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We report the preparation of both control chitosan and magnetic chitosan beads as biosorbents using chitosan as matrix and magnetite (Fe3O4) nanoparticles as reinforcement followed by detailed advanced characterization. The batch trials were performed to study the adsorption kinetics of biosorbents by removing As(III) and As(V) species from water systems. The experimental data was inserted into Langmuir and Freundlich's isotherms to undertake the mechanism and adsorption capacity of the test biosorbents. Under Langmuir's isotherm, maximum monolayer adsorption capacity (qmax) of the biosorbent was observed to be 73.69 and 79.49 mg/g for As(III) and As(V) species, respectively, under specified conditions. The optimum doses of 1.5 and 2 g/L of MCBB at pH 6.7 showed 99.5 and 99% removal of As(V) and As(III), respectively. The analysis demonstrated that the biosorption process obeyed pseudo 2nd order kinetics with linear regression coefficient (R2) of >0.999. The regeneration and reusability of biosorbents were also assessed.

Objectives/ Methods: The conventional farming approaches have been found unable to deliver an appropriate quantity of fertilizer. Similarly, no explicit measure can be established to regulate the climate parameters. In this study, we have developed a prototype comprising a sensor network (SN) based node, Raspberry Pi based embedded system (ES) that is active to monitor the climatic parameters with air temperature, air humidity, soil moisture,air carbon dioxide, and light intensity within a greenhouse environment.Raspberry Pi based ES is integrated with internet-of-things (IoT) analytics, termed as ThingSpeak, and sensor nodes are physically placed in a greenhouse environment to record climate parameter data being forwarded to the gateway. The gateway nodes direct this data to the agriculture professional(s) through a web browser over the internet. Findings: Based on the received data, the ES triggers the intelligent decision-making by implementing appropriate arrangements to regulate climate parameters. For example, the greenhouse climate is controlled through switching ``ON\'\' certain devices i.e., lights, water sprinklers/exhaust fan, and fan. We deliberate that the proposed scheme can be implementable at a bigger scale without any difficulty and it will be much advantageous for an increase in sustainability, productivity, and profitability of a farming system. Novelty: Up to the authors\' best knowledge, it is the first wireless deployment of ThingSpeak analytics for a smart greenhouse environment by using the Raspberry Pi. The main advantages of this intelligent crop monitoring system include uninterrupted mobile monitoring of the greenhouse, improved crop yield and productivity, negligible human interface/dependency. Keywords: Agricultural monitoring; greenhouses; embedded system; sensor networks (SN); IoT

There is a rapid increase in online social networks (OSN) due to the development of information and communication technologies (ICT). People around the globe are adopting the new OSN to keep in touch with friends and family members, to remain updated with the latest trends and news, etc. In almost every OSN, few influential users are active and attract other members and make clusters. Many users connect with these influential users to remain in touch with the latest ongoing trends as the influential nodes come up with the latest news. In this work, the structural analysis of the Facebook network is undergone. The influential nodes have been identified based on betweenness centrality (BC) and degree centrality (DC) measures. Further, community detection has been made in the whole network especially around the influential nodes. Upon filtration of four top-ranked nodes selected based on the high value of BC and DC, only 22.75% nodes and 8.87% remain in the network. Hence, there is a big question about the sustainability of OSN.

The dream towards fully autonomous vehicles brings a lot of challenges in terms of reliability, high performance computing and sensing capabilities in a vehicle. Nowadays, autonomous vehicles are equipped with several sensors, which allow the vehicle to sense everything on the road and to collect the information needed to drive safely. Altogether, these sensors generate a lot of data, roughly 4TB in a single day [1]. In order to process this abundance of sensory data faster and reliably, there is a need for high performance microcontroller units (MCUs) and fault tolerant data processing respectively. For this reason, we have developed a smart reconfigurable sensor (SRS) platform, which aims to solve the challenges of high performance computing and safety. An in-house fabricated entire silicon-based millimeter-wave transceiver, using IHP's 130 nm SiGe BiCMOS technology, is used as SRS front-end and software (SW) based triple modular redundancy (TMR) system is implemented for fault tolerant radar data processing. Our highly adaptive system supports fault tolerant modes (i.e. fail operational, fail safe), low power mode and distributed execution of tasks among different cores, all while meeting the strict automotive standards.

Unused spectrum is being a limited commodity for the telecom industry. However, up to 500 MHz of unlicensed spectrum in 5 GHz band is in use for several applications and services, particularly in WiFi around the world. Unlicensed spectrum has foreseen to play a vital role in 5G. It is already under discussion at different forums that how the unlicensed band advantage can promote 4G Long Term Evolution (LTE) networks. Long Term Evolution Unlicensed (LTE-U) is an extension of existing LTE mobile networks, and allow LTE technology to use unlicensed bands as an adjunct to the licensed band. It will not only deliver a better user experience to end users but also boost capacity for network operators. In this paper, a Simulink based LTE PDSCH (Physical Downlink Shared Channel) model is introduced which uses the 5 GHz band according to the specifications prescribed by LTE-U Forum and 3GPP (Third Generation Partnership Project). It demonstrates multi-codeword transmission within a small cell from an eNodeB to a User Equipment (UE). LTE enabling technologies like OFDMA, closed-loop spatial multiplexing, turbo channel coding and link adaptations are used to design this model. Minimum mean square error (MMSE) technique as a linear detection method is used for spatially multiplexed Multiple Input Multiple Output (MIMO). Bit error rate is analyzed for QPSK, 16-QAM, and 64-QAM and compared for hard and soft decisions MMSE based MIMO detection. Fairly improved throughputs are obtained for multi-antenna configurations considering the low mobility scenarios and low correlation settings between links.

In this paper, we design noise shaping filters for quantization noise reduction in ΔΣ modulators. The design problem for the noise shaping infinite impulse response (IIR) filter in the feedback of the ΔΣ modulator is in non-convex form, which is hard to solve in general. We propose an iterative linear matrix inequality (LMI) algorithm to find a near optimal noise shaping IIR filter by solving the non-convex design problem. The least-squares and the hybrid design are two methods, either of which can serve as a starting point for our iterative LMI algorithm to obtain a near optimal solution. We also compare and analyze the performances of some other noise shaping techniques with our proposed algorithm. A design example for a bandpass ΔΣ modulator based radio frequency transmitter is provided which takes into account the non-ideal behavior of the output analog filter.

Quantization is a fundamental process in digital signal processing. $\Delta \Sigma$ modulators are often utilized for quantization, which can be easily implemented with static uniform quantizers and error feedback filters. In this paper, we analyze the mean squared quantization error of the quantizer with error feedback including the $\Delta \Sigma$ modulators. First, we study the quantizer with an ideal optimal error feedback filter that minimizes the mean squared error (MSE) of quantization. We show that the amplitude response of the optimal error feedback filter can be parameterized by one parameter. This parameterization enables us to find the optimal error feedback filter numerically. Second, the relationship between the number of bits used for the quantizer and the achievable MSE is clarified by using the optimal error feedback filter. This makes it possible to investigate the efficiency of the quantizer with the optimal error feedback filter in terms of MSE. Then, ideal optimal error feedback filters are approximated by practically implementable filters using the Yule-Walker method and the linear matrix inequality-based method. Numerical examples are provided for demonstrating our analysis and synthesis.

Noise shaping filters for quantizers with error feedback are designed to mitigate the effects of quantization errors. In this paper, we prove that if the transfer function from the quantization error to the signal-of-interest has minimum phase and there is no constraint on the feedback signal, then the scaled inverse of the transfer function is the optimal noise shaping filter. Next, we design a noise shaping filter to minimize the variance or the $l_{2}$ norm of the error in the signal-of-interest under a constraint on the variance or the $l_{2}$ norm of the feedback signal, which can be expressed as bilinear matrix inequalities (BMIs). Although the BMIs are not convex, we prove that the minimization of the error variance under the constraint on the variance of the feedback signal can be cast into a convex optimization problem. This enables us to design optimal noise shaping filters using numerical methods. We formulate our design problem as a convex optimization problem using extended linear matrix inequalities to obtain noise shaping filters, except for the special case. Examples are provided to demonstrate the effectiveness of the designed noise shaping filters.

The ambitious goals for implementing autonomous systems in nearly all industry sectors create big challenges for the designers of such devices. Especially sensors, key factors for enabling autonomy, must fulfil greatest demands. The challenge is to build highly reliable sensory systems, preferably based on commercial off-the-shelf components, with short design cycles, high robustness against faults and minimal power consumption. In this paper, we present an approach for designing such a sensory system that targets automated driving. Designed as a configurable software-implemented TMR system, we based it on three Tensilica Fusion G3 cores with negligible additional hardware to each core. We are able to show that this system can be controlled to support low power, fail safe, fail operational and distributed execution of different tasks, all while keeping the strict timing and safety constraints that are crucial in the automotive area.

In networked control systems, transmitted data should be quantized into a relatively small number of bits if the rate of the communication channel is not sufficiently high. We propose a feedback quantizer for an implementable simple quantizer with high precision for networked control. The infinite impulse response (IIR) feedback filter is designed to mitigate the effect of the quantization error under a performance constraint of the feedback control system. Then, the minimum rate that achieves the constraint is numerically obtained. Simulations are provided to show the effectiveness of the proposed quantizer in a networked feedback control system.

In this paper, we propose two types of noise shaping infinite impulse response (IIR) filters for ΔΣ modulators based on H2 and H∞ norms of the in-band quantization noise. We utilize the hybrid-design and the least-squares approximation method to obtain noise shaping IIR filters by taking an indirect approach for solving the non-convex optimization problem. In both methods, we also incorporate the non-ideal behavior of the output lowpass filter into the design criteria of our noise shaping IIR filter as a weighting function. To demonstrate the effectiveness of proposed noise shaping IIR filters, we compare it with existing design methods.

Delta-Sigma modulators are often utilized to convert analog signals into digital signals. The quantization error of a Delta-Sigma modulator can be reduced by oversampling. However, oversampling increases the number of bits per time if the same number of bits are assigned to each output of the quantizer. Due to limited bandwidth, the rate-distortion relation is necessary to balance the rate and the distortion. In this paper, we analyze the relationship between the rate and the distortion of an optimal scalar Delta-Sigma modulator that minimizes the variance of the error in the output of the system connected to the Delta-Sigma modulator. Numerical examples are provided to show rate-distortion relations of the optimal Delta-Sigma modulators.

Optimal finite impulse response (FIR) error feedback filters for noise shaping in Δ Σ modulators are designed by using weighting functions based on the system norms. We minimize the weighted norms of the quantization error in the output of a Δ Σ modulator, which corresponds to the minimization of the system norm. Three norms, the H 2 system norm, the H ∞ system norm, and the l 1 norm of the impulse response of the system, are adopted. The optimization problem for three types of FIR filters are evaluated by using linear matrix inequalities (LMIs) and then solved numerically via semi-definite programming. Design examples are provided to demonstrate the effectiveness of our proposed methods.