The capacity of multiple input, multiple output (MIMO) wireless channels is computed for Ricean channels. The novelty is a geometrical (ray-tracing) interpretation of the MIMO channel capacity formula to find array geometries which greatly enhance channel capacity compared to single input-single output (SISO) systems. Index Terms-Array signal processing, channel capacity, land mobile radio cellular systems.

Abstract-We present a general framework to quantify the data throughput capabilities of a wireless communication system when it combines: 1) multiple transmit signals; 2) adaptive modulation for each signal; and 3) adaptive array processing at the receiver. We assume a noise-limited environment, corresponding to either an isolated cell or a multicell system whose out-of-cell interference is small compared with the thermal noise. We focus on the user data throughput, in bits per second/Hertz (bps/Hz), and its average over multipath fading, which we call the user spectral efficiency. First, an analysis method is developed to find the probability distribution and mean value of the spectral efficiency over the user positions and shadow fadings, both as a function of user distance from its serving base station and averaged over the cell coverage area. We assume fading conditions and receiver processing that lend themselves to closed-form analysis. The resulting formulas are simple and straightforward to compute, and they provide a number of valuable insights. Next, we run Monte Carlo simulations, both to confirm the analysis and to treat cases less amenable to simple analysis.A key contribution of this paper is a simple formula for the mean spectral efficiency in terms of the propagation exponent, mean signal-to-noise ratio at the cell boundary, number of antennas, and type of coding. Under typical propagation conditions, the mean spectral efficiency using three transmit and three receive antennas ranges from 19.2 bps/Hz (uncoded) to 26.8 bps/Hz (ideally coded), highlighting the potential benefits of multiple transmissions combined with adaptive techniques. This is much higher than the spectral efficiencies for a link using a single transmitter and a threefold receive diversity under the same conditions, where the range is from 8.77 bps/Hz to 11.4 bps/Hz. Moreover, the latter results are not nearly as practical to achieve, as they call for large signal constellations that would be highly vulnerable to impairments.

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Abstract. This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.

We describe a simulation study of a cellular system using multiple-input multiple-output (MIMO) antenna techniques along with adaptive modulation and aggressive frequency reuse. We show, for the case of 3 transmit and 3 receive antennas, how much MIMO systems outperform systems with receive-diversity-only when noise dominates. When co-channel interference from surrounding cells dominates, the differences shrink, as do the absolute numbers. We quantify these reductions for the specific cases studied, and discuss further areas of research.