple sensors can enable accurate sensing of a point in a given area-to-monitor even if that point falls outside the physical coverage of all the sensors. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We first propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The scheduling of sensor activity using the optimum schedules obtained using the proposed algorithm is shown to achieve significantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime.

Novel melody line identification algorithm for polyphonic MIDI music

The problem of automatic melody line identification in a MIDI file plays an important role towards taking QBH systems to the next level. We present here, a novel algorithm to identify the melody line in a polyphonic MIDI file. A note pruning and track/channel ranking method is used to identify the melody line. We use results from musicology to derive certain simple heuristics for the note pruning stage. This helps in the robustness of the algorithm, by way of discarding "spurious" notes. A ranking based on the melodic information in each track/channel enables us to choose the melody line accurately. Our algorithm makes no assumption about MIDI performer specific parameters, is simple and achieves an accuracy of 97% in identifying the melody line correctly. This algorithm is currently being used by us in a QBH system built in our lab.

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

Profiling k-Iteration Paths : A Generalization of the Ball-Larus Profiling Algorithm

The Ball-Larus path-profiling algorithm is an efficient technique to collect acyclic path frequencies of a program. However, longer paths -those extending across loop iterations - describe the runtime behaviour of programs better. We generalize the Ball-Larus profiling algorithm for profiling k-iteration paths - paths that can span up to to k iterations of a loop. We show that it is possible to number suchk-iteration paths perfectly, thus allowing for an efficient profiling algorithm for such longer paths. We also describe a scheme for mixed-mode profiling: profiling different parts of a procedure with different path lengths. Experimental results show that k-iteration profiling is realistic.

Modified density matrix renormalization group algorithm for the zigzag spin-1/2 chain with frustrated antiferromagnetic exchange: Comparison with field theory at large J(2)/J(1)

A modified density matrix renormalization group (DMRG) algorithm is applied to the zigzag spin-1/2 chain with frustrated antiferromagnetic exchange J(1) and J(2) between first and second neighbors. The modified algorithm yields accurate results up to J(2)/J(1) approximate to 4 for the magnetic gap Delta to the lowest triplet state, the amplitude B of the bond order wave phase, the wavelength lambda of the spiral phase, and the spin correlation length xi. The J(2)/J(1) dependences of Delta, B, lambda, and xi provide multiple comparisons to field theories of the zigzag chain. The twist angle of the spiral phase and the spin structure factor yield additional comparisons between DMRG and field theory. Attention is given to the numerical accuracy required to obtain exponentially small gaps or exponentially long correlations near a quantum phase transition.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.